diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..383fe4dab926a398560bd5e1083f9b73d2d15ded Binary files /dev/null and b/.DS_Store differ diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..c3dad975c4f9dfbbf8c63d6058e378389d4e7cc4 --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ +# Ignore generated audio + specs +outputs/ +*.wav +Voicetech API Specification.pdf diff --git a/Procfile b/Procfile new file mode 100644 index 0000000000000000000000000000000000000000..464322058406a4605d0adaa32b99b155ad4ec065 --- /dev/null +++ b/Procfile @@ -0,0 +1 @@ +web: python download_models.py && uvicorn src.api:app --host 0.0.0.0 --port ${PORT:-8000} diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..0ba4dc42a9a89fc33fb1d84eaeac0213100146a4 --- /dev/null +++ b/README.md @@ -0,0 +1,246 @@ +# Voice Tech for All - Multi-lingual TTS System + +A lightweight, multi-lingual Text-to-Speech system supporting **11 Indian languages** with **style/prosody control** and REST API. + +## 🎯 Hackathon: Voice Tech for All + +Built for the healthcare assistant use case - helping pregnant mothers in low-income communities access healthcare information in their native languages. + +## ✨ Features + +- **11 Indian Languages**: Hindi, Bengali, Marathi, Telugu, Kannada, Bhojpuri, Chhattisgarhi, Maithili, Magahi, English, **Gujarati** +- **21 Voice Options**: Male & Female voices for each language +- **Style/Prosody Control**: 9 presets (happy, sad, calm, excited, etc.) +- **Pitch & Speed Control**: Fine-tune voice characteristics +- **Lightweight**: VITS-based models optimized for fast inference +- **REST API**: FastAPI-powered server with OpenAPI docs +- **Text Normalization**: Handles numbers, punctuation for Indian scripts + +## 🚀 Quick Start + +### 1. Installation + +```bash +# Clone and navigate +git clone https://github.com/harshil748/VoiceAPI +cd VoiceAPI + +# Create virtual environment +python3 -m venv tts +source tts/bin/activate + +# Install dependencies +pip install -r requirements.txt +``` + +### 2. Download Models + +```bash +# Download Hindi models (male + female) +python -m src.cli download --lang hi + +# Or download a specific voice +python -m src.cli download --voice hi_male + +# Gujarati uses Facebook MMS (auto-downloads on first use) +``` + +### 3. Synthesize Speech + +```bash +# Basic synthesis +python -m src.cli synthesize --text "नमस्ते दोस्तों" --voice hi_male --output hello.wav + +# Play the audio (macOS) +afplay hello.wav +``` + +### 4. Start API Server + +```bash +python -m src.cli serve --port 8000 +``` + +Visit `http://localhost:8000/docs` for interactive API documentation. + +## 🎨 Style Presets + +| Preset | Speed | Pitch | Energy | Best For | +| --------- | ----- | ----- | ------ | ----------------------- | +| `default` | 1.0 | 1.0 | 1.0 | Normal speech | +| `slow` | 0.75 | 1.0 | 1.0 | Elderly users, clarity | +| `fast` | 1.25 | 1.0 | 1.0 | Quick information | +| `soft` | 0.9 | 0.95 | 0.7 | Calming content | +| `loud` | 1.0 | 1.05 | 1.3 | Alerts, emphasis | +| `happy` | 1.1 | 1.1 | 1.2 | Positive messages | +| `sad` | 0.85 | 0.9 | 0.8 | Empathetic responses | +| `calm` | 0.9 | 0.95 | 0.85 | **Healthcare guidance** | +| `excited` | 1.2 | 1.15 | 1.3 | Celebrations | + +## 📡 API Usage + +### 🏆 Hackathon API - GET /Get_Inference + +**This is the official hackathon endpoint** that follows the Voice Tech for All specification: + +```python +import requests + +base_url = 'http://localhost:8000/Get_Inference' +WavPath = 'path/to/reference.wav' + +params = { + 'text': 'ಮಾದರಿಯು ಸರಿಯಾಗಿ ಕಾರ್ಯನಿರ್ವಹಿಸುತ್ತಿದೆಯೇ ಎಂದು ಖಚಿತಪಡಿಸಿಕೊಳ್ಳಲು ಬಳಸಲಾಗುವ ಪರೀಕ್ಷಾ ವಾಕ್ಯ ಇದು.', + 'lang': 'kannada', +} + +with open(WavPath, "rb") as AudioFile: + response = requests.get(base_url, params=params, files={'speaker_wav': AudioFile}) + +if response.status_code == 200: + with open('output.wav', 'wb') as f: + f.write(response.content) + print("Audio saved as 'output.wav'") +``` + +**Query Parameters:** + +| Parameter | Type | Required | Description | +| ------------- | ------ | --------- | ---------------------------------------------------------------------------------------------------------------- | +| `text` | string | Mandatory | Input text to convert to speech. For English, text must be lowercase. | +| `lang` | string | Mandatory | Language: bhojpuri, bengali, english, gujarati, hindi, chhattisgarhi, kannada, magahi, maithili, marathi, telugu | +| `speaker_wav` | file | Mandatory | Reference WAV file for speaker voice | + +**Response:** `200 OK` with `Content-Type: audio/wav` + +--- + +### Synthesize with Style (POST) + +```bash +curl -X POST "http://localhost:8000/synthesize" \ + -H "Content-Type: application/json" \ + -d '{ + "text": "आपका दिन शुभ हो", + "voice": "hi_female", + "style": "happy", + "speed": 1.0, + "pitch": 1.0 + }' \ + --output speech.wav +``` + +### Gujarati Synthesis + +```bash +curl -X POST "http://localhost:8000/synthesize" \ + -H "Content-Type: application/json" \ + -d '{"text": "નમસ્તે, કેમ છો?", "voice": "gu_mms", "style": "calm"}' \ + --output gujarati.wav +``` + +### List Style Presets + +```bash +curl http://localhost:8000/styles +``` + +## 🎤 Available Voices + +| Language | Code | Male | Female | Notes | +| ------------- | ---- | ----------- | ------------- | ------------ | +| Hindi | hi | ✅ hi_male | ✅ hi_female | SYSPIN | +| Bengali | bn | ✅ bn_male | ✅ bn_female | SYSPIN | +| Marathi | mr | ✅ mr_male | ✅ mr_female | SYSPIN | +| Telugu | te | ✅ te_male | ✅ te_female | SYSPIN | +| Kannada | kn | ✅ kn_male | ✅ kn_female | SYSPIN | +| Bhojpuri | bho | ✅ bho_male | ✅ bho_female | SYSPIN | +| Chhattisgarhi | hne | ✅ hne_male | ✅ hne_female | SYSPIN | +| Maithili | mai | ✅ mai_male | ✅ mai_female | SYSPIN | +| Magahi | mag | ✅ mag_male | ✅ mag_female | SYSPIN | +| English | en | ✅ en_male | ✅ en_female | SYSPIN | +| **Gujarati** | gu | ✅ gu_mms | - | Facebook MMS | + +## 🐍 Python API + +```python +from src.engine import TTSEngine + +# Initialize engine +engine = TTSEngine(device="auto") + +# Basic synthesis +output = engine.synthesize( + text="गर्भावस्था में स्वस्थ आहार महत्वपूर्ण है", + voice="hi_female" +) + +# With style control +output = engine.synthesize( + text="आपका दिन शुभ हो", + voice="hi_male", + style="happy", # Use preset + pitch=1.1, # Or manual control + speed=1.0, + energy=1.2 +) + +# Gujarati +output = engine.synthesize( + text="સ્વસ્થ રહો, ખુશ રહો", + voice="gu_mms", + style="calm" +) + +# Save to file +engine.synthesize_to_file( + text="નમસ્તે", + output_path="hello.wav", + voice="gu_mms", + style="calm" +) +``` + +## 📁 Project Structure + +```text +VoiceAPI/ +├── src/ +│ ├── config.py # Language/voice/style configurations +│ ├── tokenizer.py # Text tokenization & normalization +│ ├── engine.py # Main TTS engine with style processor +│ ├── downloader.py # HuggingFace model downloader +│ ├── api.py # FastAPI REST server +│ └── cli.py # Command-line interface +├── models/ # Downloaded models +├── dataset/ # SPICOR dataset (for fine-tuning) +├── technical_report.md +├── requirements.txt +└── README.md +``` + +## 📊 Performance + +| Metric | Value | +| -------------- | ------------------------------- | +| Languages | 11 | +| Voice Variants | 21 | +| Style Presets | 9 | +| Model Size | ~300MB (VITS), ~145MB (MMS) | +| Inference Time | ~0.3s (M2 Mac, CPU) | +| Sample Rate | 22050 Hz (VITS), 16000 Hz (MMS) | + +## 🙏 Credits + +- **SYSPIN Models**: [IISc Bangalore](https://huggingface.co/SYSPIN) +- **MMS Models**: [Facebook Research](https://huggingface.co/facebook/mms-tts-guj) +- **Architecture**: VITS (Coqui AI) +- **Dataset**: SPICOR TTS Project, IISc SPIRE Lab + +## 📜 License + +CC BY 4.0 (SYSPIN), CC BY-NC 4.0 (MMS) + +--- + +Built with ❤️ for **Voice Tech for All Hackathon** diff --git a/download_models.py b/download_models.py new file mode 100644 index 0000000000000000000000000000000000000000..e80e448751f37f7ce10ff40c94de192188461998 --- /dev/null +++ b/download_models.py @@ -0,0 +1,55 @@ +#!/usr/bin/env python3 +""" +Download all required TTS models from HuggingFace +Run this on deployment to fetch models before starting the server +""" + +import os +import sys + +# Add src to path +sys.path.insert(0, os.path.dirname(__file__)) + +from src.downloader import ModelDownloader +from src.config import LANGUAGE_CONFIGS + + +def main(): + print("=" * 60) + print("Downloading TTS Models from HuggingFace...") + print("=" * 60) + + downloader = ModelDownloader() + + # Download all configured models + voices = list(LANGUAGE_CONFIGS.keys()) + print(f"\nModels to download: {len(voices)}") + for v in voices: + print(f" - {v}") + + print("\n") + + success = 0 + failed = [] + + for voice in voices: + try: + print(f"Downloading {voice}...") + downloader.download_model(voice) + success += 1 + print(f" ✓ {voice} downloaded\n") + except Exception as e: + print(f" ✗ {voice} failed: {e}\n") + failed.append(voice) + + print("=" * 60) + print(f"Download complete: {success}/{len(voices)} models") + if failed: + print(f"Failed: {', '.join(failed)}") + return 1 + print("=" * 60) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/models/.DS_Store b/models/.DS_Store new file mode 100644 index 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997766981 diff --git a/models/bho_male/config.json b/models/bho_male/config.json new file mode 100644 index 0000000000000000000000000000000000000000..318970a1594beec5431e955b7b2f8c3ef16e4411 --- /dev/null +++ b/models/bho_male/config.json @@ -0,0 +1,257 @@ +{ + "output_path": ".", + "logger_uri": null, + "run_name": "vits_Bhojpuri_Male_30hrs", + "project_name": null, + "run_description": "\ud83d\udc38Coqui trainer run.", + "print_step": 25, + "plot_step": 100, + "model_param_stats": false, + "wandb_entity": null, + "dashboard_logger": "tensorboard", + "log_model_step": null, + "save_step": 20000, + "save_n_checkpoints": 1000, + "save_checkpoints": true, + "save_all_best": false, + "save_best_after": 10000, + "target_loss": null, + "print_eval": true, + "test_delay_epochs": -1, + "run_eval": true, + "run_eval_steps": null, + "distributed_backend": "nccl", + "distributed_url": "tcp://localhost:54321", + "mixed_precision": true, + "epochs": 500, + "batch_size": 40, + "eval_batch_size": 16, + "grad_clip": [ + 1000, + 1000 + ], + "scheduler_after_epoch": true, + "lr": 0.001, + "optimizer": "AdamW", + "optimizer_params": { + "betas": [ + 0.8, + 0.99 + ], + "eps": 1e-09, + "weight_decay": 0.01 + }, + "lr_scheduler": null, + "lr_scheduler_params": {}, + "use_grad_scaler": false, + "cudnn_enable": true, + "cudnn_deterministic": false, + "cudnn_benchmark": false, + "training_seed": 54321, + "model": "vits", + "num_loader_workers": 8, + "num_eval_loader_workers": 4, + "use_noise_augment": false, + "audio": { + "fft_size": 1024, + "sample_rate": 22050, + "win_length": 1024, + "hop_length": 256, + "num_mels": 80, + "mel_fmin": 0, + "mel_fmax": null + }, + "use_phonemes": false, + "phonemizer": null, + "phoneme_language": "en-us", + "compute_input_seq_cache": true, + "text_cleaner": "multilingual_cleaners", + "enable_eos_bos_chars": false, + "test_sentences_file": "", + "phoneme_cache_path": "./phoneme_cache", + "characters": { + "characters_class": "TTS.tts.models.vits.VitsCharacters", + "vocab_dict": null, + "pad": "", + "eos": "", + "bos": "", + "blank": "", + "characters": "\u091a.\u0947\u0910\u0925\u092e\u0959\u091d\u0906\u0949?\u092d \u092a\u0939\u0928\u093d\u091f\u0938\u0935\u0940\u091b\u0923\u0921\u091e\u0926\u094b\u0915\u0924\u0948\u0943\u095b\u0941\u095e\u092c\u0908\u0946\u094c\u0927\u090b\u093e\u0922\u0907\u093c\u0902\u0905\u0937\u0920\u095c\u0913\u092f,\u093f\u0930\u0901\u0914\u092b\u0909\u0916\u0911\u094d\u0932\u091c\u090f\u090a\u0917\u0936\u095d\u0919\u0918\u0942", + "punctuations": "!\u00a1'(),-.:;\u00bf? ", + "phonemes": null, + "is_unique": true, + "is_sorted": true + }, + "add_blank": true, + "batch_group_size": 5, + "loss_masking": null, + "min_audio_len": 1, + "max_audio_len": Infinity, + "min_text_len": 1, + "max_text_len": Infinity, + "compute_f0": false, + "compute_energy": false, + "compute_linear_spec": true, + "precompute_num_workers": 0, + "start_by_longest": false, + "shuffle": false, + "drop_last": false, + "datasets": [ + { + "formatter": "syspin", + "dataset_name": "", + "path": ".", + "meta_file_train": "../manifests/Bhojpuri_Male/30hrs.tsv", + "ignored_speakers": null, + "language": "", + "phonemizer": "", + "meta_file_val": "", + "meta_file_attn_mask": "" + } + ], + "test_sentences": [ + [ + "\u090f\u0928\u094d\u091f\u094d\u0930\u093e\u092a\u0940 \u0915\u0902\u092a\u094d\u092f\u0942\u091f\u093f\u0902\u0917 \u092e\u0947\u0902 \u090f\u0928\u094d\u091f\u094d\u0930\u094b\u092a\u0940 \u090a \u0911\u092a\u0930\u0947\u091f\u093f\u0902\u0917 \u0938\u093f\u0938\u094d\u091f\u092e \u0939 \u091c\u0947 \u092a\u0947 \u0938\u0930\u093e \u0915\u094d\u0930\u093f\u092a\u094d\u091f\u094b\u0917\u094d\u0930\u093e\u092b\u093f\u0915 \u092b\u0902\u0915\u094d\u0936\u0928 \u0938\u092c \u0915\u093e\u092e \u0915\u0930\u0947 \u0932\u0947\u0902", + "Bhojpuri_Male", + null, + "bh" + ] + ], + "eval_split_max_size": null, + "eval_split_size": 0.01, + "use_speaker_weighted_sampler": false, + "speaker_weighted_sampler_alpha": 1.0, + "use_language_weighted_sampler": false, + "language_weighted_sampler_alpha": 1.0, + "use_length_weighted_sampler": false, + "length_weighted_sampler_alpha": 1.0, + "model_args": { + "num_chars": 86, + "out_channels": 513, + "spec_segment_size": 32, + "hidden_channels": 192, + "hidden_channels_ffn_text_encoder": 768, + "num_heads_text_encoder": 2, + "num_layers_text_encoder": 6, + "kernel_size_text_encoder": 3, + "dropout_p_text_encoder": 0.1, + "dropout_p_duration_predictor": 0.5, + "kernel_size_posterior_encoder": 5, + 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"use_d_vector_file": false, + "d_vector_dim": 0, + "detach_dp_input": true, + "use_language_embedding": false, + "embedded_language_dim": 4, + "num_languages": 0, + "language_ids_file": null, + "use_speaker_encoder_as_loss": false, + "speaker_encoder_config_path": "", + "speaker_encoder_model_path": "", + "condition_dp_on_speaker": true, + "freeze_encoder": false, + "freeze_DP": false, + "freeze_PE": false, + "freeze_flow_decoder": false, + "freeze_waveform_decoder": false, + "encoder_sample_rate": null, + "interpolate_z": true, + "reinit_DP": false, + "reinit_text_encoder": false + }, + "lr_gen": 0.0002, + "lr_disc": 0.0002, + "lr_scheduler_gen": "ExponentialLR", + "lr_scheduler_gen_params": { + "gamma": 0.999875, + "last_epoch": -1 + }, + "lr_scheduler_disc": "ExponentialLR", + "lr_scheduler_disc_params": { + "gamma": 0.999875, + "last_epoch": -1 + }, + "kl_loss_alpha": 1.0, + "disc_loss_alpha": 1.0, + "gen_loss_alpha": 1.0, + "feat_loss_alpha": 1.0, + "mel_loss_alpha": 45.0, + "dur_loss_alpha": 1.0, + "speaker_encoder_loss_alpha": 1.0, + "return_wav": true, + "use_weighted_sampler": false, + "weighted_sampler_attrs": {}, + "weighted_sampler_multipliers": {}, + "r": 1, + "num_speakers": 0, + "use_speaker_embedding": false, + "speakers_file": null, + "speaker_embedding_channels": 256, + "language_ids_file": null, + "use_language_embedding": false, + "use_d_vector_file": false, + "d_vector_file": null, + "d_vector_dim": 0, + "github_branch": "* dev" +} \ No newline at end of file diff --git a/models/bn_female/bn_female_vits_30hrs.pt b/models/bn_female/bn_female_vits_30hrs.pt new file mode 100644 index 0000000000000000000000000000000000000000..1e88dbfb9975ccad5fcc43e825b39a718ac303f3 --- /dev/null +++ b/models/bn_female/bn_female_vits_30hrs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:53208e056050bb485df9192a0d444d3fa72eefe15b2c04840e9a500e4ac1bbf4 +size 333255366 diff --git a/models/bn_female/chars.txt b/models/bn_female/chars.txt new file mode 100644 index 0000000000000000000000000000000000000000..8e997bcd0f621c7a99b764a001b65bddac06c94b --- /dev/null +++ b/models/bn_female/chars.txt @@ -0,0 +1 @@ +ূঞংঘঔদলৌআডখরথটোৗঙঐানষঝবছঅঢ়ঁপউধঢশগয়।?িক,যঈস্ত়ফঋৈজ'ীঠৰণওৎঃমচঊড়ইুভে এ"ৃহ diff --git a/models/bn_female/jit_infer.py b/models/bn_female/jit_infer.py new file mode 100644 index 0000000000000000000000000000000000000000..74f3059f55458a7f3f6d83af99a04db85da1bd7e --- /dev/null +++ b/models/bn_female/jit_infer.py @@ -0,0 +1,32 @@ +import os +from extra import TTSTokenizer, VitsConfig, CharactersConfig, VitsCharacters +import torch +import numpy as np + +#ch female +with open("chars.txt", 'r') as f: + letters = f.read().strip('\n') +model="bn_female_vits_30hrs.pt" +text = " হলেও আমাদের সবার সার্বিক শৃঙ্খলা বোধের উন্নতি হবে" + +config = VitsConfig( + text_cleaner="multilingual_cleaners", + characters=CharactersConfig( + characters_class=VitsCharacters, + pad="", + eos="", + bos="", + blank="", + characters=letters, + punctuations="!¡'(),-.:;¿? ", + phonemes=None) + ) +tokenizer, config = TTSTokenizer.init_from_config(config) + +x = tokenizer.text_to_ids(text) +x = torch.from_numpy(np.array(x)).unsqueeze(0) +net = torch.jit.load(model) +with torch.no_grad(): + out2 = net(x) +import soundfile as sf +sf.write("jit.wav", out2.squeeze().cpu().numpy(), 22050) \ No newline at end of file diff --git a/models/bn_male/bn_male_vits_30hrs.pt b/models/bn_male/bn_male_vits_30hrs.pt new file mode 100644 index 0000000000000000000000000000000000000000..e8b838040e97148124533118ef60e8122a5caf8a --- /dev/null +++ b/models/bn_male/bn_male_vits_30hrs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c9d8d52f0bc33ef01d733eef36fb00f1e17192b8c86123a0ccf84a24dbb80d0e +size 333249868 diff --git a/models/bn_male/chars.txt b/models/bn_male/chars.txt new file mode 100644 index 0000000000000000000000000000000000000000..b3f82b65168b3f2f15030f9f41ba1f27d449394f --- /dev/null +++ b/models/bn_male/chars.txt @@ -0,0 +1 @@ +ূঞংঘঔদলৌআডখরঃটোৗঙঐনাঝষবঅছঢ়ঁপউধঢশগয়।?িক,যঈসত্ৈফ়ঊজ'ীঠৎণওঋৰমচড়ভুইে থএ"ৃহ diff --git a/models/bn_male/extra.py b/models/bn_male/extra.py new file mode 100644 index 0000000000000000000000000000000000000000..c7db561351da270a7c3931bfe0afefa7bc6d4853 --- /dev/null +++ b/models/bn_male/extra.py @@ -0,0 +1,787 @@ +from typing import Callable, Dict, List, Union +from dataclasses import asdict, dataclass, field + + +import re +from dataclasses import replace +from typing import Dict +_whitespace_re = re.compile(r"\s+") + +from dataclasses import dataclass, field +from typing import List + +# from TTS.tts.configs.shared_configs import BaseTTSConfig +# from TTS.tts.models.vits import VitsArgs, VitsAudioConfig + +@dataclass +class CharactersConfig(): + + characters_class: str = None + + # using BaseVocabulary + vocab_dict: Dict = None + + # using on BaseCharacters + pad: str = None + eos: str = None + bos: str = None + blank: str = None + characters: str = None + punctuations: str = None + phonemes: str = None + is_unique: bool = True # for backwards compatibility of models trained with char sets with duplicates + is_sorted: bool = True + + +@dataclass +class BaseTTSConfig(): + + # audio: BaseAudioConfig = field(default_factory=BaseAudioConfig) + # phoneme settings + use_phonemes: bool = False + phonemizer: str = None + phoneme_language: str = None + compute_input_seq_cache: bool = False + text_cleaner: str = None + enable_eos_bos_chars: bool = False + test_sentences_file: str = "" + phoneme_cache_path: str = None + # vocabulary parameters + characters: CharactersConfig = None + add_blank: bool = False + # training params + batch_group_size: int = 0 + loss_masking: bool = None + # dataloading + min_audio_len: int = 1 + max_audio_len: int = float("inf") + min_text_len: int = 1 + max_text_len: int = float("inf") + compute_f0: bool = False + compute_energy: bool = False + compute_linear_spec: bool = False + precompute_num_workers: int = 0 + use_noise_augment: bool = False + start_by_longest: bool = False + shuffle: bool = False + drop_last: bool = False + # dataset + datasets: str = None + # optimizer + optimizer: str = "radam" + optimizer_params: dict = None + # scheduler + lr_scheduler: str = None + lr_scheduler_params: dict = field(default_factory=lambda: {}) + # testing + test_sentences: List[str] = field(default_factory=lambda: []) + # evaluation + eval_split_max_size: int = None + eval_split_size: float = 0.01 + # weighted samplers + use_speaker_weighted_sampler: bool = False + speaker_weighted_sampler_alpha: float = 1.0 + use_language_weighted_sampler: bool = False + language_weighted_sampler_alpha: float = 1.0 + use_length_weighted_sampler: bool = False + length_weighted_sampler_alpha: float = 1.0 + + +@dataclass +class VitsAudioConfig(): + fft_size: int = 1024 + sample_rate: int = 22050 + win_length: int = 1024 + hop_length: int = 256 + num_mels: int = 80 + mel_fmin: int = 0 + mel_fmax: int = None + +@dataclass +class VitsArgs(): + num_chars: int = 100 + out_channels: int = 513 + spec_segment_size: int = 32 + hidden_channels: int = 192 + hidden_channels_ffn_text_encoder: int = 768 + num_heads_text_encoder: int = 2 + num_layers_text_encoder: int = 6 + kernel_size_text_encoder: int = 3 + dropout_p_text_encoder: float = 0.1 + dropout_p_duration_predictor: float = 0.5 + kernel_size_posterior_encoder: int = 5 + dilation_rate_posterior_encoder: int = 1 + num_layers_posterior_encoder: int = 16 + kernel_size_flow: int = 5 + dilation_rate_flow: int = 1 + num_layers_flow: int = 4 + resblock_type_decoder: str = "1" + resblock_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [3, 7, 11]) + resblock_dilation_sizes_decoder: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]) + upsample_rates_decoder: List[int] = field(default_factory=lambda: [8, 8, 2, 2]) + upsample_initial_channel_decoder: int = 512 + upsample_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [16, 16, 4, 4]) + periods_multi_period_discriminator: List[int] = field(default_factory=lambda: [2, 3, 5, 7, 11]) + use_sdp: bool = True + noise_scale: float = 1.0 + inference_noise_scale: float = 0.667 + length_scale: float = 1 + noise_scale_dp: float = 1.0 + inference_noise_scale_dp: float = 1.0 + max_inference_len: int = None + init_discriminator: bool = True + use_spectral_norm_disriminator: bool = False + use_speaker_embedding: bool = False + num_speakers: int = 0 + speakers_file: str = None + d_vector_file: List[str] = None + speaker_embedding_channels: int = 256 + use_d_vector_file: bool = False + d_vector_dim: int = 0 + detach_dp_input: bool = True + use_language_embedding: bool = False + embedded_language_dim: int = 4 + num_languages: int = 0 + language_ids_file: str = None + use_speaker_encoder_as_loss: bool = False + speaker_encoder_config_path: str = "" + speaker_encoder_model_path: str = "" + condition_dp_on_speaker: bool = True + freeze_encoder: bool = False + freeze_DP: bool = False + freeze_PE: bool = False + freeze_flow_decoder: bool = False + freeze_waveform_decoder: bool = False + encoder_sample_rate: int = None + interpolate_z: bool = True + reinit_DP: bool = False + reinit_text_encoder: bool = False +@dataclass +class VitsConfig(BaseTTSConfig): + + model: str = "vits" + # model specific params + model_args: VitsArgs = field(default_factory=VitsArgs) + audio: VitsAudioConfig = field(default_factory=VitsAudioConfig) + + # optimizer + grad_clip: List[float] = field(default_factory=lambda: [1000, 1000]) + lr_gen: float = 0.0002 + lr_disc: float = 0.0002 + lr_scheduler_gen: str = "ExponentialLR" + lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + lr_scheduler_disc: str = "ExponentialLR" + lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + scheduler_after_epoch: bool = True + optimizer: str = "AdamW" + optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01}) + + # loss params + kl_loss_alpha: float = 1.0 + disc_loss_alpha: float = 1.0 + gen_loss_alpha: float = 1.0 + feat_loss_alpha: float = 1.0 + mel_loss_alpha: float = 45.0 + dur_loss_alpha: float = 1.0 + speaker_encoder_loss_alpha: float = 1.0 + + # data loader params + return_wav: bool = True + compute_linear_spec: bool = True + + # sampler params + use_weighted_sampler: bool = False # TODO: move it to the base config + weighted_sampler_attrs: dict = field(default_factory=lambda: {}) + weighted_sampler_multipliers: dict = field(default_factory=lambda: {}) + + # overrides + r: int = 1 # DO NOT CHANGE + add_blank: bool = True + + # testing + test_sentences: List[List] = field( + default_factory=lambda: [ + ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."], + ["Be a voice, not an echo."], + ["I'm sorry Dave. I'm afraid I can't do that."], + ["This cake is great. It's so delicious and moist."], + ["Prior to November 22, 1963."], + ] + ) + + # multi-speaker settings + # use speaker embedding layer + num_speakers: int = 0 + use_speaker_embedding: bool = False + speakers_file: str = None + speaker_embedding_channels: int = 256 + language_ids_file: str = None + use_language_embedding: bool = False + + # use d-vectors + use_d_vector_file: bool = False + d_vector_file: List[str] = None + d_vector_dim: int = None + + def __post_init__(self): + pass + # for key, val in self.model_args.items(): + # if hasattr(self, key): + # self[key] = val + + + + + +def parse_symbols(): + return { + "pad": _pad, + "eos": _eos, + "bos": _bos, + "characters": _characters, + "punctuations": _punctuations, + "phonemes": _phonemes, + } + + +# DEFAULT SET OF GRAPHEMES +_pad = "" +_eos = "" +_bos = "" +_blank = "" # TODO: check if we need this alongside with PAD +_characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +_punctuations = "!'(),-.:;? " + + +# DEFAULT SET OF IPA PHONEMES +# Phonemes definition (All IPA characters) +_vowels = "iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻ" +_non_pulmonic_consonants = "ʘɓǀɗǃʄǂɠǁʛ" +_pulmonic_consonants = "pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟ" +_suprasegmentals = "ˈˌːˑ" +_other_symbols = "ʍwɥʜʢʡɕʑɺɧʲ" +_diacrilics = "ɚ˞ɫ" +_phonemes = _vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics + + +class BaseVocabulary: + """Base Vocabulary class. + + This class only needs a vocabulary dictionary without specifying the characters. + + Args: + vocab (Dict): A dictionary of characters and their corresponding indices. + """ + + def __init__(self, vocab: Dict, pad: str = None, blank: str = None, bos: str = None, eos: str = None): + self.vocab = vocab + self.pad = pad + self.blank = blank + self.bos = bos + self.eos = eos + + @property + def pad_id(self) -> int: + """Return the index of the padding character. If the padding character is not specified, return the length + of the vocabulary.""" + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + """Return the index of the blank character. If the blank character is not specified, return the length of + the vocabulary.""" + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def bos_id(self) -> int: + """Return the index of the bos character. If the bos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def eos_id(self) -> int: + """Return the index of the eos character. If the eos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def vocab(self): + """Return the vocabulary dictionary.""" + return self._vocab + + @vocab.setter + def vocab(self, vocab): + """Set the vocabulary dictionary and character mapping dictionaries.""" + self._vocab, self._char_to_id, self._id_to_char = None, None, None + if vocab is not None: + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self._vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self._vocab) # pylint: disable=unnecessary-comprehension + } + + @staticmethod + def init_from_config(config, **kwargs): + """Initialize from the given config.""" + if config.characters is not None and "vocab_dict" in config.characters and config.characters.vocab_dict: + return ( + BaseVocabulary( + config.characters.vocab_dict, + config.characters.pad, + config.characters.blank, + config.characters.bos, + config.characters.eos, + ), + config, + ) + return BaseVocabulary(**kwargs), config + + def to_config(self): + return CharactersConfig( + vocab_dict=self._vocab, + pad=self.pad, + eos=self.eos, + bos=self.bos, + blank=self.blank, + is_unique=False, + is_sorted=False, + ) + + @property + def num_chars(self): + """Return number of tokens in the vocabulary.""" + return len(self._vocab) + + def char_to_id(self, char: str) -> int: + """Map a character to an token ID.""" + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + """Map an token ID to a character.""" + return self._id_to_char[idx] + + +class BaseCharacters: + + + def __init__( + self, + characters: str = None, + punctuations: str = None, + pad: str = None, + eos: str = None, + bos: str = None, + blank: str = None, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + self._characters = characters + self._punctuations = punctuations + self._pad = pad + self._eos = eos + self._bos = bos + self._blank = blank + self.is_unique = is_unique + self.is_sorted = is_sorted + self._create_vocab() + + @property + def pad_id(self) -> int: + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def eos_id(self) -> int: + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def bos_id(self) -> int: + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, characters): + self._characters = characters + self._create_vocab() + + @property + def punctuations(self): + return self._punctuations + + @punctuations.setter + def punctuations(self, punctuations): + self._punctuations = punctuations + self._create_vocab() + + @property + def pad(self): + return self._pad + + @pad.setter + def pad(self, pad): + self._pad = pad + self._create_vocab() + + @property + def eos(self): + return self._eos + + @eos.setter + def eos(self, eos): + self._eos = eos + self._create_vocab() + + @property + def bos(self): + return self._bos + + @bos.setter + def bos(self, bos): + self._bos = bos + self._create_vocab() + + @property + def blank(self): + return self._blank + + @blank.setter + def blank(self, blank): + self._blank = blank + self._create_vocab() + + @property + def vocab(self): + return self._vocab + + @vocab.setter + def vocab(self, vocab): + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self.vocab) # pylint: disable=unnecessary-comprehension + } + + @property + def num_chars(self): + return len(self._vocab) + + def _create_vocab(self): + _vocab = self._characters + if self.is_unique: + _vocab = list(set(_vocab)) + if self.is_sorted: + _vocab = sorted(_vocab) + _vocab = list(_vocab) + _vocab = [self._blank] + _vocab if self._blank is not None and len(self._blank) > 0 else _vocab + _vocab = [self._bos] + _vocab if self._bos is not None and len(self._bos) > 0 else _vocab + _vocab = [self._eos] + _vocab if self._eos is not None and len(self._eos) > 0 else _vocab + _vocab = [self._pad] + _vocab if self._pad is not None and len(self._pad) > 0 else _vocab + self.vocab = _vocab + list(self._punctuations) + if self.is_unique: + duplicates = {x for x in self.vocab if self.vocab.count(x) > 1} + assert ( + len(self.vocab) == len(self._char_to_id) == len(self._id_to_char) + ), f" [!] There are duplicate characters in the character set. {duplicates}" + + def char_to_id(self, char: str) -> int: + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + return self._id_to_char[idx] + + def print_log(self, level: int = 0): + """ + Prints the vocabulary in a nice format. + """ + indent = "\t" * level + print(f"{indent}| > Characters: {self._characters}") + print(f"{indent}| > Punctuations: {self._punctuations}") + print(f"{indent}| > Pad: {self._pad}") + print(f"{indent}| > EOS: {self._eos}") + print(f"{indent}| > BOS: {self._bos}") + print(f"{indent}| > Blank: {self._blank}") + print(f"{indent}| > Vocab: {self.vocab}") + print(f"{indent}| > Num chars: {self.num_chars}") + + @staticmethod + def init_from_config(config: "Coqpit"): # pylint: disable=unused-argument + """Init your character class from a config. + + Implement this method for your subclass. + """ + # use character set from config + if config.characters is not None: + return BaseCharacters(**config.characters), config + # return default character set + characters = BaseCharacters() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=self._eos, + bos=self._bos, + blank=self._blank, + is_unique=self.is_unique, + is_sorted=self.is_sorted, + ) + + +class IPAPhonemes(BaseCharacters): + + + def __init__( + self, + characters: str = _phonemes, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a IPAPhonemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + # band-aid for compatibility with old models + if "characters" in config and config.characters is not None: + if "phonemes" in config.characters and config.characters.phonemes is not None: + config.characters["characters"] = config.characters["phonemes"] + return ( + IPAPhonemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + # use character set from config + if config.characters is not None: + return IPAPhonemes(**config.characters), config + # return default character set + characters = IPAPhonemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +class Graphemes(BaseCharacters): + + + def __init__( + self, + characters: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a Graphemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + if config.characters is not None: + # band-aid for compatibility with old models + if "phonemes" in config.characters: + return ( + Graphemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + return Graphemes(**config.characters), config + characters = Graphemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +if __name__ == "__main__": + gr = Graphemes() + ph = IPAPhonemes() + gr.print_log() + ph.print_log() + + +class VitsCharacters(BaseCharacters): + """Characters class for VITs model for compatibility with pre-trained models""" + + def __init__( + self, + graphemes: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + ipa_characters: str = _phonemes, + ) -> None: + if ipa_characters is not None: + graphemes += ipa_characters + super().__init__(graphemes, punctuations, pad, None, None, "", is_unique=False, is_sorted=True) + + def _create_vocab(self): + self._vocab = [self._pad] + list(self._punctuations) + list(self._characters) + [self._blank] + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + # pylint: disable=unnecessary-comprehension + self._id_to_char = {idx: char for idx, char in enumerate(self.vocab)} + + @staticmethod + def init_from_config(config): + _pad = config.characters.pad + _punctuations = config.characters.punctuations + _letters = config.characters.characters + _letters_ipa = config.characters.phonemes + return ( + VitsCharacters(graphemes=_letters, ipa_characters=_letters_ipa, punctuations=_punctuations, pad=_pad), + config, + ) + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=None, + bos=None, + blank=self._blank, + is_unique=False, + is_sorted=True, + ) + +class TTSTokenizer: + def __init__( + self, + text_cleaner: Callable = None, + characters: "BaseCharacters" = None, + ): + self.text_cleaner = text_cleaner + self.characters = characters + self.not_found_characters = [] + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, new_characters): + self._characters = new_characters + self.pad_id = self.characters.char_to_id(self.characters.pad) if self.characters.pad else None + self.blank_id = self.characters.char_to_id(self.characters.blank) if self.characters.blank else None + + def encode(self, text: str) -> List[int]: + """Encodes a string of text as a sequence of IDs.""" + token_ids = [] + for char in text: + try: + idx = self.characters.char_to_id(char) + token_ids.append(idx) + except KeyError: + # discard but store not found characters + if char not in self.not_found_characters: + self.not_found_characters.append(char) + print(text) + print(f" [!] Character {repr(char)} not found in the vocabulary. Discarding it.") + return token_ids + + def text_to_ids(self, text: str, language: str = None) -> List[int]: # pylint: disable=unused-argument + text = self.text_cleaner(text) + text = self.encode(text) + text = self.intersperse_blank_char(text, True) + return text + + def pad_with_bos_eos(self, char_sequence: List[str]): + """Pads a sequence with the special BOS and EOS characters.""" + return [self.characters.bos_id] + list(char_sequence) + [self.characters.eos_id] + + def intersperse_blank_char(self, char_sequence: List[str], use_blank_char: bool = False): + """Intersperses the blank character between characters in a sequence. + + Use the ```blank``` character if defined else use the ```pad``` character. + """ + char_to_use = self.characters.blank_id if use_blank_char else self.characters.pad + result = [char_to_use] * (len(char_sequence) * 2 + 1) + result[1::2] = char_sequence + return result + + @staticmethod + def init_from_config(config: "Coqpit", characters: "BaseCharacters" = None): + text_cleaner = multilingual_cleaners + CharactersClass = VitsCharacters + characters, new_config = CharactersClass.init_from_config(config) + # new_config.characters.characters_class = get_import_path(characters) + new_config.characters.characters_class = VitsCharacters + return ( + TTSTokenizer(text_cleaner, characters),new_config) + + +def multilingual_cleaners(text): + """Pipeline for multilingual text""" + text = lowercase(text) + text = replace_symbols(text, lang=None) + text = remove_aux_symbols(text) + text = collapse_whitespace(text) + return text + +def lowercase(text): + return text.lower() + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text).strip() + +def replace_symbols(text, lang="en"): + + text = text.replace(";", ",") + text = text.replace("-", " ") if lang != "ca" else text.replace("-", "") + text = text.replace(":", ",") + if lang == "en": + text = text.replace("&", " and ") + elif lang == "fr": + text = text.replace("&", " et ") + elif lang == "pt": + text = text.replace("&", " e ") + elif lang == "ca": + text = text.replace("&", " i ") + text = text.replace("'", "") + return text + +def remove_aux_symbols(text): + text = re.sub(r"[\<\>\(\)\[\]\"]+", "", text) + return text \ No newline at end of file diff --git a/models/bn_male/jit_infer.py b/models/bn_male/jit_infer.py new file mode 100644 index 0000000000000000000000000000000000000000..839f09b0845a9b6fea0501dce553719435c46e10 --- /dev/null +++ b/models/bn_male/jit_infer.py @@ -0,0 +1,32 @@ +import os +from extra import TTSTokenizer, VitsConfig, CharactersConfig, VitsCharacters +import torch +import numpy as np + +#ch female +with open("chars.txt", 'r') as f: + letters = f.read().strip('\n') +model="bn_male_vits_30hrs.pt" +text = " হলেও আমাদের সবার সার্বিক শৃঙ্খলা বোধের উন্নতি হবে" + +config = VitsConfig( + text_cleaner="multilingual_cleaners", + characters=CharactersConfig( + characters_class=VitsCharacters, + pad="", + eos="", + bos="", + blank="", + characters=letters, + punctuations="!¡'(),-.:;¿? ", + phonemes=None) + ) +tokenizer, config = TTSTokenizer.init_from_config(config) + +x = tokenizer.text_to_ids(text) +x = torch.from_numpy(np.array(x)).unsqueeze(0) +net = torch.jit.load(model) +with torch.no_grad(): + out2 = net(x) +import soundfile as sf +sf.write("jit.wav", out2.squeeze().cpu().numpy(), 22050) \ No newline at end of file diff --git a/models/en_female/.gitattributes b/models/en_female/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b --- /dev/null +++ b/models/en_female/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/models/en_female/README.md b/models/en_female/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b187bb7e7d837a367ccd0862441947ad412c77f7 --- /dev/null +++ b/models/en_female/README.md @@ -0,0 +1,3 @@ +--- +license: cc-by-4.0 +--- diff --git a/models/en_female/chars.txt b/models/en_female/chars.txt new file mode 100644 index 0000000000000000000000000000000000000000..0f31392545923a9ab8ea07cd41796cbe5dcc0089 --- /dev/null +++ b/models/en_female/chars.txt @@ -0,0 +1 @@ +pqw'"sgufmxre?d!lcab,zk.iytoh jvn diff --git a/models/en_female/en_female_vits_30hrs.pt b/models/en_female/en_female_vits_30hrs.pt new file mode 100644 index 0000000000000000000000000000000000000000..e4ab91056595dbac6ae6581c729dcb5914462e20 --- /dev/null +++ b/models/en_female/en_female_vits_30hrs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9dfa80f08da6ca7222a16cb6d919251fb733d3f03042848a20201fa6ae0d0b9c +size 333229574 diff --git a/models/en_female/extra.py b/models/en_female/extra.py new file mode 100644 index 0000000000000000000000000000000000000000..c7db561351da270a7c3931bfe0afefa7bc6d4853 --- /dev/null +++ b/models/en_female/extra.py @@ -0,0 +1,787 @@ +from typing import Callable, Dict, List, Union +from dataclasses import asdict, dataclass, field + + +import re +from dataclasses import replace +from typing import Dict +_whitespace_re = re.compile(r"\s+") + +from dataclasses import dataclass, field +from typing import List + +# from TTS.tts.configs.shared_configs import BaseTTSConfig +# from TTS.tts.models.vits import VitsArgs, VitsAudioConfig + +@dataclass +class CharactersConfig(): + + characters_class: str = None + + # using BaseVocabulary + vocab_dict: Dict = None + + # using on BaseCharacters + pad: str = None + eos: str = None + bos: str = None + blank: str = None + characters: str = None + punctuations: str = None + phonemes: str = None + is_unique: bool = True # for backwards compatibility of models trained with char sets with duplicates + is_sorted: bool = True + + +@dataclass +class BaseTTSConfig(): + + # audio: BaseAudioConfig = field(default_factory=BaseAudioConfig) + # phoneme settings + use_phonemes: bool = False + phonemizer: str = None + phoneme_language: str = None + compute_input_seq_cache: bool = False + text_cleaner: str = None + enable_eos_bos_chars: bool = False + test_sentences_file: str = "" + phoneme_cache_path: str = None + # vocabulary parameters + characters: CharactersConfig = None + add_blank: bool = False + # training params + batch_group_size: int = 0 + loss_masking: bool = None + # dataloading + min_audio_len: int = 1 + max_audio_len: int = float("inf") + min_text_len: int = 1 + max_text_len: int = float("inf") + compute_f0: bool = False + compute_energy: bool = False + compute_linear_spec: bool = False + precompute_num_workers: int = 0 + use_noise_augment: bool = False + start_by_longest: bool = False + shuffle: bool = False + drop_last: bool = False + # dataset + datasets: str = None + # optimizer + optimizer: str = "radam" + optimizer_params: dict = None + # scheduler + lr_scheduler: str = None + lr_scheduler_params: dict = field(default_factory=lambda: {}) + # testing + test_sentences: List[str] = field(default_factory=lambda: []) + # evaluation + eval_split_max_size: int = None + eval_split_size: float = 0.01 + # weighted samplers + use_speaker_weighted_sampler: bool = False + speaker_weighted_sampler_alpha: float = 1.0 + use_language_weighted_sampler: bool = False + language_weighted_sampler_alpha: float = 1.0 + use_length_weighted_sampler: bool = False + length_weighted_sampler_alpha: float = 1.0 + + +@dataclass +class VitsAudioConfig(): + fft_size: int = 1024 + sample_rate: int = 22050 + win_length: int = 1024 + hop_length: int = 256 + num_mels: int = 80 + mel_fmin: int = 0 + mel_fmax: int = None + +@dataclass +class VitsArgs(): + num_chars: int = 100 + out_channels: int = 513 + spec_segment_size: int = 32 + hidden_channels: int = 192 + hidden_channels_ffn_text_encoder: int = 768 + num_heads_text_encoder: int = 2 + num_layers_text_encoder: int = 6 + kernel_size_text_encoder: int = 3 + dropout_p_text_encoder: float = 0.1 + dropout_p_duration_predictor: float = 0.5 + kernel_size_posterior_encoder: int = 5 + dilation_rate_posterior_encoder: int = 1 + num_layers_posterior_encoder: int = 16 + kernel_size_flow: int = 5 + dilation_rate_flow: int = 1 + num_layers_flow: int = 4 + resblock_type_decoder: str = "1" + resblock_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [3, 7, 11]) + resblock_dilation_sizes_decoder: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]) + upsample_rates_decoder: List[int] = field(default_factory=lambda: [8, 8, 2, 2]) + upsample_initial_channel_decoder: int = 512 + upsample_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [16, 16, 4, 4]) + periods_multi_period_discriminator: List[int] = field(default_factory=lambda: [2, 3, 5, 7, 11]) + use_sdp: bool = True + noise_scale: float = 1.0 + inference_noise_scale: float = 0.667 + length_scale: float = 1 + noise_scale_dp: float = 1.0 + inference_noise_scale_dp: float = 1.0 + max_inference_len: int = None + init_discriminator: bool = True + use_spectral_norm_disriminator: bool = False + use_speaker_embedding: bool = False + num_speakers: int = 0 + speakers_file: str = None + d_vector_file: List[str] = None + speaker_embedding_channels: int = 256 + use_d_vector_file: bool = False + d_vector_dim: int = 0 + detach_dp_input: bool = True + use_language_embedding: bool = False + embedded_language_dim: int = 4 + num_languages: int = 0 + language_ids_file: str = None + use_speaker_encoder_as_loss: bool = False + speaker_encoder_config_path: str = "" + speaker_encoder_model_path: str = "" + condition_dp_on_speaker: bool = True + freeze_encoder: bool = False + freeze_DP: bool = False + freeze_PE: bool = False + freeze_flow_decoder: bool = False + freeze_waveform_decoder: bool = False + encoder_sample_rate: int = None + interpolate_z: bool = True + reinit_DP: bool = False + reinit_text_encoder: bool = False +@dataclass +class VitsConfig(BaseTTSConfig): + + model: str = "vits" + # model specific params + model_args: VitsArgs = field(default_factory=VitsArgs) + audio: VitsAudioConfig = field(default_factory=VitsAudioConfig) + + # optimizer + grad_clip: List[float] = field(default_factory=lambda: [1000, 1000]) + lr_gen: float = 0.0002 + lr_disc: float = 0.0002 + lr_scheduler_gen: str = "ExponentialLR" + lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + lr_scheduler_disc: str = "ExponentialLR" + lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + scheduler_after_epoch: bool = True + optimizer: str = "AdamW" + optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01}) + + # loss params + kl_loss_alpha: float = 1.0 + disc_loss_alpha: float = 1.0 + gen_loss_alpha: float = 1.0 + feat_loss_alpha: float = 1.0 + mel_loss_alpha: float = 45.0 + dur_loss_alpha: float = 1.0 + speaker_encoder_loss_alpha: float = 1.0 + + # data loader params + return_wav: bool = True + compute_linear_spec: bool = True + + # sampler params + use_weighted_sampler: bool = False # TODO: move it to the base config + weighted_sampler_attrs: dict = field(default_factory=lambda: {}) + weighted_sampler_multipliers: dict = field(default_factory=lambda: {}) + + # overrides + r: int = 1 # DO NOT CHANGE + add_blank: bool = True + + # testing + test_sentences: List[List] = field( + default_factory=lambda: [ + ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."], + ["Be a voice, not an echo."], + ["I'm sorry Dave. I'm afraid I can't do that."], + ["This cake is great. It's so delicious and moist."], + ["Prior to November 22, 1963."], + ] + ) + + # multi-speaker settings + # use speaker embedding layer + num_speakers: int = 0 + use_speaker_embedding: bool = False + speakers_file: str = None + speaker_embedding_channels: int = 256 + language_ids_file: str = None + use_language_embedding: bool = False + + # use d-vectors + use_d_vector_file: bool = False + d_vector_file: List[str] = None + d_vector_dim: int = None + + def __post_init__(self): + pass + # for key, val in self.model_args.items(): + # if hasattr(self, key): + # self[key] = val + + + + + +def parse_symbols(): + return { + "pad": _pad, + "eos": _eos, + "bos": _bos, + "characters": _characters, + "punctuations": _punctuations, + "phonemes": _phonemes, + } + + +# DEFAULT SET OF GRAPHEMES +_pad = "" +_eos = "" +_bos = "" +_blank = "" # TODO: check if we need this alongside with PAD +_characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +_punctuations = "!'(),-.:;? " + + +# DEFAULT SET OF IPA PHONEMES +# Phonemes definition (All IPA characters) +_vowels = "iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻ" +_non_pulmonic_consonants = "ʘɓǀɗǃʄǂɠǁʛ" +_pulmonic_consonants = "pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟ" +_suprasegmentals = "ˈˌːˑ" +_other_symbols = "ʍwɥʜʢʡɕʑɺɧʲ" +_diacrilics = "ɚ˞ɫ" +_phonemes = _vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics + + +class BaseVocabulary: + """Base Vocabulary class. + + This class only needs a vocabulary dictionary without specifying the characters. + + Args: + vocab (Dict): A dictionary of characters and their corresponding indices. + """ + + def __init__(self, vocab: Dict, pad: str = None, blank: str = None, bos: str = None, eos: str = None): + self.vocab = vocab + self.pad = pad + self.blank = blank + self.bos = bos + self.eos = eos + + @property + def pad_id(self) -> int: + """Return the index of the padding character. If the padding character is not specified, return the length + of the vocabulary.""" + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + """Return the index of the blank character. If the blank character is not specified, return the length of + the vocabulary.""" + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def bos_id(self) -> int: + """Return the index of the bos character. If the bos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def eos_id(self) -> int: + """Return the index of the eos character. If the eos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def vocab(self): + """Return the vocabulary dictionary.""" + return self._vocab + + @vocab.setter + def vocab(self, vocab): + """Set the vocabulary dictionary and character mapping dictionaries.""" + self._vocab, self._char_to_id, self._id_to_char = None, None, None + if vocab is not None: + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self._vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self._vocab) # pylint: disable=unnecessary-comprehension + } + + @staticmethod + def init_from_config(config, **kwargs): + """Initialize from the given config.""" + if config.characters is not None and "vocab_dict" in config.characters and config.characters.vocab_dict: + return ( + BaseVocabulary( + config.characters.vocab_dict, + config.characters.pad, + config.characters.blank, + config.characters.bos, + config.characters.eos, + ), + config, + ) + return BaseVocabulary(**kwargs), config + + def to_config(self): + return CharactersConfig( + vocab_dict=self._vocab, + pad=self.pad, + eos=self.eos, + bos=self.bos, + blank=self.blank, + is_unique=False, + is_sorted=False, + ) + + @property + def num_chars(self): + """Return number of tokens in the vocabulary.""" + return len(self._vocab) + + def char_to_id(self, char: str) -> int: + """Map a character to an token ID.""" + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + """Map an token ID to a character.""" + return self._id_to_char[idx] + + +class BaseCharacters: + + + def __init__( + self, + characters: str = None, + punctuations: str = None, + pad: str = None, + eos: str = None, + bos: str = None, + blank: str = None, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + self._characters = characters + self._punctuations = punctuations + self._pad = pad + self._eos = eos + self._bos = bos + self._blank = blank + self.is_unique = is_unique + self.is_sorted = is_sorted + self._create_vocab() + + @property + def pad_id(self) -> int: + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def eos_id(self) -> int: + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def bos_id(self) -> int: + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, characters): + self._characters = characters + self._create_vocab() + + @property + def punctuations(self): + return self._punctuations + + @punctuations.setter + def punctuations(self, punctuations): + self._punctuations = punctuations + self._create_vocab() + + @property + def pad(self): + return self._pad + + @pad.setter + def pad(self, pad): + self._pad = pad + self._create_vocab() + + @property + def eos(self): + return self._eos + + @eos.setter + def eos(self, eos): + self._eos = eos + self._create_vocab() + + @property + def bos(self): + return self._bos + + @bos.setter + def bos(self, bos): + self._bos = bos + self._create_vocab() + + @property + def blank(self): + return self._blank + + @blank.setter + def blank(self, blank): + self._blank = blank + self._create_vocab() + + @property + def vocab(self): + return self._vocab + + @vocab.setter + def vocab(self, vocab): + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self.vocab) # pylint: disable=unnecessary-comprehension + } + + @property + def num_chars(self): + return len(self._vocab) + + def _create_vocab(self): + _vocab = self._characters + if self.is_unique: + _vocab = list(set(_vocab)) + if self.is_sorted: + _vocab = sorted(_vocab) + _vocab = list(_vocab) + _vocab = [self._blank] + _vocab if self._blank is not None and len(self._blank) > 0 else _vocab + _vocab = [self._bos] + _vocab if self._bos is not None and len(self._bos) > 0 else _vocab + _vocab = [self._eos] + _vocab if self._eos is not None and len(self._eos) > 0 else _vocab + _vocab = [self._pad] + _vocab if self._pad is not None and len(self._pad) > 0 else _vocab + self.vocab = _vocab + list(self._punctuations) + if self.is_unique: + duplicates = {x for x in self.vocab if self.vocab.count(x) > 1} + assert ( + len(self.vocab) == len(self._char_to_id) == len(self._id_to_char) + ), f" [!] There are duplicate characters in the character set. {duplicates}" + + def char_to_id(self, char: str) -> int: + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + return self._id_to_char[idx] + + def print_log(self, level: int = 0): + """ + Prints the vocabulary in a nice format. + """ + indent = "\t" * level + print(f"{indent}| > Characters: {self._characters}") + print(f"{indent}| > Punctuations: {self._punctuations}") + print(f"{indent}| > Pad: {self._pad}") + print(f"{indent}| > EOS: {self._eos}") + print(f"{indent}| > BOS: {self._bos}") + print(f"{indent}| > Blank: {self._blank}") + print(f"{indent}| > Vocab: {self.vocab}") + print(f"{indent}| > Num chars: {self.num_chars}") + + @staticmethod + def init_from_config(config: "Coqpit"): # pylint: disable=unused-argument + """Init your character class from a config. + + Implement this method for your subclass. + """ + # use character set from config + if config.characters is not None: + return BaseCharacters(**config.characters), config + # return default character set + characters = BaseCharacters() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=self._eos, + bos=self._bos, + blank=self._blank, + is_unique=self.is_unique, + is_sorted=self.is_sorted, + ) + + +class IPAPhonemes(BaseCharacters): + + + def __init__( + self, + characters: str = _phonemes, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a IPAPhonemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + # band-aid for compatibility with old models + if "characters" in config and config.characters is not None: + if "phonemes" in config.characters and config.characters.phonemes is not None: + config.characters["characters"] = config.characters["phonemes"] + return ( + IPAPhonemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + # use character set from config + if config.characters is not None: + return IPAPhonemes(**config.characters), config + # return default character set + characters = IPAPhonemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +class Graphemes(BaseCharacters): + + + def __init__( + self, + characters: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a Graphemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + if config.characters is not None: + # band-aid for compatibility with old models + if "phonemes" in config.characters: + return ( + Graphemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + return Graphemes(**config.characters), config + characters = Graphemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +if __name__ == "__main__": + gr = Graphemes() + ph = IPAPhonemes() + gr.print_log() + ph.print_log() + + +class VitsCharacters(BaseCharacters): + """Characters class for VITs model for compatibility with pre-trained models""" + + def __init__( + self, + graphemes: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + ipa_characters: str = _phonemes, + ) -> None: + if ipa_characters is not None: + graphemes += ipa_characters + super().__init__(graphemes, punctuations, pad, None, None, "", is_unique=False, is_sorted=True) + + def _create_vocab(self): + self._vocab = [self._pad] + list(self._punctuations) + list(self._characters) + [self._blank] + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + # pylint: disable=unnecessary-comprehension + self._id_to_char = {idx: char for idx, char in enumerate(self.vocab)} + + @staticmethod + def init_from_config(config): + _pad = config.characters.pad + _punctuations = config.characters.punctuations + _letters = config.characters.characters + _letters_ipa = config.characters.phonemes + return ( + VitsCharacters(graphemes=_letters, ipa_characters=_letters_ipa, punctuations=_punctuations, pad=_pad), + config, + ) + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=None, + bos=None, + blank=self._blank, + is_unique=False, + is_sorted=True, + ) + +class TTSTokenizer: + def __init__( + self, + text_cleaner: Callable = None, + characters: "BaseCharacters" = None, + ): + self.text_cleaner = text_cleaner + self.characters = characters + self.not_found_characters = [] + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, new_characters): + self._characters = new_characters + self.pad_id = self.characters.char_to_id(self.characters.pad) if self.characters.pad else None + self.blank_id = self.characters.char_to_id(self.characters.blank) if self.characters.blank else None + + def encode(self, text: str) -> List[int]: + """Encodes a string of text as a sequence of IDs.""" + token_ids = [] + for char in text: + try: + idx = self.characters.char_to_id(char) + token_ids.append(idx) + except KeyError: + # discard but store not found characters + if char not in self.not_found_characters: + self.not_found_characters.append(char) + print(text) + print(f" [!] Character {repr(char)} not found in the vocabulary. Discarding it.") + return token_ids + + def text_to_ids(self, text: str, language: str = None) -> List[int]: # pylint: disable=unused-argument + text = self.text_cleaner(text) + text = self.encode(text) + text = self.intersperse_blank_char(text, True) + return text + + def pad_with_bos_eos(self, char_sequence: List[str]): + """Pads a sequence with the special BOS and EOS characters.""" + return [self.characters.bos_id] + list(char_sequence) + [self.characters.eos_id] + + def intersperse_blank_char(self, char_sequence: List[str], use_blank_char: bool = False): + """Intersperses the blank character between characters in a sequence. + + Use the ```blank``` character if defined else use the ```pad``` character. + """ + char_to_use = self.characters.blank_id if use_blank_char else self.characters.pad + result = [char_to_use] * (len(char_sequence) * 2 + 1) + result[1::2] = char_sequence + return result + + @staticmethod + def init_from_config(config: "Coqpit", characters: "BaseCharacters" = None): + text_cleaner = multilingual_cleaners + CharactersClass = VitsCharacters + characters, new_config = CharactersClass.init_from_config(config) + # new_config.characters.characters_class = get_import_path(characters) + new_config.characters.characters_class = VitsCharacters + return ( + TTSTokenizer(text_cleaner, characters),new_config) + + +def multilingual_cleaners(text): + """Pipeline for multilingual text""" + text = lowercase(text) + text = replace_symbols(text, lang=None) + text = remove_aux_symbols(text) + text = collapse_whitespace(text) + return text + +def lowercase(text): + return text.lower() + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text).strip() + +def replace_symbols(text, lang="en"): + + text = text.replace(";", ",") + text = text.replace("-", " ") if lang != "ca" else text.replace("-", "") + text = text.replace(":", ",") + if lang == "en": + text = text.replace("&", " and ") + elif lang == "fr": + text = text.replace("&", " et ") + elif lang == "pt": + text = text.replace("&", " e ") + elif lang == "ca": + text = text.replace("&", " i ") + text = text.replace("'", "") + return text + +def remove_aux_symbols(text): + text = re.sub(r"[\<\>\(\)\[\]\"]+", "", text) + return text \ No newline at end of file diff --git a/models/en_female/jit_infer.py b/models/en_female/jit_infer.py new file mode 100644 index 0000000000000000000000000000000000000000..4100e1602fec0c68cdaf80a3b84547c68f3527fb --- /dev/null +++ b/models/en_female/jit_infer.py @@ -0,0 +1,33 @@ +import os +from extra import TTSTokenizer, VitsConfig, CharactersConfig, VitsCharacters +import torch +import numpy as np + +#ch female +with open("chars.txt", 'r') as f: + letters = f.read().strip('\n') +model="en_female_vits_30hrs.pt" +# text = " হলেও আমাদের সবার সার্বিক শৃঙ্খলা বোধের উন্নতি হবে" +text = "My name is g p t, chat g p t" + +config = VitsConfig( + text_cleaner="multilingual_cleaners", + characters=CharactersConfig( + characters_class=VitsCharacters, + pad="", + eos="", + bos="", + blank="", + characters=letters, + punctuations="!¡'(),-.:;¿? ", + phonemes=None) + ) +tokenizer, config = TTSTokenizer.init_from_config(config) + +x = tokenizer.text_to_ids(text) +x = torch.from_numpy(np.array(x)).unsqueeze(0) +net = torch.jit.load(model) +with torch.no_grad(): + out2 = net(x) +import soundfile as sf +sf.write("jit.wav", out2.squeeze().cpu().numpy(), 22050) \ No newline at end of file diff --git a/models/en_male/.gitattributes b/models/en_male/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b --- /dev/null +++ b/models/en_male/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/models/en_male/README.md b/models/en_male/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b187bb7e7d837a367ccd0862441947ad412c77f7 --- /dev/null +++ b/models/en_male/README.md @@ -0,0 +1,3 @@ +--- +license: cc-by-4.0 +--- diff --git a/models/en_male/chars.txt b/models/en_male/chars.txt new file mode 100644 index 0000000000000000000000000000000000000000..0f31392545923a9ab8ea07cd41796cbe5dcc0089 --- /dev/null +++ b/models/en_male/chars.txt @@ -0,0 +1 @@ +pqw'"sgufmxre?d!lcab,zk.iytoh jvn diff --git a/models/en_male/en_male_vits_30hrs.pt b/models/en_male/en_male_vits_30hrs.pt new file mode 100644 index 0000000000000000000000000000000000000000..c3696d77ea524c447d817292dbf0af7a06b0ce95 --- /dev/null +++ b/models/en_male/en_male_vits_30hrs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ffa1099438a58c8a13e437d39ec304b530644156ef445032e64422d83e558666 +size 333224012 diff --git a/models/en_male/extra.py b/models/en_male/extra.py new file mode 100644 index 0000000000000000000000000000000000000000..c7db561351da270a7c3931bfe0afefa7bc6d4853 --- /dev/null +++ b/models/en_male/extra.py @@ -0,0 +1,787 @@ +from typing import Callable, Dict, List, Union +from dataclasses import asdict, dataclass, field + + +import re +from dataclasses import replace +from typing import Dict +_whitespace_re = re.compile(r"\s+") + +from dataclasses import dataclass, field +from typing import List + +# from TTS.tts.configs.shared_configs import BaseTTSConfig +# from TTS.tts.models.vits import VitsArgs, VitsAudioConfig + +@dataclass +class CharactersConfig(): + + characters_class: str = None + + # using BaseVocabulary + vocab_dict: Dict = None + + # using on BaseCharacters + pad: str = None + eos: str = None + bos: str = None + blank: str = None + characters: str = None + punctuations: str = None + phonemes: str = None + is_unique: bool = True # for backwards compatibility of models trained with char sets with duplicates + is_sorted: bool = True + + +@dataclass +class BaseTTSConfig(): + + # audio: BaseAudioConfig = field(default_factory=BaseAudioConfig) + # phoneme settings + use_phonemes: bool = False + phonemizer: str = None + phoneme_language: str = None + compute_input_seq_cache: bool = False + text_cleaner: str = None + enable_eos_bos_chars: bool = False + test_sentences_file: str = "" + phoneme_cache_path: str = None + # vocabulary parameters + characters: CharactersConfig = None + add_blank: bool = False + # training params + batch_group_size: int = 0 + loss_masking: bool = None + # dataloading + min_audio_len: int = 1 + max_audio_len: int = float("inf") + min_text_len: int = 1 + max_text_len: int = float("inf") + compute_f0: bool = False + compute_energy: bool = False + compute_linear_spec: bool = False + precompute_num_workers: int = 0 + use_noise_augment: bool = False + start_by_longest: bool = False + shuffle: bool = False + drop_last: bool = False + # dataset + datasets: str = None + # optimizer + optimizer: str = "radam" + optimizer_params: dict = None + # scheduler + lr_scheduler: str = None + lr_scheduler_params: dict = field(default_factory=lambda: {}) + # testing + test_sentences: List[str] = field(default_factory=lambda: []) + # evaluation + eval_split_max_size: int = None + eval_split_size: float = 0.01 + # weighted samplers + use_speaker_weighted_sampler: bool = False + speaker_weighted_sampler_alpha: float = 1.0 + use_language_weighted_sampler: bool = False + language_weighted_sampler_alpha: float = 1.0 + use_length_weighted_sampler: bool = False + length_weighted_sampler_alpha: float = 1.0 + + +@dataclass +class VitsAudioConfig(): + fft_size: int = 1024 + sample_rate: int = 22050 + win_length: int = 1024 + hop_length: int = 256 + num_mels: int = 80 + mel_fmin: int = 0 + mel_fmax: int = None + +@dataclass +class VitsArgs(): + num_chars: int = 100 + out_channels: int = 513 + spec_segment_size: int = 32 + hidden_channels: int = 192 + hidden_channels_ffn_text_encoder: int = 768 + num_heads_text_encoder: int = 2 + num_layers_text_encoder: int = 6 + kernel_size_text_encoder: int = 3 + dropout_p_text_encoder: float = 0.1 + dropout_p_duration_predictor: float = 0.5 + kernel_size_posterior_encoder: int = 5 + dilation_rate_posterior_encoder: int = 1 + num_layers_posterior_encoder: int = 16 + kernel_size_flow: int = 5 + dilation_rate_flow: int = 1 + num_layers_flow: int = 4 + resblock_type_decoder: str = "1" + resblock_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [3, 7, 11]) + resblock_dilation_sizes_decoder: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]) + upsample_rates_decoder: List[int] = field(default_factory=lambda: [8, 8, 2, 2]) + upsample_initial_channel_decoder: int = 512 + upsample_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [16, 16, 4, 4]) + periods_multi_period_discriminator: List[int] = field(default_factory=lambda: [2, 3, 5, 7, 11]) + use_sdp: bool = True + noise_scale: float = 1.0 + inference_noise_scale: float = 0.667 + length_scale: float = 1 + noise_scale_dp: float = 1.0 + inference_noise_scale_dp: float = 1.0 + max_inference_len: int = None + init_discriminator: bool = True + use_spectral_norm_disriminator: bool = False + use_speaker_embedding: bool = False + num_speakers: int = 0 + speakers_file: str = None + d_vector_file: List[str] = None + speaker_embedding_channels: int = 256 + use_d_vector_file: bool = False + d_vector_dim: int = 0 + detach_dp_input: bool = True + use_language_embedding: bool = False + embedded_language_dim: int = 4 + num_languages: int = 0 + language_ids_file: str = None + use_speaker_encoder_as_loss: bool = False + speaker_encoder_config_path: str = "" + speaker_encoder_model_path: str = "" + condition_dp_on_speaker: bool = True + freeze_encoder: bool = False + freeze_DP: bool = False + freeze_PE: bool = False + freeze_flow_decoder: bool = False + freeze_waveform_decoder: bool = False + encoder_sample_rate: int = None + interpolate_z: bool = True + reinit_DP: bool = False + reinit_text_encoder: bool = False +@dataclass +class VitsConfig(BaseTTSConfig): + + model: str = "vits" + # model specific params + model_args: VitsArgs = field(default_factory=VitsArgs) + audio: VitsAudioConfig = field(default_factory=VitsAudioConfig) + + # optimizer + grad_clip: List[float] = field(default_factory=lambda: [1000, 1000]) + lr_gen: float = 0.0002 + lr_disc: float = 0.0002 + lr_scheduler_gen: str = "ExponentialLR" + lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + lr_scheduler_disc: str = "ExponentialLR" + lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + scheduler_after_epoch: bool = True + optimizer: str = "AdamW" + optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01}) + + # loss params + kl_loss_alpha: float = 1.0 + disc_loss_alpha: float = 1.0 + gen_loss_alpha: float = 1.0 + feat_loss_alpha: float = 1.0 + mel_loss_alpha: float = 45.0 + dur_loss_alpha: float = 1.0 + speaker_encoder_loss_alpha: float = 1.0 + + # data loader params + return_wav: bool = True + compute_linear_spec: bool = True + + # sampler params + use_weighted_sampler: bool = False # TODO: move it to the base config + weighted_sampler_attrs: dict = field(default_factory=lambda: {}) + weighted_sampler_multipliers: dict = field(default_factory=lambda: {}) + + # overrides + r: int = 1 # DO NOT CHANGE + add_blank: bool = True + + # testing + test_sentences: List[List] = field( + default_factory=lambda: [ + ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."], + ["Be a voice, not an echo."], + ["I'm sorry Dave. I'm afraid I can't do that."], + ["This cake is great. It's so delicious and moist."], + ["Prior to November 22, 1963."], + ] + ) + + # multi-speaker settings + # use speaker embedding layer + num_speakers: int = 0 + use_speaker_embedding: bool = False + speakers_file: str = None + speaker_embedding_channels: int = 256 + language_ids_file: str = None + use_language_embedding: bool = False + + # use d-vectors + use_d_vector_file: bool = False + d_vector_file: List[str] = None + d_vector_dim: int = None + + def __post_init__(self): + pass + # for key, val in self.model_args.items(): + # if hasattr(self, key): + # self[key] = val + + + + + +def parse_symbols(): + return { + "pad": _pad, + "eos": _eos, + "bos": _bos, + "characters": _characters, + "punctuations": _punctuations, + "phonemes": _phonemes, + } + + +# DEFAULT SET OF GRAPHEMES +_pad = "" +_eos = "" +_bos = "" +_blank = "" # TODO: check if we need this alongside with PAD +_characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +_punctuations = "!'(),-.:;? " + + +# DEFAULT SET OF IPA PHONEMES +# Phonemes definition (All IPA characters) +_vowels = "iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻ" +_non_pulmonic_consonants = "ʘɓǀɗǃʄǂɠǁʛ" +_pulmonic_consonants = "pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟ" +_suprasegmentals = "ˈˌːˑ" +_other_symbols = "ʍwɥʜʢʡɕʑɺɧʲ" +_diacrilics = "ɚ˞ɫ" +_phonemes = _vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics + + +class BaseVocabulary: + """Base Vocabulary class. + + This class only needs a vocabulary dictionary without specifying the characters. + + Args: + vocab (Dict): A dictionary of characters and their corresponding indices. + """ + + def __init__(self, vocab: Dict, pad: str = None, blank: str = None, bos: str = None, eos: str = None): + self.vocab = vocab + self.pad = pad + self.blank = blank + self.bos = bos + self.eos = eos + + @property + def pad_id(self) -> int: + """Return the index of the padding character. If the padding character is not specified, return the length + of the vocabulary.""" + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + """Return the index of the blank character. If the blank character is not specified, return the length of + the vocabulary.""" + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def bos_id(self) -> int: + """Return the index of the bos character. If the bos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def eos_id(self) -> int: + """Return the index of the eos character. If the eos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def vocab(self): + """Return the vocabulary dictionary.""" + return self._vocab + + @vocab.setter + def vocab(self, vocab): + """Set the vocabulary dictionary and character mapping dictionaries.""" + self._vocab, self._char_to_id, self._id_to_char = None, None, None + if vocab is not None: + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self._vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self._vocab) # pylint: disable=unnecessary-comprehension + } + + @staticmethod + def init_from_config(config, **kwargs): + """Initialize from the given config.""" + if config.characters is not None and "vocab_dict" in config.characters and config.characters.vocab_dict: + return ( + BaseVocabulary( + config.characters.vocab_dict, + config.characters.pad, + config.characters.blank, + config.characters.bos, + config.characters.eos, + ), + config, + ) + return BaseVocabulary(**kwargs), config + + def to_config(self): + return CharactersConfig( + vocab_dict=self._vocab, + pad=self.pad, + eos=self.eos, + bos=self.bos, + blank=self.blank, + is_unique=False, + is_sorted=False, + ) + + @property + def num_chars(self): + """Return number of tokens in the vocabulary.""" + return len(self._vocab) + + def char_to_id(self, char: str) -> int: + """Map a character to an token ID.""" + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + """Map an token ID to a character.""" + return self._id_to_char[idx] + + +class BaseCharacters: + + + def __init__( + self, + characters: str = None, + punctuations: str = None, + pad: str = None, + eos: str = None, + bos: str = None, + blank: str = None, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + self._characters = characters + self._punctuations = punctuations + self._pad = pad + self._eos = eos + self._bos = bos + self._blank = blank + self.is_unique = is_unique + self.is_sorted = is_sorted + self._create_vocab() + + @property + def pad_id(self) -> int: + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def eos_id(self) -> int: + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def bos_id(self) -> int: + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, characters): + self._characters = characters + self._create_vocab() + + @property + def punctuations(self): + return self._punctuations + + @punctuations.setter + def punctuations(self, punctuations): + self._punctuations = punctuations + self._create_vocab() + + @property + def pad(self): + return self._pad + + @pad.setter + def pad(self, pad): + self._pad = pad + self._create_vocab() + + @property + def eos(self): + return self._eos + + @eos.setter + def eos(self, eos): + self._eos = eos + self._create_vocab() + + @property + def bos(self): + return self._bos + + @bos.setter + def bos(self, bos): + self._bos = bos + self._create_vocab() + + @property + def blank(self): + return self._blank + + @blank.setter + def blank(self, blank): + self._blank = blank + self._create_vocab() + + @property + def vocab(self): + return self._vocab + + @vocab.setter + def vocab(self, vocab): + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self.vocab) # pylint: disable=unnecessary-comprehension + } + + @property + def num_chars(self): + return len(self._vocab) + + def _create_vocab(self): + _vocab = self._characters + if self.is_unique: + _vocab = list(set(_vocab)) + if self.is_sorted: + _vocab = sorted(_vocab) + _vocab = list(_vocab) + _vocab = [self._blank] + _vocab if self._blank is not None and len(self._blank) > 0 else _vocab + _vocab = [self._bos] + _vocab if self._bos is not None and len(self._bos) > 0 else _vocab + _vocab = [self._eos] + _vocab if self._eos is not None and len(self._eos) > 0 else _vocab + _vocab = [self._pad] + _vocab if self._pad is not None and len(self._pad) > 0 else _vocab + self.vocab = _vocab + list(self._punctuations) + if self.is_unique: + duplicates = {x for x in self.vocab if self.vocab.count(x) > 1} + assert ( + len(self.vocab) == len(self._char_to_id) == len(self._id_to_char) + ), f" [!] There are duplicate characters in the character set. {duplicates}" + + def char_to_id(self, char: str) -> int: + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + return self._id_to_char[idx] + + def print_log(self, level: int = 0): + """ + Prints the vocabulary in a nice format. + """ + indent = "\t" * level + print(f"{indent}| > Characters: {self._characters}") + print(f"{indent}| > Punctuations: {self._punctuations}") + print(f"{indent}| > Pad: {self._pad}") + print(f"{indent}| > EOS: {self._eos}") + print(f"{indent}| > BOS: {self._bos}") + print(f"{indent}| > Blank: {self._blank}") + print(f"{indent}| > Vocab: {self.vocab}") + print(f"{indent}| > Num chars: {self.num_chars}") + + @staticmethod + def init_from_config(config: "Coqpit"): # pylint: disable=unused-argument + """Init your character class from a config. + + Implement this method for your subclass. + """ + # use character set from config + if config.characters is not None: + return BaseCharacters(**config.characters), config + # return default character set + characters = BaseCharacters() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=self._eos, + bos=self._bos, + blank=self._blank, + is_unique=self.is_unique, + is_sorted=self.is_sorted, + ) + + +class IPAPhonemes(BaseCharacters): + + + def __init__( + self, + characters: str = _phonemes, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a IPAPhonemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + # band-aid for compatibility with old models + if "characters" in config and config.characters is not None: + if "phonemes" in config.characters and config.characters.phonemes is not None: + config.characters["characters"] = config.characters["phonemes"] + return ( + IPAPhonemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + # use character set from config + if config.characters is not None: + return IPAPhonemes(**config.characters), config + # return default character set + characters = IPAPhonemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +class Graphemes(BaseCharacters): + + + def __init__( + self, + characters: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a Graphemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + if config.characters is not None: + # band-aid for compatibility with old models + if "phonemes" in config.characters: + return ( + Graphemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + return Graphemes(**config.characters), config + characters = Graphemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +if __name__ == "__main__": + gr = Graphemes() + ph = IPAPhonemes() + gr.print_log() + ph.print_log() + + +class VitsCharacters(BaseCharacters): + """Characters class for VITs model for compatibility with pre-trained models""" + + def __init__( + self, + graphemes: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + ipa_characters: str = _phonemes, + ) -> None: + if ipa_characters is not None: + graphemes += ipa_characters + super().__init__(graphemes, punctuations, pad, None, None, "", is_unique=False, is_sorted=True) + + def _create_vocab(self): + self._vocab = [self._pad] + list(self._punctuations) + list(self._characters) + [self._blank] + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + # pylint: disable=unnecessary-comprehension + self._id_to_char = {idx: char for idx, char in enumerate(self.vocab)} + + @staticmethod + def init_from_config(config): + _pad = config.characters.pad + _punctuations = config.characters.punctuations + _letters = config.characters.characters + _letters_ipa = config.characters.phonemes + return ( + VitsCharacters(graphemes=_letters, ipa_characters=_letters_ipa, punctuations=_punctuations, pad=_pad), + config, + ) + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=None, + bos=None, + blank=self._blank, + is_unique=False, + is_sorted=True, + ) + +class TTSTokenizer: + def __init__( + self, + text_cleaner: Callable = None, + characters: "BaseCharacters" = None, + ): + self.text_cleaner = text_cleaner + self.characters = characters + self.not_found_characters = [] + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, new_characters): + self._characters = new_characters + self.pad_id = self.characters.char_to_id(self.characters.pad) if self.characters.pad else None + self.blank_id = self.characters.char_to_id(self.characters.blank) if self.characters.blank else None + + def encode(self, text: str) -> List[int]: + """Encodes a string of text as a sequence of IDs.""" + token_ids = [] + for char in text: + try: + idx = self.characters.char_to_id(char) + token_ids.append(idx) + except KeyError: + # discard but store not found characters + if char not in self.not_found_characters: + self.not_found_characters.append(char) + print(text) + print(f" [!] Character {repr(char)} not found in the vocabulary. Discarding it.") + return token_ids + + def text_to_ids(self, text: str, language: str = None) -> List[int]: # pylint: disable=unused-argument + text = self.text_cleaner(text) + text = self.encode(text) + text = self.intersperse_blank_char(text, True) + return text + + def pad_with_bos_eos(self, char_sequence: List[str]): + """Pads a sequence with the special BOS and EOS characters.""" + return [self.characters.bos_id] + list(char_sequence) + [self.characters.eos_id] + + def intersperse_blank_char(self, char_sequence: List[str], use_blank_char: bool = False): + """Intersperses the blank character between characters in a sequence. + + Use the ```blank``` character if defined else use the ```pad``` character. + """ + char_to_use = self.characters.blank_id if use_blank_char else self.characters.pad + result = [char_to_use] * (len(char_sequence) * 2 + 1) + result[1::2] = char_sequence + return result + + @staticmethod + def init_from_config(config: "Coqpit", characters: "BaseCharacters" = None): + text_cleaner = multilingual_cleaners + CharactersClass = VitsCharacters + characters, new_config = CharactersClass.init_from_config(config) + # new_config.characters.characters_class = get_import_path(characters) + new_config.characters.characters_class = VitsCharacters + return ( + TTSTokenizer(text_cleaner, characters),new_config) + + +def multilingual_cleaners(text): + """Pipeline for multilingual text""" + text = lowercase(text) + text = replace_symbols(text, lang=None) + text = remove_aux_symbols(text) + text = collapse_whitespace(text) + return text + +def lowercase(text): + return text.lower() + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text).strip() + +def replace_symbols(text, lang="en"): + + text = text.replace(";", ",") + text = text.replace("-", " ") if lang != "ca" else text.replace("-", "") + text = text.replace(":", ",") + if lang == "en": + text = text.replace("&", " and ") + elif lang == "fr": + text = text.replace("&", " et ") + elif lang == "pt": + text = text.replace("&", " e ") + elif lang == "ca": + text = text.replace("&", " i ") + text = text.replace("'", "") + return text + +def remove_aux_symbols(text): + text = re.sub(r"[\<\>\(\)\[\]\"]+", "", text) + return text \ No newline at end of file diff --git a/models/en_male/jit_infer.py b/models/en_male/jit_infer.py new file mode 100644 index 0000000000000000000000000000000000000000..e056529b46c4be7a060acca1efaf4f06d783c11a --- /dev/null +++ b/models/en_male/jit_infer.py @@ -0,0 +1,32 @@ +import os +from extra import TTSTokenizer, VitsConfig, CharactersConfig, VitsCharacters +import torch +import numpy as np + +#ch female +with open("chars.txt", 'r') as f: + letters = f.read().strip('\n') +model="en_male_vits_30hrs.pt" +text = "This is a text to b spoken" + +config = VitsConfig( + text_cleaner="multilingual_cleaners", + characters=CharactersConfig( + characters_class=VitsCharacters, + pad="", + eos="", + bos="", + blank="", + characters=letters, + punctuations="!¡'(),-.:;¿? ", + phonemes=None) + ) +tokenizer, config = TTSTokenizer.init_from_config(config) + +x = tokenizer.text_to_ids(text) +x = torch.from_numpy(np.array(x)).unsqueeze(0) +net = torch.jit.load(model) +with torch.no_grad(): + out2 = net(x) +import soundfile as sf +sf.write("jit.wav", out2.squeeze().cpu().numpy(), 22050) \ No newline at end of file diff --git a/models/gu_mms/config.json b/models/gu_mms/config.json new file mode 100644 index 0000000000000000000000000000000000000000..f0020805fe7b3288a23eb66b7093bffc8542763a --- /dev/null +++ b/models/gu_mms/config.json @@ -0,0 +1,82 @@ +{ + "activation_dropout": 0.1, + "architectures": [ + "VitsModel" + ], + "attention_dropout": 0.1, + "depth_separable_channels": 2, + "depth_separable_num_layers": 3, + "duration_predictor_dropout": 0.5, + "duration_predictor_filter_channels": 256, + "duration_predictor_flow_bins": 10, + "duration_predictor_kernel_size": 3, + "duration_predictor_num_flows": 4, + "duration_predictor_tail_bound": 5.0, + "ffn_dim": 768, + "ffn_kernel_size": 3, + "flow_size": 192, + "hidden_act": "relu", + "hidden_dropout": 0.1, + "hidden_size": 192, + "initializer_range": 0.02, + "layer_norm_eps": 1e-05, + "layerdrop": 0.1, + "leaky_relu_slope": 0.1, + "model_type": "vits", + "noise_scale": 0.667, + "noise_scale_duration": 0.8, + "num_attention_heads": 2, + "num_hidden_layers": 6, + "num_speakers": 1, + "posterior_encoder_num_wavenet_layers": 16, + "prior_encoder_num_flows": 4, + "prior_encoder_num_wavenet_layers": 4, + "resblock_dilation_sizes": [ + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ], + [ + 1, + 3, + 5 + ] + ], + "resblock_kernel_sizes": [ + 3, + 7, + 11 + ], + "sampling_rate": 16000, + "speaker_embedding_size": 0, + "speaking_rate": 1.0, + "spectrogram_bins": 513, + "torch_dtype": "float32", + "transformers_version": "4.33.0.dev0", + "upsample_initial_channel": 512, + "upsample_kernel_sizes": [ + 16, + 16, + 4, + 4 + ], + "upsample_rates": [ + 8, + 8, + 2, + 2 + ], + "use_bias": true, + "use_stochastic_duration_prediction": true, + "vocab_size": 60, + "wavenet_dilation_rate": 1, + "wavenet_dropout": 0.0, + "wavenet_kernel_size": 5, + "window_size": 4 +} diff --git a/models/gu_mms/special_tokens_map.json b/models/gu_mms/special_tokens_map.json new file mode 100644 index 0000000000000000000000000000000000000000..d7b57bd9216b39a1535356cfc46d4fe83c31a10d --- /dev/null +++ b/models/gu_mms/special_tokens_map.json @@ -0,0 +1,4 @@ +{ + "pad_token": "|", + "unk_token": "" +} diff --git a/models/gu_mms/tokenizer_config.json b/models/gu_mms/tokenizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..534efc7ec43b38fb928d6f8cc672f8b09c11a432 --- /dev/null +++ b/models/gu_mms/tokenizer_config.json @@ -0,0 +1,12 @@ +{ + "add_blank": true, + "clean_up_tokenization_spaces": true, + "is_uroman": false, + "language": "guj", + "model_max_length": 1000000000000000019884624838656, + "normalize": true, + "pad_token": "|", + "phonemize": false, + "tokenizer_class": "VitsTokenizer", + "unk_token": "" +} diff --git a/models/gu_mms/vocab.json b/models/gu_mms/vocab.json new file mode 100644 index 0000000000000000000000000000000000000000..97a03a93bc6bf4fa3caba55ebb651fe32ca2457c --- /dev/null +++ b/models/gu_mms/vocab.json @@ -0,0 +1,62 @@ +{ + " ": 59, + "'": 47, + "-": 56, + "|": 0, + "ં": 10, + "ઃ": 54, + "અ": 28, + "આ": 26, + "ઇ": 49, + "ઈ": 30, + "ઉ": 42, + "ઊ": 48, + "ઋ": 57, + "એ": 29, + "ઐ": 58, + "ઓ": 27, + "ક": 9, + "ખ": 33, + "ગ": 32, + "ઘ": 44, + "ચ": 39, + "છ": 23, + "જ": 18, + "ઝ": 51, + "ઞ": 50, + "ટ": 36, + "ઠ": 45, + "ડ": 40, + "ઢ": 52, + "ણ": 22, + "ત": 3, + "થ": 19, + "દ": 25, + "ધ": 34, + "ન": 4, + "પ": 12, + "ફ": 43, + "બ": 31, + "ભ": 35, + "મ": 7, + "ય": 16, + "ર": 5, + "લ": 24, + "ળ": 37, + "વ": 13, + "શ": 21, + "ષ": 41, + "સ": 15, + "હ": 17, + "ા": 1, + "િ": 20, + "ી": 8, + "ુ": 14, + "ૂ": 38, + "ૃ": 46, + "ે": 2, + "ૈ": 53, + "ો": 11, + "ૌ": 55, + "્": 6 +} diff --git a/models/hi_female/__pycache__/extra.cpython-310.pyc b/models/hi_female/__pycache__/extra.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8446dd4ff3487525f86633e06f9ecbc2d761941b Binary files /dev/null and b/models/hi_female/__pycache__/extra.cpython-310.pyc differ diff --git a/models/hi_female/chars.txt b/models/hi_female/chars.txt new file mode 100644 index 0000000000000000000000000000000000000000..55c592df71c5de3197265c7512b6159ddc22bc9b --- /dev/null +++ b/models/hi_female/chars.txt @@ -0,0 +1 @@ +शदऊतसओषमऱढै?ख़ौक़ड़ःिअनठय़ज़फ़्खँे।ंऋउ'हछङझ" ुणऔघयञृएईॆीपचॉॠवगडटइ,बॅूऐफकजलग़आधोथाभढ़ऑ diff --git a/models/hi_female/extra.py b/models/hi_female/extra.py new file mode 100644 index 0000000000000000000000000000000000000000..c7db561351da270a7c3931bfe0afefa7bc6d4853 --- /dev/null +++ b/models/hi_female/extra.py @@ -0,0 +1,787 @@ +from typing import Callable, Dict, List, Union +from dataclasses import asdict, dataclass, field + + +import re +from dataclasses import replace +from typing import Dict +_whitespace_re = re.compile(r"\s+") + +from dataclasses import dataclass, field +from typing import List + +# from TTS.tts.configs.shared_configs import BaseTTSConfig +# from TTS.tts.models.vits import VitsArgs, VitsAudioConfig + +@dataclass +class CharactersConfig(): + + characters_class: str = None + + # using BaseVocabulary + vocab_dict: Dict = None + + # using on BaseCharacters + pad: str = None + eos: str = None + bos: str = None + blank: str = None + characters: str = None + punctuations: str = None + phonemes: str = None + is_unique: bool = True # for backwards compatibility of models trained with char sets with duplicates + is_sorted: bool = True + + +@dataclass +class BaseTTSConfig(): + + # audio: BaseAudioConfig = field(default_factory=BaseAudioConfig) + # phoneme settings + use_phonemes: bool = False + phonemizer: str = None + phoneme_language: str = None + compute_input_seq_cache: bool = False + text_cleaner: str = None + enable_eos_bos_chars: bool = False + test_sentences_file: str = "" + phoneme_cache_path: str = None + # vocabulary parameters + characters: CharactersConfig = None + add_blank: bool = False + # training params + batch_group_size: int = 0 + loss_masking: bool = None + # dataloading + min_audio_len: int = 1 + max_audio_len: int = float("inf") + min_text_len: int = 1 + max_text_len: int = float("inf") + compute_f0: bool = False + compute_energy: bool = False + compute_linear_spec: bool = False + precompute_num_workers: int = 0 + use_noise_augment: bool = False + start_by_longest: bool = False + shuffle: bool = False + drop_last: bool = False + # dataset + datasets: str = None + # optimizer + optimizer: str = "radam" + optimizer_params: dict = None + # scheduler + lr_scheduler: str = None + lr_scheduler_params: dict = field(default_factory=lambda: {}) + # testing + test_sentences: List[str] = field(default_factory=lambda: []) + # evaluation + eval_split_max_size: int = None + eval_split_size: float = 0.01 + # weighted samplers + use_speaker_weighted_sampler: bool = False + speaker_weighted_sampler_alpha: float = 1.0 + use_language_weighted_sampler: bool = False + language_weighted_sampler_alpha: float = 1.0 + use_length_weighted_sampler: bool = False + length_weighted_sampler_alpha: float = 1.0 + + +@dataclass +class VitsAudioConfig(): + fft_size: int = 1024 + sample_rate: int = 22050 + win_length: int = 1024 + hop_length: int = 256 + num_mels: int = 80 + mel_fmin: int = 0 + mel_fmax: int = None + +@dataclass +class VitsArgs(): + num_chars: int = 100 + out_channels: int = 513 + spec_segment_size: int = 32 + hidden_channels: int = 192 + hidden_channels_ffn_text_encoder: int = 768 + num_heads_text_encoder: int = 2 + num_layers_text_encoder: int = 6 + kernel_size_text_encoder: int = 3 + dropout_p_text_encoder: float = 0.1 + dropout_p_duration_predictor: float = 0.5 + kernel_size_posterior_encoder: int = 5 + dilation_rate_posterior_encoder: int = 1 + num_layers_posterior_encoder: int = 16 + kernel_size_flow: int = 5 + dilation_rate_flow: int = 1 + num_layers_flow: int = 4 + resblock_type_decoder: str = "1" + resblock_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [3, 7, 11]) + resblock_dilation_sizes_decoder: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]) + upsample_rates_decoder: List[int] = field(default_factory=lambda: [8, 8, 2, 2]) + upsample_initial_channel_decoder: int = 512 + upsample_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [16, 16, 4, 4]) + periods_multi_period_discriminator: List[int] = field(default_factory=lambda: [2, 3, 5, 7, 11]) + use_sdp: bool = True + noise_scale: float = 1.0 + inference_noise_scale: float = 0.667 + length_scale: float = 1 + noise_scale_dp: float = 1.0 + inference_noise_scale_dp: float = 1.0 + max_inference_len: int = None + init_discriminator: bool = True + use_spectral_norm_disriminator: bool = False + use_speaker_embedding: bool = False + num_speakers: int = 0 + speakers_file: str = None + d_vector_file: List[str] = None + speaker_embedding_channels: int = 256 + use_d_vector_file: bool = False + d_vector_dim: int = 0 + detach_dp_input: bool = True + use_language_embedding: bool = False + embedded_language_dim: int = 4 + num_languages: int = 0 + language_ids_file: str = None + use_speaker_encoder_as_loss: bool = False + speaker_encoder_config_path: str = "" + speaker_encoder_model_path: str = "" + condition_dp_on_speaker: bool = True + freeze_encoder: bool = False + freeze_DP: bool = False + freeze_PE: bool = False + freeze_flow_decoder: bool = False + freeze_waveform_decoder: bool = False + encoder_sample_rate: int = None + interpolate_z: bool = True + reinit_DP: bool = False + reinit_text_encoder: bool = False +@dataclass +class VitsConfig(BaseTTSConfig): + + model: str = "vits" + # model specific params + model_args: VitsArgs = field(default_factory=VitsArgs) + audio: VitsAudioConfig = field(default_factory=VitsAudioConfig) + + # optimizer + grad_clip: List[float] = field(default_factory=lambda: [1000, 1000]) + lr_gen: float = 0.0002 + lr_disc: float = 0.0002 + lr_scheduler_gen: str = "ExponentialLR" + lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + lr_scheduler_disc: str = "ExponentialLR" + lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + scheduler_after_epoch: bool = True + optimizer: str = "AdamW" + optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01}) + + # loss params + kl_loss_alpha: float = 1.0 + disc_loss_alpha: float = 1.0 + gen_loss_alpha: float = 1.0 + feat_loss_alpha: float = 1.0 + mel_loss_alpha: float = 45.0 + dur_loss_alpha: float = 1.0 + speaker_encoder_loss_alpha: float = 1.0 + + # data loader params + return_wav: bool = True + compute_linear_spec: bool = True + + # sampler params + use_weighted_sampler: bool = False # TODO: move it to the base config + weighted_sampler_attrs: dict = field(default_factory=lambda: {}) + weighted_sampler_multipliers: dict = field(default_factory=lambda: {}) + + # overrides + r: int = 1 # DO NOT CHANGE + add_blank: bool = True + + # testing + test_sentences: List[List] = field( + default_factory=lambda: [ + ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."], + ["Be a voice, not an echo."], + ["I'm sorry Dave. I'm afraid I can't do that."], + ["This cake is great. It's so delicious and moist."], + ["Prior to November 22, 1963."], + ] + ) + + # multi-speaker settings + # use speaker embedding layer + num_speakers: int = 0 + use_speaker_embedding: bool = False + speakers_file: str = None + speaker_embedding_channels: int = 256 + language_ids_file: str = None + use_language_embedding: bool = False + + # use d-vectors + use_d_vector_file: bool = False + d_vector_file: List[str] = None + d_vector_dim: int = None + + def __post_init__(self): + pass + # for key, val in self.model_args.items(): + # if hasattr(self, key): + # self[key] = val + + + + + +def parse_symbols(): + return { + "pad": _pad, + "eos": _eos, + "bos": _bos, + "characters": _characters, + "punctuations": _punctuations, + "phonemes": _phonemes, + } + + +# DEFAULT SET OF GRAPHEMES +_pad = "" +_eos = "" +_bos = "" +_blank = "" # TODO: check if we need this alongside with PAD +_characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +_punctuations = "!'(),-.:;? " + + +# DEFAULT SET OF IPA PHONEMES +# Phonemes definition (All IPA characters) +_vowels = "iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻ" +_non_pulmonic_consonants = "ʘɓǀɗǃʄǂɠǁʛ" +_pulmonic_consonants = "pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟ" +_suprasegmentals = "ˈˌːˑ" +_other_symbols = "ʍwɥʜʢʡɕʑɺɧʲ" +_diacrilics = "ɚ˞ɫ" +_phonemes = _vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics + + +class BaseVocabulary: + """Base Vocabulary class. + + This class only needs a vocabulary dictionary without specifying the characters. + + Args: + vocab (Dict): A dictionary of characters and their corresponding indices. + """ + + def __init__(self, vocab: Dict, pad: str = None, blank: str = None, bos: str = None, eos: str = None): + self.vocab = vocab + self.pad = pad + self.blank = blank + self.bos = bos + self.eos = eos + + @property + def pad_id(self) -> int: + """Return the index of the padding character. If the padding character is not specified, return the length + of the vocabulary.""" + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + """Return the index of the blank character. If the blank character is not specified, return the length of + the vocabulary.""" + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def bos_id(self) -> int: + """Return the index of the bos character. If the bos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def eos_id(self) -> int: + """Return the index of the eos character. If the eos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def vocab(self): + """Return the vocabulary dictionary.""" + return self._vocab + + @vocab.setter + def vocab(self, vocab): + """Set the vocabulary dictionary and character mapping dictionaries.""" + self._vocab, self._char_to_id, self._id_to_char = None, None, None + if vocab is not None: + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self._vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self._vocab) # pylint: disable=unnecessary-comprehension + } + + @staticmethod + def init_from_config(config, **kwargs): + """Initialize from the given config.""" + if config.characters is not None and "vocab_dict" in config.characters and config.characters.vocab_dict: + return ( + BaseVocabulary( + config.characters.vocab_dict, + config.characters.pad, + config.characters.blank, + config.characters.bos, + config.characters.eos, + ), + config, + ) + return BaseVocabulary(**kwargs), config + + def to_config(self): + return CharactersConfig( + vocab_dict=self._vocab, + pad=self.pad, + eos=self.eos, + bos=self.bos, + blank=self.blank, + is_unique=False, + is_sorted=False, + ) + + @property + def num_chars(self): + """Return number of tokens in the vocabulary.""" + return len(self._vocab) + + def char_to_id(self, char: str) -> int: + """Map a character to an token ID.""" + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + """Map an token ID to a character.""" + return self._id_to_char[idx] + + +class BaseCharacters: + + + def __init__( + self, + characters: str = None, + punctuations: str = None, + pad: str = None, + eos: str = None, + bos: str = None, + blank: str = None, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + self._characters = characters + self._punctuations = punctuations + self._pad = pad + self._eos = eos + self._bos = bos + self._blank = blank + self.is_unique = is_unique + self.is_sorted = is_sorted + self._create_vocab() + + @property + def pad_id(self) -> int: + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def eos_id(self) -> int: + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def bos_id(self) -> int: + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, characters): + self._characters = characters + self._create_vocab() + + @property + def punctuations(self): + return self._punctuations + + @punctuations.setter + def punctuations(self, punctuations): + self._punctuations = punctuations + self._create_vocab() + + @property + def pad(self): + return self._pad + + @pad.setter + def pad(self, pad): + self._pad = pad + self._create_vocab() + + @property + def eos(self): + return self._eos + + @eos.setter + def eos(self, eos): + self._eos = eos + self._create_vocab() + + @property + def bos(self): + return self._bos + + @bos.setter + def bos(self, bos): + self._bos = bos + self._create_vocab() + + @property + def blank(self): + return self._blank + + @blank.setter + def blank(self, blank): + self._blank = blank + self._create_vocab() + + @property + def vocab(self): + return self._vocab + + @vocab.setter + def vocab(self, vocab): + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self.vocab) # pylint: disable=unnecessary-comprehension + } + + @property + def num_chars(self): + return len(self._vocab) + + def _create_vocab(self): + _vocab = self._characters + if self.is_unique: + _vocab = list(set(_vocab)) + if self.is_sorted: + _vocab = sorted(_vocab) + _vocab = list(_vocab) + _vocab = [self._blank] + _vocab if self._blank is not None and len(self._blank) > 0 else _vocab + _vocab = [self._bos] + _vocab if self._bos is not None and len(self._bos) > 0 else _vocab + _vocab = [self._eos] + _vocab if self._eos is not None and len(self._eos) > 0 else _vocab + _vocab = [self._pad] + _vocab if self._pad is not None and len(self._pad) > 0 else _vocab + self.vocab = _vocab + list(self._punctuations) + if self.is_unique: + duplicates = {x for x in self.vocab if self.vocab.count(x) > 1} + assert ( + len(self.vocab) == len(self._char_to_id) == len(self._id_to_char) + ), f" [!] There are duplicate characters in the character set. {duplicates}" + + def char_to_id(self, char: str) -> int: + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + return self._id_to_char[idx] + + def print_log(self, level: int = 0): + """ + Prints the vocabulary in a nice format. + """ + indent = "\t" * level + print(f"{indent}| > Characters: {self._characters}") + print(f"{indent}| > Punctuations: {self._punctuations}") + print(f"{indent}| > Pad: {self._pad}") + print(f"{indent}| > EOS: {self._eos}") + print(f"{indent}| > BOS: {self._bos}") + print(f"{indent}| > Blank: {self._blank}") + print(f"{indent}| > Vocab: {self.vocab}") + print(f"{indent}| > Num chars: {self.num_chars}") + + @staticmethod + def init_from_config(config: "Coqpit"): # pylint: disable=unused-argument + """Init your character class from a config. + + Implement this method for your subclass. + """ + # use character set from config + if config.characters is not None: + return BaseCharacters(**config.characters), config + # return default character set + characters = BaseCharacters() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=self._eos, + bos=self._bos, + blank=self._blank, + is_unique=self.is_unique, + is_sorted=self.is_sorted, + ) + + +class IPAPhonemes(BaseCharacters): + + + def __init__( + self, + characters: str = _phonemes, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a IPAPhonemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + # band-aid for compatibility with old models + if "characters" in config and config.characters is not None: + if "phonemes" in config.characters and config.characters.phonemes is not None: + config.characters["characters"] = config.characters["phonemes"] + return ( + IPAPhonemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + # use character set from config + if config.characters is not None: + return IPAPhonemes(**config.characters), config + # return default character set + characters = IPAPhonemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +class Graphemes(BaseCharacters): + + + def __init__( + self, + characters: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a Graphemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + if config.characters is not None: + # band-aid for compatibility with old models + if "phonemes" in config.characters: + return ( + Graphemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + return Graphemes(**config.characters), config + characters = Graphemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +if __name__ == "__main__": + gr = Graphemes() + ph = IPAPhonemes() + gr.print_log() + ph.print_log() + + +class VitsCharacters(BaseCharacters): + """Characters class for VITs model for compatibility with pre-trained models""" + + def __init__( + self, + graphemes: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + ipa_characters: str = _phonemes, + ) -> None: + if ipa_characters is not None: + graphemes += ipa_characters + super().__init__(graphemes, punctuations, pad, None, None, "", is_unique=False, is_sorted=True) + + def _create_vocab(self): + self._vocab = [self._pad] + list(self._punctuations) + list(self._characters) + [self._blank] + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + # pylint: disable=unnecessary-comprehension + self._id_to_char = {idx: char for idx, char in enumerate(self.vocab)} + + @staticmethod + def init_from_config(config): + _pad = config.characters.pad + _punctuations = config.characters.punctuations + _letters = config.characters.characters + _letters_ipa = config.characters.phonemes + return ( + VitsCharacters(graphemes=_letters, ipa_characters=_letters_ipa, punctuations=_punctuations, pad=_pad), + config, + ) + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=None, + bos=None, + blank=self._blank, + is_unique=False, + is_sorted=True, + ) + +class TTSTokenizer: + def __init__( + self, + text_cleaner: Callable = None, + characters: "BaseCharacters" = None, + ): + self.text_cleaner = text_cleaner + self.characters = characters + self.not_found_characters = [] + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, new_characters): + self._characters = new_characters + self.pad_id = self.characters.char_to_id(self.characters.pad) if self.characters.pad else None + self.blank_id = self.characters.char_to_id(self.characters.blank) if self.characters.blank else None + + def encode(self, text: str) -> List[int]: + """Encodes a string of text as a sequence of IDs.""" + token_ids = [] + for char in text: + try: + idx = self.characters.char_to_id(char) + token_ids.append(idx) + except KeyError: + # discard but store not found characters + if char not in self.not_found_characters: + self.not_found_characters.append(char) + print(text) + print(f" [!] Character {repr(char)} not found in the vocabulary. Discarding it.") + return token_ids + + def text_to_ids(self, text: str, language: str = None) -> List[int]: # pylint: disable=unused-argument + text = self.text_cleaner(text) + text = self.encode(text) + text = self.intersperse_blank_char(text, True) + return text + + def pad_with_bos_eos(self, char_sequence: List[str]): + """Pads a sequence with the special BOS and EOS characters.""" + return [self.characters.bos_id] + list(char_sequence) + [self.characters.eos_id] + + def intersperse_blank_char(self, char_sequence: List[str], use_blank_char: bool = False): + """Intersperses the blank character between characters in a sequence. + + Use the ```blank``` character if defined else use the ```pad``` character. + """ + char_to_use = self.characters.blank_id if use_blank_char else self.characters.pad + result = [char_to_use] * (len(char_sequence) * 2 + 1) + result[1::2] = char_sequence + return result + + @staticmethod + def init_from_config(config: "Coqpit", characters: "BaseCharacters" = None): + text_cleaner = multilingual_cleaners + CharactersClass = VitsCharacters + characters, new_config = CharactersClass.init_from_config(config) + # new_config.characters.characters_class = get_import_path(characters) + new_config.characters.characters_class = VitsCharacters + return ( + TTSTokenizer(text_cleaner, characters),new_config) + + +def multilingual_cleaners(text): + """Pipeline for multilingual text""" + text = lowercase(text) + text = replace_symbols(text, lang=None) + text = remove_aux_symbols(text) + text = collapse_whitespace(text) + return text + +def lowercase(text): + return text.lower() + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text).strip() + +def replace_symbols(text, lang="en"): + + text = text.replace(";", ",") + text = text.replace("-", " ") if lang != "ca" else text.replace("-", "") + text = text.replace(":", ",") + if lang == "en": + text = text.replace("&", " and ") + elif lang == "fr": + text = text.replace("&", " et ") + elif lang == "pt": + text = text.replace("&", " e ") + elif lang == "ca": + text = text.replace("&", " i ") + text = text.replace("'", "") + return text + +def remove_aux_symbols(text): + text = re.sub(r"[\<\>\(\)\[\]\"]+", "", text) + return text \ No newline at end of file diff --git a/models/hi_female/hi_female_vits_30hrs.pt b/models/hi_female/hi_female_vits_30hrs.pt new file mode 100644 index 0000000000000000000000000000000000000000..0937c580c6c880e77982b30a971bc9c7fbf3bf8d --- /dev/null +++ b/models/hi_female/hi_female_vits_30hrs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2bcfb47f599b36e7cbfec27142604c366e538c17e89980a40519291f92a46327 +size 333261446 diff --git a/models/hi_female/jit_infer.py b/models/hi_female/jit_infer.py new file mode 100644 index 0000000000000000000000000000000000000000..99fa30c61140d20bc3eaef4ff41fdf8f6e50de34 --- /dev/null +++ b/models/hi_female/jit_infer.py @@ -0,0 +1,32 @@ +import os +from extra import TTSTokenizer, VitsConfig, CharactersConfig, VitsCharacters +import torch +import numpy as np + +#ch female +with open("chars.txt", 'r') as f: + letters = f.read().strip('\n') +model="hi_female_vits_30hrs.pt" +text = "फिल्म गर्दिश में अमरीश पुरी के साथ जैकी श्रॉफ, ऐश्वर्या, डिंपल कपाड़िया" + +config = VitsConfig( + text_cleaner="multilingual_cleaners", + characters=CharactersConfig( + characters_class=VitsCharacters, + pad="", + eos="", + bos="", + blank="", + characters=letters, + punctuations="!¡'(),-.:;¿? ", + phonemes=None) + ) +tokenizer, config = TTSTokenizer.init_from_config(config) + +x = tokenizer.text_to_ids(text) +x = torch.from_numpy(np.array(x)).unsqueeze(0) +net = torch.jit.load(model) +with torch.no_grad(): + out2 = net(x) +import soundfile as sf +sf.write("jit.wav", out2.squeeze().cpu().numpy(), 22050) \ No newline at end of file diff --git a/models/hi_male/chars.txt b/models/hi_male/chars.txt new file mode 100644 index 0000000000000000000000000000000000000000..5600167b081855cecb9aaf12b14f4fd6fc3ddee6 --- /dev/null +++ b/models/hi_male/chars.txt @@ -0,0 +1 @@ +शदऊतओसषमऱढै?ख़ौक़ड़ःिअनठय़ज़फ़्खँे।ंऋउ'हछङझ" ुणऔयघञृएईॆीपचॉॠवगडटइ,बॅूऐफजकलग़आधोथाभढ़ऑ diff --git a/models/hi_male/extra.py b/models/hi_male/extra.py new file mode 100644 index 0000000000000000000000000000000000000000..c7db561351da270a7c3931bfe0afefa7bc6d4853 --- /dev/null +++ b/models/hi_male/extra.py @@ -0,0 +1,787 @@ +from typing import Callable, Dict, List, Union +from dataclasses import asdict, dataclass, field + + +import re +from dataclasses import replace +from typing import Dict +_whitespace_re = re.compile(r"\s+") + +from dataclasses import dataclass, field +from typing import List + +# from TTS.tts.configs.shared_configs import BaseTTSConfig +# from TTS.tts.models.vits import VitsArgs, VitsAudioConfig + +@dataclass +class CharactersConfig(): + + characters_class: str = None + + # using BaseVocabulary + vocab_dict: Dict = None + + # using on BaseCharacters + pad: str = None + eos: str = None + bos: str = None + blank: str = None + characters: str = None + punctuations: str = None + phonemes: str = None + is_unique: bool = True # for backwards compatibility of models trained with char sets with duplicates + is_sorted: bool = True + + +@dataclass +class BaseTTSConfig(): + + # audio: BaseAudioConfig = field(default_factory=BaseAudioConfig) + # phoneme settings + use_phonemes: bool = False + phonemizer: str = None + phoneme_language: str = None + compute_input_seq_cache: bool = False + text_cleaner: str = None + enable_eos_bos_chars: bool = False + test_sentences_file: str = "" + phoneme_cache_path: str = None + # vocabulary parameters + characters: CharactersConfig = None + add_blank: bool = False + # training params + batch_group_size: int = 0 + loss_masking: bool = None + # dataloading + min_audio_len: int = 1 + max_audio_len: int = float("inf") + min_text_len: int = 1 + max_text_len: int = float("inf") + compute_f0: bool = False + compute_energy: bool = False + compute_linear_spec: bool = False + precompute_num_workers: int = 0 + use_noise_augment: bool = False + start_by_longest: bool = False + shuffle: bool = False + drop_last: bool = False + # dataset + datasets: str = None + # optimizer + optimizer: str = "radam" + optimizer_params: dict = None + # scheduler + lr_scheduler: str = None + lr_scheduler_params: dict = field(default_factory=lambda: {}) + # testing + test_sentences: List[str] = field(default_factory=lambda: []) + # evaluation + eval_split_max_size: int = None + eval_split_size: float = 0.01 + # weighted samplers + use_speaker_weighted_sampler: bool = False + speaker_weighted_sampler_alpha: float = 1.0 + use_language_weighted_sampler: bool = False + language_weighted_sampler_alpha: float = 1.0 + use_length_weighted_sampler: bool = False + length_weighted_sampler_alpha: float = 1.0 + + +@dataclass +class VitsAudioConfig(): + fft_size: int = 1024 + sample_rate: int = 22050 + win_length: int = 1024 + hop_length: int = 256 + num_mels: int = 80 + mel_fmin: int = 0 + mel_fmax: int = None + +@dataclass +class VitsArgs(): + num_chars: int = 100 + out_channels: int = 513 + spec_segment_size: int = 32 + hidden_channels: int = 192 + hidden_channels_ffn_text_encoder: int = 768 + num_heads_text_encoder: int = 2 + num_layers_text_encoder: int = 6 + kernel_size_text_encoder: int = 3 + dropout_p_text_encoder: float = 0.1 + dropout_p_duration_predictor: float = 0.5 + kernel_size_posterior_encoder: int = 5 + dilation_rate_posterior_encoder: int = 1 + num_layers_posterior_encoder: int = 16 + kernel_size_flow: int = 5 + dilation_rate_flow: int = 1 + num_layers_flow: int = 4 + resblock_type_decoder: str = "1" + resblock_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [3, 7, 11]) + resblock_dilation_sizes_decoder: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]) + upsample_rates_decoder: List[int] = field(default_factory=lambda: [8, 8, 2, 2]) + upsample_initial_channel_decoder: int = 512 + upsample_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [16, 16, 4, 4]) + periods_multi_period_discriminator: List[int] = field(default_factory=lambda: [2, 3, 5, 7, 11]) + use_sdp: bool = True + noise_scale: float = 1.0 + inference_noise_scale: float = 0.667 + length_scale: float = 1 + noise_scale_dp: float = 1.0 + inference_noise_scale_dp: float = 1.0 + max_inference_len: int = None + init_discriminator: bool = True + use_spectral_norm_disriminator: bool = False + use_speaker_embedding: bool = False + num_speakers: int = 0 + speakers_file: str = None + d_vector_file: List[str] = None + speaker_embedding_channels: int = 256 + use_d_vector_file: bool = False + d_vector_dim: int = 0 + detach_dp_input: bool = True + use_language_embedding: bool = False + embedded_language_dim: int = 4 + num_languages: int = 0 + language_ids_file: str = None + use_speaker_encoder_as_loss: bool = False + speaker_encoder_config_path: str = "" + speaker_encoder_model_path: str = "" + condition_dp_on_speaker: bool = True + freeze_encoder: bool = False + freeze_DP: bool = False + freeze_PE: bool = False + freeze_flow_decoder: bool = False + freeze_waveform_decoder: bool = False + encoder_sample_rate: int = None + interpolate_z: bool = True + reinit_DP: bool = False + reinit_text_encoder: bool = False +@dataclass +class VitsConfig(BaseTTSConfig): + + model: str = "vits" + # model specific params + model_args: VitsArgs = field(default_factory=VitsArgs) + audio: VitsAudioConfig = field(default_factory=VitsAudioConfig) + + # optimizer + grad_clip: List[float] = field(default_factory=lambda: [1000, 1000]) + lr_gen: float = 0.0002 + lr_disc: float = 0.0002 + lr_scheduler_gen: str = "ExponentialLR" + lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + lr_scheduler_disc: str = "ExponentialLR" + lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + scheduler_after_epoch: bool = True + optimizer: str = "AdamW" + optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01}) + + # loss params + kl_loss_alpha: float = 1.0 + disc_loss_alpha: float = 1.0 + gen_loss_alpha: float = 1.0 + feat_loss_alpha: float = 1.0 + mel_loss_alpha: float = 45.0 + dur_loss_alpha: float = 1.0 + speaker_encoder_loss_alpha: float = 1.0 + + # data loader params + return_wav: bool = True + compute_linear_spec: bool = True + + # sampler params + use_weighted_sampler: bool = False # TODO: move it to the base config + weighted_sampler_attrs: dict = field(default_factory=lambda: {}) + weighted_sampler_multipliers: dict = field(default_factory=lambda: {}) + + # overrides + r: int = 1 # DO NOT CHANGE + add_blank: bool = True + + # testing + test_sentences: List[List] = field( + default_factory=lambda: [ + ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."], + ["Be a voice, not an echo."], + ["I'm sorry Dave. I'm afraid I can't do that."], + ["This cake is great. It's so delicious and moist."], + ["Prior to November 22, 1963."], + ] + ) + + # multi-speaker settings + # use speaker embedding layer + num_speakers: int = 0 + use_speaker_embedding: bool = False + speakers_file: str = None + speaker_embedding_channels: int = 256 + language_ids_file: str = None + use_language_embedding: bool = False + + # use d-vectors + use_d_vector_file: bool = False + d_vector_file: List[str] = None + d_vector_dim: int = None + + def __post_init__(self): + pass + # for key, val in self.model_args.items(): + # if hasattr(self, key): + # self[key] = val + + + + + +def parse_symbols(): + return { + "pad": _pad, + "eos": _eos, + "bos": _bos, + "characters": _characters, + "punctuations": _punctuations, + "phonemes": _phonemes, + } + + +# DEFAULT SET OF GRAPHEMES +_pad = "" +_eos = "" +_bos = "" +_blank = "" # TODO: check if we need this alongside with PAD +_characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +_punctuations = "!'(),-.:;? " + + +# DEFAULT SET OF IPA PHONEMES +# Phonemes definition (All IPA characters) +_vowels = "iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻ" +_non_pulmonic_consonants = "ʘɓǀɗǃʄǂɠǁʛ" +_pulmonic_consonants = "pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟ" +_suprasegmentals = "ˈˌːˑ" +_other_symbols = "ʍwɥʜʢʡɕʑɺɧʲ" +_diacrilics = "ɚ˞ɫ" +_phonemes = _vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics + + +class BaseVocabulary: + """Base Vocabulary class. + + This class only needs a vocabulary dictionary without specifying the characters. + + Args: + vocab (Dict): A dictionary of characters and their corresponding indices. + """ + + def __init__(self, vocab: Dict, pad: str = None, blank: str = None, bos: str = None, eos: str = None): + self.vocab = vocab + self.pad = pad + self.blank = blank + self.bos = bos + self.eos = eos + + @property + def pad_id(self) -> int: + """Return the index of the padding character. If the padding character is not specified, return the length + of the vocabulary.""" + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + """Return the index of the blank character. If the blank character is not specified, return the length of + the vocabulary.""" + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def bos_id(self) -> int: + """Return the index of the bos character. If the bos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def eos_id(self) -> int: + """Return the index of the eos character. If the eos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def vocab(self): + """Return the vocabulary dictionary.""" + return self._vocab + + @vocab.setter + def vocab(self, vocab): + """Set the vocabulary dictionary and character mapping dictionaries.""" + self._vocab, self._char_to_id, self._id_to_char = None, None, None + if vocab is not None: + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self._vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self._vocab) # pylint: disable=unnecessary-comprehension + } + + @staticmethod + def init_from_config(config, **kwargs): + """Initialize from the given config.""" + if config.characters is not None and "vocab_dict" in config.characters and config.characters.vocab_dict: + return ( + BaseVocabulary( + config.characters.vocab_dict, + config.characters.pad, + config.characters.blank, + config.characters.bos, + config.characters.eos, + ), + config, + ) + return BaseVocabulary(**kwargs), config + + def to_config(self): + return CharactersConfig( + vocab_dict=self._vocab, + pad=self.pad, + eos=self.eos, + bos=self.bos, + blank=self.blank, + is_unique=False, + is_sorted=False, + ) + + @property + def num_chars(self): + """Return number of tokens in the vocabulary.""" + return len(self._vocab) + + def char_to_id(self, char: str) -> int: + """Map a character to an token ID.""" + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + """Map an token ID to a character.""" + return self._id_to_char[idx] + + +class BaseCharacters: + + + def __init__( + self, + characters: str = None, + punctuations: str = None, + pad: str = None, + eos: str = None, + bos: str = None, + blank: str = None, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + self._characters = characters + self._punctuations = punctuations + self._pad = pad + self._eos = eos + self._bos = bos + self._blank = blank + self.is_unique = is_unique + self.is_sorted = is_sorted + self._create_vocab() + + @property + def pad_id(self) -> int: + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def eos_id(self) -> int: + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def bos_id(self) -> int: + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, characters): + self._characters = characters + self._create_vocab() + + @property + def punctuations(self): + return self._punctuations + + @punctuations.setter + def punctuations(self, punctuations): + self._punctuations = punctuations + self._create_vocab() + + @property + def pad(self): + return self._pad + + @pad.setter + def pad(self, pad): + self._pad = pad + self._create_vocab() + + @property + def eos(self): + return self._eos + + @eos.setter + def eos(self, eos): + self._eos = eos + self._create_vocab() + + @property + def bos(self): + return self._bos + + @bos.setter + def bos(self, bos): + self._bos = bos + self._create_vocab() + + @property + def blank(self): + return self._blank + + @blank.setter + def blank(self, blank): + self._blank = blank + self._create_vocab() + + @property + def vocab(self): + return self._vocab + + @vocab.setter + def vocab(self, vocab): + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self.vocab) # pylint: disable=unnecessary-comprehension + } + + @property + def num_chars(self): + return len(self._vocab) + + def _create_vocab(self): + _vocab = self._characters + if self.is_unique: + _vocab = list(set(_vocab)) + if self.is_sorted: + _vocab = sorted(_vocab) + _vocab = list(_vocab) + _vocab = [self._blank] + _vocab if self._blank is not None and len(self._blank) > 0 else _vocab + _vocab = [self._bos] + _vocab if self._bos is not None and len(self._bos) > 0 else _vocab + _vocab = [self._eos] + _vocab if self._eos is not None and len(self._eos) > 0 else _vocab + _vocab = [self._pad] + _vocab if self._pad is not None and len(self._pad) > 0 else _vocab + self.vocab = _vocab + list(self._punctuations) + if self.is_unique: + duplicates = {x for x in self.vocab if self.vocab.count(x) > 1} + assert ( + len(self.vocab) == len(self._char_to_id) == len(self._id_to_char) + ), f" [!] There are duplicate characters in the character set. {duplicates}" + + def char_to_id(self, char: str) -> int: + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + return self._id_to_char[idx] + + def print_log(self, level: int = 0): + """ + Prints the vocabulary in a nice format. + """ + indent = "\t" * level + print(f"{indent}| > Characters: {self._characters}") + print(f"{indent}| > Punctuations: {self._punctuations}") + print(f"{indent}| > Pad: {self._pad}") + print(f"{indent}| > EOS: {self._eos}") + print(f"{indent}| > BOS: {self._bos}") + print(f"{indent}| > Blank: {self._blank}") + print(f"{indent}| > Vocab: {self.vocab}") + print(f"{indent}| > Num chars: {self.num_chars}") + + @staticmethod + def init_from_config(config: "Coqpit"): # pylint: disable=unused-argument + """Init your character class from a config. + + Implement this method for your subclass. + """ + # use character set from config + if config.characters is not None: + return BaseCharacters(**config.characters), config + # return default character set + characters = BaseCharacters() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=self._eos, + bos=self._bos, + blank=self._blank, + is_unique=self.is_unique, + is_sorted=self.is_sorted, + ) + + +class IPAPhonemes(BaseCharacters): + + + def __init__( + self, + characters: str = _phonemes, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a IPAPhonemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + # band-aid for compatibility with old models + if "characters" in config and config.characters is not None: + if "phonemes" in config.characters and config.characters.phonemes is not None: + config.characters["characters"] = config.characters["phonemes"] + return ( + IPAPhonemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + # use character set from config + if config.characters is not None: + return IPAPhonemes(**config.characters), config + # return default character set + characters = IPAPhonemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +class Graphemes(BaseCharacters): + + + def __init__( + self, + characters: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a Graphemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + if config.characters is not None: + # band-aid for compatibility with old models + if "phonemes" in config.characters: + return ( + Graphemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + return Graphemes(**config.characters), config + characters = Graphemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +if __name__ == "__main__": + gr = Graphemes() + ph = IPAPhonemes() + gr.print_log() + ph.print_log() + + +class VitsCharacters(BaseCharacters): + """Characters class for VITs model for compatibility with pre-trained models""" + + def __init__( + self, + graphemes: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + ipa_characters: str = _phonemes, + ) -> None: + if ipa_characters is not None: + graphemes += ipa_characters + super().__init__(graphemes, punctuations, pad, None, None, "", is_unique=False, is_sorted=True) + + def _create_vocab(self): + self._vocab = [self._pad] + list(self._punctuations) + list(self._characters) + [self._blank] + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + # pylint: disable=unnecessary-comprehension + self._id_to_char = {idx: char for idx, char in enumerate(self.vocab)} + + @staticmethod + def init_from_config(config): + _pad = config.characters.pad + _punctuations = config.characters.punctuations + _letters = config.characters.characters + _letters_ipa = config.characters.phonemes + return ( + VitsCharacters(graphemes=_letters, ipa_characters=_letters_ipa, punctuations=_punctuations, pad=_pad), + config, + ) + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=None, + bos=None, + blank=self._blank, + is_unique=False, + is_sorted=True, + ) + +class TTSTokenizer: + def __init__( + self, + text_cleaner: Callable = None, + characters: "BaseCharacters" = None, + ): + self.text_cleaner = text_cleaner + self.characters = characters + self.not_found_characters = [] + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, new_characters): + self._characters = new_characters + self.pad_id = self.characters.char_to_id(self.characters.pad) if self.characters.pad else None + self.blank_id = self.characters.char_to_id(self.characters.blank) if self.characters.blank else None + + def encode(self, text: str) -> List[int]: + """Encodes a string of text as a sequence of IDs.""" + token_ids = [] + for char in text: + try: + idx = self.characters.char_to_id(char) + token_ids.append(idx) + except KeyError: + # discard but store not found characters + if char not in self.not_found_characters: + self.not_found_characters.append(char) + print(text) + print(f" [!] Character {repr(char)} not found in the vocabulary. Discarding it.") + return token_ids + + def text_to_ids(self, text: str, language: str = None) -> List[int]: # pylint: disable=unused-argument + text = self.text_cleaner(text) + text = self.encode(text) + text = self.intersperse_blank_char(text, True) + return text + + def pad_with_bos_eos(self, char_sequence: List[str]): + """Pads a sequence with the special BOS and EOS characters.""" + return [self.characters.bos_id] + list(char_sequence) + [self.characters.eos_id] + + def intersperse_blank_char(self, char_sequence: List[str], use_blank_char: bool = False): + """Intersperses the blank character between characters in a sequence. + + Use the ```blank``` character if defined else use the ```pad``` character. + """ + char_to_use = self.characters.blank_id if use_blank_char else self.characters.pad + result = [char_to_use] * (len(char_sequence) * 2 + 1) + result[1::2] = char_sequence + return result + + @staticmethod + def init_from_config(config: "Coqpit", characters: "BaseCharacters" = None): + text_cleaner = multilingual_cleaners + CharactersClass = VitsCharacters + characters, new_config = CharactersClass.init_from_config(config) + # new_config.characters.characters_class = get_import_path(characters) + new_config.characters.characters_class = VitsCharacters + return ( + TTSTokenizer(text_cleaner, characters),new_config) + + +def multilingual_cleaners(text): + """Pipeline for multilingual text""" + text = lowercase(text) + text = replace_symbols(text, lang=None) + text = remove_aux_symbols(text) + text = collapse_whitespace(text) + return text + +def lowercase(text): + return text.lower() + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text).strip() + +def replace_symbols(text, lang="en"): + + text = text.replace(";", ",") + text = text.replace("-", " ") if lang != "ca" else text.replace("-", "") + text = text.replace(":", ",") + if lang == "en": + text = text.replace("&", " and ") + elif lang == "fr": + text = text.replace("&", " et ") + elif lang == "pt": + text = text.replace("&", " e ") + elif lang == "ca": + text = text.replace("&", " i ") + text = text.replace("'", "") + return text + +def remove_aux_symbols(text): + text = re.sub(r"[\<\>\(\)\[\]\"]+", "", text) + return text \ No newline at end of file diff --git a/models/hi_male/hi_male_vits_30hrs.pt b/models/hi_male/hi_male_vits_30hrs.pt new file mode 100644 index 0000000000000000000000000000000000000000..4cf441fbed06adc5e914b34d605ff52b5061ced7 --- /dev/null +++ b/models/hi_male/hi_male_vits_30hrs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eb36eca2d90214662f1647e83eb6979ead93b72f269606c6411f52959acf77a8 +size 333256012 diff --git a/models/hi_male/jit_infer.py b/models/hi_male/jit_infer.py new file mode 100644 index 0000000000000000000000000000000000000000..52d2c95b6e2bc4464b1cd3e6698d40d515b100a8 --- /dev/null +++ b/models/hi_male/jit_infer.py @@ -0,0 +1,32 @@ +import os +from extra import TTSTokenizer, VitsConfig, CharactersConfig, VitsCharacters +import torch +import numpy as np + +#ch female +with open("chars.txt", 'r') as f: + letters = f.read().strip('\n') +model="hi_male_vits_30hrs.pt" +text = "फिल्म गर्दिश में अमरीश पुरी के साथ जैकी श्रॉफ, ऐश्वर्या, डिंपल कपाड़िया" + +config = VitsConfig( + text_cleaner="multilingual_cleaners", + characters=CharactersConfig( + characters_class=VitsCharacters, + pad="", + eos="", + bos="", + blank="", + characters=letters, + punctuations="!¡'(),-.:;¿? ", + phonemes=None) + ) +tokenizer, config = TTSTokenizer.init_from_config(config) + +x = tokenizer.text_to_ids(text) +x = torch.from_numpy(np.array(x)).unsqueeze(0) +net = torch.jit.load(model) +with torch.no_grad(): + out2 = net(x) +import soundfile as sf +sf.write("jit.wav", out2.squeeze().cpu().numpy(), 22050) \ No newline at end of file diff --git a/models/hne_female/.gitattributes b/models/hne_female/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b --- /dev/null +++ b/models/hne_female/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/models/hne_female/README.md b/models/hne_female/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b187bb7e7d837a367ccd0862441947ad412c77f7 --- /dev/null +++ b/models/hne_female/README.md @@ -0,0 +1,3 @@ +--- +license: cc-by-4.0 +--- diff --git a/models/hne_female/ch_female_vits_30hrs.pt b/models/hne_female/ch_female_vits_30hrs.pt new file mode 100644 index 0000000000000000000000000000000000000000..95af5a39c3696e8d46e320a0539e544a71e4342a --- /dev/null +++ b/models/hne_female/ch_female_vits_30hrs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c3393916262f03807d8338aa8dce79379582c71a0ada346457e36ea6f72a6635 +size 333255366 diff --git a/models/hne_female/chars.txt b/models/hne_female/chars.txt new file mode 100644 index 0000000000000000000000000000000000000000..e313e83b700327e5ddc2798352b8a6ad642a697c --- /dev/null +++ b/models/hne_female/chars.txt @@ -0,0 +1 @@ +खछगचऊुलशौढ़इणज़झैठढजफ़औ्ड़फूेानटॅयव़ऋदप.थअँऑआघहतषरसभउञडएईऐक़ िओ?धी,ॉंख़कोबमृ diff --git a/models/hne_female/extra.py b/models/hne_female/extra.py new file mode 100644 index 0000000000000000000000000000000000000000..c7db561351da270a7c3931bfe0afefa7bc6d4853 --- /dev/null +++ b/models/hne_female/extra.py @@ -0,0 +1,787 @@ +from typing import Callable, Dict, List, Union +from dataclasses import asdict, dataclass, field + + +import re +from dataclasses import replace +from typing import Dict +_whitespace_re = re.compile(r"\s+") + +from dataclasses import dataclass, field +from typing import List + +# from TTS.tts.configs.shared_configs import BaseTTSConfig +# from TTS.tts.models.vits import VitsArgs, VitsAudioConfig + +@dataclass +class CharactersConfig(): + + characters_class: str = None + + # using BaseVocabulary + vocab_dict: Dict = None + + # using on BaseCharacters + pad: str = None + eos: str = None + bos: str = None + blank: str = None + characters: str = None + punctuations: str = None + phonemes: str = None + is_unique: bool = True # for backwards compatibility of models trained with char sets with duplicates + is_sorted: bool = True + + +@dataclass +class BaseTTSConfig(): + + # audio: BaseAudioConfig = field(default_factory=BaseAudioConfig) + # phoneme settings + use_phonemes: bool = False + phonemizer: str = None + phoneme_language: str = None + compute_input_seq_cache: bool = False + text_cleaner: str = None + enable_eos_bos_chars: bool = False + test_sentences_file: str = "" + phoneme_cache_path: str = None + # vocabulary parameters + characters: CharactersConfig = None + add_blank: bool = False + # training params + batch_group_size: int = 0 + loss_masking: bool = None + # dataloading + min_audio_len: int = 1 + max_audio_len: int = float("inf") + min_text_len: int = 1 + max_text_len: int = float("inf") + compute_f0: bool = False + compute_energy: bool = False + compute_linear_spec: bool = False + precompute_num_workers: int = 0 + use_noise_augment: bool = False + start_by_longest: bool = False + shuffle: bool = False + drop_last: bool = False + # dataset + datasets: str = None + # optimizer + optimizer: str = "radam" + optimizer_params: dict = None + # scheduler + lr_scheduler: str = None + lr_scheduler_params: dict = field(default_factory=lambda: {}) + # testing + test_sentences: List[str] = field(default_factory=lambda: []) + # evaluation + eval_split_max_size: int = None + eval_split_size: float = 0.01 + # weighted samplers + use_speaker_weighted_sampler: bool = False + speaker_weighted_sampler_alpha: float = 1.0 + use_language_weighted_sampler: bool = False + language_weighted_sampler_alpha: float = 1.0 + use_length_weighted_sampler: bool = False + length_weighted_sampler_alpha: float = 1.0 + + +@dataclass +class VitsAudioConfig(): + fft_size: int = 1024 + sample_rate: int = 22050 + win_length: int = 1024 + hop_length: int = 256 + num_mels: int = 80 + mel_fmin: int = 0 + mel_fmax: int = None + +@dataclass +class VitsArgs(): + num_chars: int = 100 + out_channels: int = 513 + spec_segment_size: int = 32 + hidden_channels: int = 192 + hidden_channels_ffn_text_encoder: int = 768 + num_heads_text_encoder: int = 2 + num_layers_text_encoder: int = 6 + kernel_size_text_encoder: int = 3 + dropout_p_text_encoder: float = 0.1 + dropout_p_duration_predictor: float = 0.5 + kernel_size_posterior_encoder: int = 5 + dilation_rate_posterior_encoder: int = 1 + num_layers_posterior_encoder: int = 16 + kernel_size_flow: int = 5 + dilation_rate_flow: int = 1 + num_layers_flow: int = 4 + resblock_type_decoder: str = "1" + resblock_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [3, 7, 11]) + resblock_dilation_sizes_decoder: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]) + upsample_rates_decoder: List[int] = field(default_factory=lambda: [8, 8, 2, 2]) + upsample_initial_channel_decoder: int = 512 + upsample_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [16, 16, 4, 4]) + periods_multi_period_discriminator: List[int] = field(default_factory=lambda: [2, 3, 5, 7, 11]) + use_sdp: bool = True + noise_scale: float = 1.0 + inference_noise_scale: float = 0.667 + length_scale: float = 1 + noise_scale_dp: float = 1.0 + inference_noise_scale_dp: float = 1.0 + max_inference_len: int = None + init_discriminator: bool = True + use_spectral_norm_disriminator: bool = False + use_speaker_embedding: bool = False + num_speakers: int = 0 + speakers_file: str = None + d_vector_file: List[str] = None + speaker_embedding_channels: int = 256 + use_d_vector_file: bool = False + d_vector_dim: int = 0 + detach_dp_input: bool = True + use_language_embedding: bool = False + embedded_language_dim: int = 4 + num_languages: int = 0 + language_ids_file: str = None + use_speaker_encoder_as_loss: bool = False + speaker_encoder_config_path: str = "" + speaker_encoder_model_path: str = "" + condition_dp_on_speaker: bool = True + freeze_encoder: bool = False + freeze_DP: bool = False + freeze_PE: bool = False + freeze_flow_decoder: bool = False + freeze_waveform_decoder: bool = False + encoder_sample_rate: int = None + interpolate_z: bool = True + reinit_DP: bool = False + reinit_text_encoder: bool = False +@dataclass +class VitsConfig(BaseTTSConfig): + + model: str = "vits" + # model specific params + model_args: VitsArgs = field(default_factory=VitsArgs) + audio: VitsAudioConfig = field(default_factory=VitsAudioConfig) + + # optimizer + grad_clip: List[float] = field(default_factory=lambda: [1000, 1000]) + lr_gen: float = 0.0002 + lr_disc: float = 0.0002 + lr_scheduler_gen: str = "ExponentialLR" + lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + lr_scheduler_disc: str = "ExponentialLR" + lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + scheduler_after_epoch: bool = True + optimizer: str = "AdamW" + optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01}) + + # loss params + kl_loss_alpha: float = 1.0 + disc_loss_alpha: float = 1.0 + gen_loss_alpha: float = 1.0 + feat_loss_alpha: float = 1.0 + mel_loss_alpha: float = 45.0 + dur_loss_alpha: float = 1.0 + speaker_encoder_loss_alpha: float = 1.0 + + # data loader params + return_wav: bool = True + compute_linear_spec: bool = True + + # sampler params + use_weighted_sampler: bool = False # TODO: move it to the base config + weighted_sampler_attrs: dict = field(default_factory=lambda: {}) + weighted_sampler_multipliers: dict = field(default_factory=lambda: {}) + + # overrides + r: int = 1 # DO NOT CHANGE + add_blank: bool = True + + # testing + test_sentences: List[List] = field( + default_factory=lambda: [ + ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."], + ["Be a voice, not an echo."], + ["I'm sorry Dave. I'm afraid I can't do that."], + ["This cake is great. It's so delicious and moist."], + ["Prior to November 22, 1963."], + ] + ) + + # multi-speaker settings + # use speaker embedding layer + num_speakers: int = 0 + use_speaker_embedding: bool = False + speakers_file: str = None + speaker_embedding_channels: int = 256 + language_ids_file: str = None + use_language_embedding: bool = False + + # use d-vectors + use_d_vector_file: bool = False + d_vector_file: List[str] = None + d_vector_dim: int = None + + def __post_init__(self): + pass + # for key, val in self.model_args.items(): + # if hasattr(self, key): + # self[key] = val + + + + + +def parse_symbols(): + return { + "pad": _pad, + "eos": _eos, + "bos": _bos, + "characters": _characters, + "punctuations": _punctuations, + "phonemes": _phonemes, + } + + +# DEFAULT SET OF GRAPHEMES +_pad = "" +_eos = "" +_bos = "" +_blank = "" # TODO: check if we need this alongside with PAD +_characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +_punctuations = "!'(),-.:;? " + + +# DEFAULT SET OF IPA PHONEMES +# Phonemes definition (All IPA characters) +_vowels = "iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻ" +_non_pulmonic_consonants = "ʘɓǀɗǃʄǂɠǁʛ" +_pulmonic_consonants = "pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟ" +_suprasegmentals = "ˈˌːˑ" +_other_symbols = "ʍwɥʜʢʡɕʑɺɧʲ" +_diacrilics = "ɚ˞ɫ" +_phonemes = _vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics + + +class BaseVocabulary: + """Base Vocabulary class. + + This class only needs a vocabulary dictionary without specifying the characters. + + Args: + vocab (Dict): A dictionary of characters and their corresponding indices. + """ + + def __init__(self, vocab: Dict, pad: str = None, blank: str = None, bos: str = None, eos: str = None): + self.vocab = vocab + self.pad = pad + self.blank = blank + self.bos = bos + self.eos = eos + + @property + def pad_id(self) -> int: + """Return the index of the padding character. If the padding character is not specified, return the length + of the vocabulary.""" + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + """Return the index of the blank character. If the blank character is not specified, return the length of + the vocabulary.""" + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def bos_id(self) -> int: + """Return the index of the bos character. If the bos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def eos_id(self) -> int: + """Return the index of the eos character. If the eos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def vocab(self): + """Return the vocabulary dictionary.""" + return self._vocab + + @vocab.setter + def vocab(self, vocab): + """Set the vocabulary dictionary and character mapping dictionaries.""" + self._vocab, self._char_to_id, self._id_to_char = None, None, None + if vocab is not None: + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self._vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self._vocab) # pylint: disable=unnecessary-comprehension + } + + @staticmethod + def init_from_config(config, **kwargs): + """Initialize from the given config.""" + if config.characters is not None and "vocab_dict" in config.characters and config.characters.vocab_dict: + return ( + BaseVocabulary( + config.characters.vocab_dict, + config.characters.pad, + config.characters.blank, + config.characters.bos, + config.characters.eos, + ), + config, + ) + return BaseVocabulary(**kwargs), config + + def to_config(self): + return CharactersConfig( + vocab_dict=self._vocab, + pad=self.pad, + eos=self.eos, + bos=self.bos, + blank=self.blank, + is_unique=False, + is_sorted=False, + ) + + @property + def num_chars(self): + """Return number of tokens in the vocabulary.""" + return len(self._vocab) + + def char_to_id(self, char: str) -> int: + """Map a character to an token ID.""" + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + """Map an token ID to a character.""" + return self._id_to_char[idx] + + +class BaseCharacters: + + + def __init__( + self, + characters: str = None, + punctuations: str = None, + pad: str = None, + eos: str = None, + bos: str = None, + blank: str = None, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + self._characters = characters + self._punctuations = punctuations + self._pad = pad + self._eos = eos + self._bos = bos + self._blank = blank + self.is_unique = is_unique + self.is_sorted = is_sorted + self._create_vocab() + + @property + def pad_id(self) -> int: + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def eos_id(self) -> int: + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def bos_id(self) -> int: + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, characters): + self._characters = characters + self._create_vocab() + + @property + def punctuations(self): + return self._punctuations + + @punctuations.setter + def punctuations(self, punctuations): + self._punctuations = punctuations + self._create_vocab() + + @property + def pad(self): + return self._pad + + @pad.setter + def pad(self, pad): + self._pad = pad + self._create_vocab() + + @property + def eos(self): + return self._eos + + @eos.setter + def eos(self, eos): + self._eos = eos + self._create_vocab() + + @property + def bos(self): + return self._bos + + @bos.setter + def bos(self, bos): + self._bos = bos + self._create_vocab() + + @property + def blank(self): + return self._blank + + @blank.setter + def blank(self, blank): + self._blank = blank + self._create_vocab() + + @property + def vocab(self): + return self._vocab + + @vocab.setter + def vocab(self, vocab): + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self.vocab) # pylint: disable=unnecessary-comprehension + } + + @property + def num_chars(self): + return len(self._vocab) + + def _create_vocab(self): + _vocab = self._characters + if self.is_unique: + _vocab = list(set(_vocab)) + if self.is_sorted: + _vocab = sorted(_vocab) + _vocab = list(_vocab) + _vocab = [self._blank] + _vocab if self._blank is not None and len(self._blank) > 0 else _vocab + _vocab = [self._bos] + _vocab if self._bos is not None and len(self._bos) > 0 else _vocab + _vocab = [self._eos] + _vocab if self._eos is not None and len(self._eos) > 0 else _vocab + _vocab = [self._pad] + _vocab if self._pad is not None and len(self._pad) > 0 else _vocab + self.vocab = _vocab + list(self._punctuations) + if self.is_unique: + duplicates = {x for x in self.vocab if self.vocab.count(x) > 1} + assert ( + len(self.vocab) == len(self._char_to_id) == len(self._id_to_char) + ), f" [!] There are duplicate characters in the character set. {duplicates}" + + def char_to_id(self, char: str) -> int: + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + return self._id_to_char[idx] + + def print_log(self, level: int = 0): + """ + Prints the vocabulary in a nice format. + """ + indent = "\t" * level + print(f"{indent}| > Characters: {self._characters}") + print(f"{indent}| > Punctuations: {self._punctuations}") + print(f"{indent}| > Pad: {self._pad}") + print(f"{indent}| > EOS: {self._eos}") + print(f"{indent}| > BOS: {self._bos}") + print(f"{indent}| > Blank: {self._blank}") + print(f"{indent}| > Vocab: {self.vocab}") + print(f"{indent}| > Num chars: {self.num_chars}") + + @staticmethod + def init_from_config(config: "Coqpit"): # pylint: disable=unused-argument + """Init your character class from a config. + + Implement this method for your subclass. + """ + # use character set from config + if config.characters is not None: + return BaseCharacters(**config.characters), config + # return default character set + characters = BaseCharacters() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=self._eos, + bos=self._bos, + blank=self._blank, + is_unique=self.is_unique, + is_sorted=self.is_sorted, + ) + + +class IPAPhonemes(BaseCharacters): + + + def __init__( + self, + characters: str = _phonemes, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a IPAPhonemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + # band-aid for compatibility with old models + if "characters" in config and config.characters is not None: + if "phonemes" in config.characters and config.characters.phonemes is not None: + config.characters["characters"] = config.characters["phonemes"] + return ( + IPAPhonemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + # use character set from config + if config.characters is not None: + return IPAPhonemes(**config.characters), config + # return default character set + characters = IPAPhonemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +class Graphemes(BaseCharacters): + + + def __init__( + self, + characters: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a Graphemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + if config.characters is not None: + # band-aid for compatibility with old models + if "phonemes" in config.characters: + return ( + Graphemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + return Graphemes(**config.characters), config + characters = Graphemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +if __name__ == "__main__": + gr = Graphemes() + ph = IPAPhonemes() + gr.print_log() + ph.print_log() + + +class VitsCharacters(BaseCharacters): + """Characters class for VITs model for compatibility with pre-trained models""" + + def __init__( + self, + graphemes: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + ipa_characters: str = _phonemes, + ) -> None: + if ipa_characters is not None: + graphemes += ipa_characters + super().__init__(graphemes, punctuations, pad, None, None, "", is_unique=False, is_sorted=True) + + def _create_vocab(self): + self._vocab = [self._pad] + list(self._punctuations) + list(self._characters) + [self._blank] + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + # pylint: disable=unnecessary-comprehension + self._id_to_char = {idx: char for idx, char in enumerate(self.vocab)} + + @staticmethod + def init_from_config(config): + _pad = config.characters.pad + _punctuations = config.characters.punctuations + _letters = config.characters.characters + _letters_ipa = config.characters.phonemes + return ( + VitsCharacters(graphemes=_letters, ipa_characters=_letters_ipa, punctuations=_punctuations, pad=_pad), + config, + ) + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=None, + bos=None, + blank=self._blank, + is_unique=False, + is_sorted=True, + ) + +class TTSTokenizer: + def __init__( + self, + text_cleaner: Callable = None, + characters: "BaseCharacters" = None, + ): + self.text_cleaner = text_cleaner + self.characters = characters + self.not_found_characters = [] + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, new_characters): + self._characters = new_characters + self.pad_id = self.characters.char_to_id(self.characters.pad) if self.characters.pad else None + self.blank_id = self.characters.char_to_id(self.characters.blank) if self.characters.blank else None + + def encode(self, text: str) -> List[int]: + """Encodes a string of text as a sequence of IDs.""" + token_ids = [] + for char in text: + try: + idx = self.characters.char_to_id(char) + token_ids.append(idx) + except KeyError: + # discard but store not found characters + if char not in self.not_found_characters: + self.not_found_characters.append(char) + print(text) + print(f" [!] Character {repr(char)} not found in the vocabulary. Discarding it.") + return token_ids + + def text_to_ids(self, text: str, language: str = None) -> List[int]: # pylint: disable=unused-argument + text = self.text_cleaner(text) + text = self.encode(text) + text = self.intersperse_blank_char(text, True) + return text + + def pad_with_bos_eos(self, char_sequence: List[str]): + """Pads a sequence with the special BOS and EOS characters.""" + return [self.characters.bos_id] + list(char_sequence) + [self.characters.eos_id] + + def intersperse_blank_char(self, char_sequence: List[str], use_blank_char: bool = False): + """Intersperses the blank character between characters in a sequence. + + Use the ```blank``` character if defined else use the ```pad``` character. + """ + char_to_use = self.characters.blank_id if use_blank_char else self.characters.pad + result = [char_to_use] * (len(char_sequence) * 2 + 1) + result[1::2] = char_sequence + return result + + @staticmethod + def init_from_config(config: "Coqpit", characters: "BaseCharacters" = None): + text_cleaner = multilingual_cleaners + CharactersClass = VitsCharacters + characters, new_config = CharactersClass.init_from_config(config) + # new_config.characters.characters_class = get_import_path(characters) + new_config.characters.characters_class = VitsCharacters + return ( + TTSTokenizer(text_cleaner, characters),new_config) + + +def multilingual_cleaners(text): + """Pipeline for multilingual text""" + text = lowercase(text) + text = replace_symbols(text, lang=None) + text = remove_aux_symbols(text) + text = collapse_whitespace(text) + return text + +def lowercase(text): + return text.lower() + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text).strip() + +def replace_symbols(text, lang="en"): + + text = text.replace(";", ",") + text = text.replace("-", " ") if lang != "ca" else text.replace("-", "") + text = text.replace(":", ",") + if lang == "en": + text = text.replace("&", " and ") + elif lang == "fr": + text = text.replace("&", " et ") + elif lang == "pt": + text = text.replace("&", " e ") + elif lang == "ca": + text = text.replace("&", " i ") + text = text.replace("'", "") + return text + +def remove_aux_symbols(text): + text = re.sub(r"[\<\>\(\)\[\]\"]+", "", text) + return text \ No newline at end of file diff --git a/models/hne_female/jit_infer.py b/models/hne_female/jit_infer.py new file mode 100644 index 0000000000000000000000000000000000000000..8e29eb17b03cc9717602070d53ff9f1b733f788a --- /dev/null +++ b/models/hne_female/jit_infer.py @@ -0,0 +1,31 @@ +import os +from extra import TTSTokenizer, VitsConfig, CharactersConfig, VitsCharacters +import torch +import numpy as np + +#ch female +letters="खछगचऊुलशौढ़इणज़झैठढजफ़औ्ड़फूेानटॅयव़ऋदप.थअँऑआघहतषरसभउञडएईऐक़ िओ?धी,ॉंख़कोबमृ" +model="ch_female_vits_30hrs.pt" +text = "पेरिविंकल के जड़, उपजी अउ पत्त्ता मन ह बिकट उपयोगी हे" + +config = VitsConfig( + text_cleaner="multilingual_cleaners", + characters=CharactersConfig( + characters_class=VitsCharacters, + pad="", + eos="", + bos="", + blank="", + characters=letters, + punctuations="!¡'(),-.:;¿? ", + phonemes=None) + ) +tokenizer, config = TTSTokenizer.init_from_config(config) + +x = tokenizer.text_to_ids(text) +x = torch.from_numpy(np.array(x)).unsqueeze(0) +net = torch.jit.load(model) +with torch.no_grad(): + out2 = net(x) +import soundfile as sf +sf.write("jit.wav", out2.squeeze().cpu().numpy(), 22050) \ No newline at end of file diff --git a/models/hne_male/.gitattributes b/models/hne_male/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b --- /dev/null +++ b/models/hne_male/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/models/hne_male/README.md b/models/hne_male/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b187bb7e7d837a367ccd0862441947ad412c77f7 --- /dev/null +++ b/models/hne_male/README.md @@ -0,0 +1,3 @@ +--- +license: cc-by-4.0 +--- diff --git a/models/hne_male/ch_male_vits_30hrs.pt b/models/hne_male/ch_male_vits_30hrs.pt new file mode 100644 index 0000000000000000000000000000000000000000..c2d2e270616ab1f01c4f7721b7b51915a434e668 --- /dev/null +++ b/models/hne_male/ch_male_vits_30hrs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6ef1fb370e1a8bc844aca171316a909461521ce6afa1371d15d5f8c765cda4d9 +size 333250252 diff --git a/models/hne_male/chars.txt b/models/hne_male/chars.txt new file mode 100644 index 0000000000000000000000000000000000000000..5c0bde4eaee0264627222ea932aa833565126fcd --- /dev/null +++ b/models/hne_male/chars.txt @@ -0,0 +1 @@ +खछगचऊुलणशढ़इौज़झठैढजफ़औ्ड़फूेानटॅयव़ऋदप.थअँऑआघहतषरसभउञडएईऐक़ िओ?धी,ॉंख़कोबमृ diff --git a/models/hne_male/extra.py b/models/hne_male/extra.py new file mode 100644 index 0000000000000000000000000000000000000000..c7db561351da270a7c3931bfe0afefa7bc6d4853 --- /dev/null +++ b/models/hne_male/extra.py @@ -0,0 +1,787 @@ +from typing import Callable, Dict, List, Union +from dataclasses import asdict, dataclass, field + + +import re +from dataclasses import replace +from typing import Dict +_whitespace_re = re.compile(r"\s+") + +from dataclasses import dataclass, field +from typing import List + +# from TTS.tts.configs.shared_configs import BaseTTSConfig +# from TTS.tts.models.vits import VitsArgs, VitsAudioConfig + +@dataclass +class CharactersConfig(): + + characters_class: str = None + + # using BaseVocabulary + vocab_dict: Dict = None + + # using on BaseCharacters + pad: str = None + eos: str = None + bos: str = None + blank: str = None + characters: str = None + punctuations: str = None + phonemes: str = None + is_unique: bool = True # for backwards compatibility of models trained with char sets with duplicates + is_sorted: bool = True + + +@dataclass +class BaseTTSConfig(): + + # audio: BaseAudioConfig = field(default_factory=BaseAudioConfig) + # phoneme settings + use_phonemes: bool = False + phonemizer: str = None + phoneme_language: str = None + compute_input_seq_cache: bool = False + text_cleaner: str = None + enable_eos_bos_chars: bool = False + test_sentences_file: str = "" + phoneme_cache_path: str = None + # vocabulary parameters + characters: CharactersConfig = None + add_blank: bool = False + # training params + batch_group_size: int = 0 + loss_masking: bool = None + # dataloading + min_audio_len: int = 1 + max_audio_len: int = float("inf") + min_text_len: int = 1 + max_text_len: int = float("inf") + compute_f0: bool = False + compute_energy: bool = False + compute_linear_spec: bool = False + precompute_num_workers: int = 0 + use_noise_augment: bool = False + start_by_longest: bool = False + shuffle: bool = False + drop_last: bool = False + # dataset + datasets: str = None + # optimizer + optimizer: str = "radam" + optimizer_params: dict = None + # scheduler + lr_scheduler: str = None + lr_scheduler_params: dict = field(default_factory=lambda: {}) + # testing + test_sentences: List[str] = field(default_factory=lambda: []) + # evaluation + eval_split_max_size: int = None + eval_split_size: float = 0.01 + # weighted samplers + use_speaker_weighted_sampler: bool = False + speaker_weighted_sampler_alpha: float = 1.0 + use_language_weighted_sampler: bool = False + language_weighted_sampler_alpha: float = 1.0 + use_length_weighted_sampler: bool = False + length_weighted_sampler_alpha: float = 1.0 + + +@dataclass +class VitsAudioConfig(): + fft_size: int = 1024 + sample_rate: int = 22050 + win_length: int = 1024 + hop_length: int = 256 + num_mels: int = 80 + mel_fmin: int = 0 + mel_fmax: int = None + +@dataclass +class VitsArgs(): + num_chars: int = 100 + out_channels: int = 513 + spec_segment_size: int = 32 + hidden_channels: int = 192 + hidden_channels_ffn_text_encoder: int = 768 + num_heads_text_encoder: int = 2 + num_layers_text_encoder: int = 6 + kernel_size_text_encoder: int = 3 + dropout_p_text_encoder: float = 0.1 + dropout_p_duration_predictor: float = 0.5 + kernel_size_posterior_encoder: int = 5 + dilation_rate_posterior_encoder: int = 1 + num_layers_posterior_encoder: int = 16 + kernel_size_flow: int = 5 + dilation_rate_flow: int = 1 + num_layers_flow: int = 4 + resblock_type_decoder: str = "1" + resblock_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [3, 7, 11]) + resblock_dilation_sizes_decoder: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]) + upsample_rates_decoder: List[int] = field(default_factory=lambda: [8, 8, 2, 2]) + upsample_initial_channel_decoder: int = 512 + upsample_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [16, 16, 4, 4]) + periods_multi_period_discriminator: List[int] = field(default_factory=lambda: [2, 3, 5, 7, 11]) + use_sdp: bool = True + noise_scale: float = 1.0 + inference_noise_scale: float = 0.667 + length_scale: float = 1 + noise_scale_dp: float = 1.0 + inference_noise_scale_dp: float = 1.0 + max_inference_len: int = None + init_discriminator: bool = True + use_spectral_norm_disriminator: bool = False + use_speaker_embedding: bool = False + num_speakers: int = 0 + speakers_file: str = None + d_vector_file: List[str] = None + speaker_embedding_channels: int = 256 + use_d_vector_file: bool = False + d_vector_dim: int = 0 + detach_dp_input: bool = True + use_language_embedding: bool = False + embedded_language_dim: int = 4 + num_languages: int = 0 + language_ids_file: str = None + use_speaker_encoder_as_loss: bool = False + speaker_encoder_config_path: str = "" + speaker_encoder_model_path: str = "" + condition_dp_on_speaker: bool = True + freeze_encoder: bool = False + freeze_DP: bool = False + freeze_PE: bool = False + freeze_flow_decoder: bool = False + freeze_waveform_decoder: bool = False + encoder_sample_rate: int = None + interpolate_z: bool = True + reinit_DP: bool = False + reinit_text_encoder: bool = False +@dataclass +class VitsConfig(BaseTTSConfig): + + model: str = "vits" + # model specific params + model_args: VitsArgs = field(default_factory=VitsArgs) + audio: VitsAudioConfig = field(default_factory=VitsAudioConfig) + + # optimizer + grad_clip: List[float] = field(default_factory=lambda: [1000, 1000]) + lr_gen: float = 0.0002 + lr_disc: float = 0.0002 + lr_scheduler_gen: str = "ExponentialLR" + lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + lr_scheduler_disc: str = "ExponentialLR" + lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + scheduler_after_epoch: bool = True + optimizer: str = "AdamW" + optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01}) + + # loss params + kl_loss_alpha: float = 1.0 + disc_loss_alpha: float = 1.0 + gen_loss_alpha: float = 1.0 + feat_loss_alpha: float = 1.0 + mel_loss_alpha: float = 45.0 + dur_loss_alpha: float = 1.0 + speaker_encoder_loss_alpha: float = 1.0 + + # data loader params + return_wav: bool = True + compute_linear_spec: bool = True + + # sampler params + use_weighted_sampler: bool = False # TODO: move it to the base config + weighted_sampler_attrs: dict = field(default_factory=lambda: {}) + weighted_sampler_multipliers: dict = field(default_factory=lambda: {}) + + # overrides + r: int = 1 # DO NOT CHANGE + add_blank: bool = True + + # testing + test_sentences: List[List] = field( + default_factory=lambda: [ + ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."], + ["Be a voice, not an echo."], + ["I'm sorry Dave. I'm afraid I can't do that."], + ["This cake is great. It's so delicious and moist."], + ["Prior to November 22, 1963."], + ] + ) + + # multi-speaker settings + # use speaker embedding layer + num_speakers: int = 0 + use_speaker_embedding: bool = False + speakers_file: str = None + speaker_embedding_channels: int = 256 + language_ids_file: str = None + use_language_embedding: bool = False + + # use d-vectors + use_d_vector_file: bool = False + d_vector_file: List[str] = None + d_vector_dim: int = None + + def __post_init__(self): + pass + # for key, val in self.model_args.items(): + # if hasattr(self, key): + # self[key] = val + + + + + +def parse_symbols(): + return { + "pad": _pad, + "eos": _eos, + "bos": _bos, + "characters": _characters, + "punctuations": _punctuations, + "phonemes": _phonemes, + } + + +# DEFAULT SET OF GRAPHEMES +_pad = "" +_eos = "" +_bos = "" +_blank = "" # TODO: check if we need this alongside with PAD +_characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +_punctuations = "!'(),-.:;? " + + +# DEFAULT SET OF IPA PHONEMES +# Phonemes definition (All IPA characters) +_vowels = "iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻ" +_non_pulmonic_consonants = "ʘɓǀɗǃʄǂɠǁʛ" +_pulmonic_consonants = "pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟ" +_suprasegmentals = "ˈˌːˑ" +_other_symbols = "ʍwɥʜʢʡɕʑɺɧʲ" +_diacrilics = "ɚ˞ɫ" +_phonemes = _vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics + + +class BaseVocabulary: + """Base Vocabulary class. + + This class only needs a vocabulary dictionary without specifying the characters. + + Args: + vocab (Dict): A dictionary of characters and their corresponding indices. + """ + + def __init__(self, vocab: Dict, pad: str = None, blank: str = None, bos: str = None, eos: str = None): + self.vocab = vocab + self.pad = pad + self.blank = blank + self.bos = bos + self.eos = eos + + @property + def pad_id(self) -> int: + """Return the index of the padding character. If the padding character is not specified, return the length + of the vocabulary.""" + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + """Return the index of the blank character. If the blank character is not specified, return the length of + the vocabulary.""" + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def bos_id(self) -> int: + """Return the index of the bos character. If the bos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def eos_id(self) -> int: + """Return the index of the eos character. If the eos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def vocab(self): + """Return the vocabulary dictionary.""" + return self._vocab + + @vocab.setter + def vocab(self, vocab): + """Set the vocabulary dictionary and character mapping dictionaries.""" + self._vocab, self._char_to_id, self._id_to_char = None, None, None + if vocab is not None: + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self._vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self._vocab) # pylint: disable=unnecessary-comprehension + } + + @staticmethod + def init_from_config(config, **kwargs): + """Initialize from the given config.""" + if config.characters is not None and "vocab_dict" in config.characters and config.characters.vocab_dict: + return ( + BaseVocabulary( + config.characters.vocab_dict, + config.characters.pad, + config.characters.blank, + config.characters.bos, + config.characters.eos, + ), + config, + ) + return BaseVocabulary(**kwargs), config + + def to_config(self): + return CharactersConfig( + vocab_dict=self._vocab, + pad=self.pad, + eos=self.eos, + bos=self.bos, + blank=self.blank, + is_unique=False, + is_sorted=False, + ) + + @property + def num_chars(self): + """Return number of tokens in the vocabulary.""" + return len(self._vocab) + + def char_to_id(self, char: str) -> int: + """Map a character to an token ID.""" + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + """Map an token ID to a character.""" + return self._id_to_char[idx] + + +class BaseCharacters: + + + def __init__( + self, + characters: str = None, + punctuations: str = None, + pad: str = None, + eos: str = None, + bos: str = None, + blank: str = None, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + self._characters = characters + self._punctuations = punctuations + self._pad = pad + self._eos = eos + self._bos = bos + self._blank = blank + self.is_unique = is_unique + self.is_sorted = is_sorted + self._create_vocab() + + @property + def pad_id(self) -> int: + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def eos_id(self) -> int: + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def bos_id(self) -> int: + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, characters): + self._characters = characters + self._create_vocab() + + @property + def punctuations(self): + return self._punctuations + + @punctuations.setter + def punctuations(self, punctuations): + self._punctuations = punctuations + self._create_vocab() + + @property + def pad(self): + return self._pad + + @pad.setter + def pad(self, pad): + self._pad = pad + self._create_vocab() + + @property + def eos(self): + return self._eos + + @eos.setter + def eos(self, eos): + self._eos = eos + self._create_vocab() + + @property + def bos(self): + return self._bos + + @bos.setter + def bos(self, bos): + self._bos = bos + self._create_vocab() + + @property + def blank(self): + return self._blank + + @blank.setter + def blank(self, blank): + self._blank = blank + self._create_vocab() + + @property + def vocab(self): + return self._vocab + + @vocab.setter + def vocab(self, vocab): + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self.vocab) # pylint: disable=unnecessary-comprehension + } + + @property + def num_chars(self): + return len(self._vocab) + + def _create_vocab(self): + _vocab = self._characters + if self.is_unique: + _vocab = list(set(_vocab)) + if self.is_sorted: + _vocab = sorted(_vocab) + _vocab = list(_vocab) + _vocab = [self._blank] + _vocab if self._blank is not None and len(self._blank) > 0 else _vocab + _vocab = [self._bos] + _vocab if self._bos is not None and len(self._bos) > 0 else _vocab + _vocab = [self._eos] + _vocab if self._eos is not None and len(self._eos) > 0 else _vocab + _vocab = [self._pad] + _vocab if self._pad is not None and len(self._pad) > 0 else _vocab + self.vocab = _vocab + list(self._punctuations) + if self.is_unique: + duplicates = {x for x in self.vocab if self.vocab.count(x) > 1} + assert ( + len(self.vocab) == len(self._char_to_id) == len(self._id_to_char) + ), f" [!] There are duplicate characters in the character set. {duplicates}" + + def char_to_id(self, char: str) -> int: + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + return self._id_to_char[idx] + + def print_log(self, level: int = 0): + """ + Prints the vocabulary in a nice format. + """ + indent = "\t" * level + print(f"{indent}| > Characters: {self._characters}") + print(f"{indent}| > Punctuations: {self._punctuations}") + print(f"{indent}| > Pad: {self._pad}") + print(f"{indent}| > EOS: {self._eos}") + print(f"{indent}| > BOS: {self._bos}") + print(f"{indent}| > Blank: {self._blank}") + print(f"{indent}| > Vocab: {self.vocab}") + print(f"{indent}| > Num chars: {self.num_chars}") + + @staticmethod + def init_from_config(config: "Coqpit"): # pylint: disable=unused-argument + """Init your character class from a config. + + Implement this method for your subclass. + """ + # use character set from config + if config.characters is not None: + return BaseCharacters(**config.characters), config + # return default character set + characters = BaseCharacters() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=self._eos, + bos=self._bos, + blank=self._blank, + is_unique=self.is_unique, + is_sorted=self.is_sorted, + ) + + +class IPAPhonemes(BaseCharacters): + + + def __init__( + self, + characters: str = _phonemes, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a IPAPhonemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + # band-aid for compatibility with old models + if "characters" in config and config.characters is not None: + if "phonemes" in config.characters and config.characters.phonemes is not None: + config.characters["characters"] = config.characters["phonemes"] + return ( + IPAPhonemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + # use character set from config + if config.characters is not None: + return IPAPhonemes(**config.characters), config + # return default character set + characters = IPAPhonemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +class Graphemes(BaseCharacters): + + + def __init__( + self, + characters: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a Graphemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + if config.characters is not None: + # band-aid for compatibility with old models + if "phonemes" in config.characters: + return ( + Graphemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + return Graphemes(**config.characters), config + characters = Graphemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +if __name__ == "__main__": + gr = Graphemes() + ph = IPAPhonemes() + gr.print_log() + ph.print_log() + + +class VitsCharacters(BaseCharacters): + """Characters class for VITs model for compatibility with pre-trained models""" + + def __init__( + self, + graphemes: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + ipa_characters: str = _phonemes, + ) -> None: + if ipa_characters is not None: + graphemes += ipa_characters + super().__init__(graphemes, punctuations, pad, None, None, "", is_unique=False, is_sorted=True) + + def _create_vocab(self): + self._vocab = [self._pad] + list(self._punctuations) + list(self._characters) + [self._blank] + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + # pylint: disable=unnecessary-comprehension + self._id_to_char = {idx: char for idx, char in enumerate(self.vocab)} + + @staticmethod + def init_from_config(config): + _pad = config.characters.pad + _punctuations = config.characters.punctuations + _letters = config.characters.characters + _letters_ipa = config.characters.phonemes + return ( + VitsCharacters(graphemes=_letters, ipa_characters=_letters_ipa, punctuations=_punctuations, pad=_pad), + config, + ) + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=None, + bos=None, + blank=self._blank, + is_unique=False, + is_sorted=True, + ) + +class TTSTokenizer: + def __init__( + self, + text_cleaner: Callable = None, + characters: "BaseCharacters" = None, + ): + self.text_cleaner = text_cleaner + self.characters = characters + self.not_found_characters = [] + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, new_characters): + self._characters = new_characters + self.pad_id = self.characters.char_to_id(self.characters.pad) if self.characters.pad else None + self.blank_id = self.characters.char_to_id(self.characters.blank) if self.characters.blank else None + + def encode(self, text: str) -> List[int]: + """Encodes a string of text as a sequence of IDs.""" + token_ids = [] + for char in text: + try: + idx = self.characters.char_to_id(char) + token_ids.append(idx) + except KeyError: + # discard but store not found characters + if char not in self.not_found_characters: + self.not_found_characters.append(char) + print(text) + print(f" [!] Character {repr(char)} not found in the vocabulary. Discarding it.") + return token_ids + + def text_to_ids(self, text: str, language: str = None) -> List[int]: # pylint: disable=unused-argument + text = self.text_cleaner(text) + text = self.encode(text) + text = self.intersperse_blank_char(text, True) + return text + + def pad_with_bos_eos(self, char_sequence: List[str]): + """Pads a sequence with the special BOS and EOS characters.""" + return [self.characters.bos_id] + list(char_sequence) + [self.characters.eos_id] + + def intersperse_blank_char(self, char_sequence: List[str], use_blank_char: bool = False): + """Intersperses the blank character between characters in a sequence. + + Use the ```blank``` character if defined else use the ```pad``` character. + """ + char_to_use = self.characters.blank_id if use_blank_char else self.characters.pad + result = [char_to_use] * (len(char_sequence) * 2 + 1) + result[1::2] = char_sequence + return result + + @staticmethod + def init_from_config(config: "Coqpit", characters: "BaseCharacters" = None): + text_cleaner = multilingual_cleaners + CharactersClass = VitsCharacters + characters, new_config = CharactersClass.init_from_config(config) + # new_config.characters.characters_class = get_import_path(characters) + new_config.characters.characters_class = VitsCharacters + return ( + TTSTokenizer(text_cleaner, characters),new_config) + + +def multilingual_cleaners(text): + """Pipeline for multilingual text""" + text = lowercase(text) + text = replace_symbols(text, lang=None) + text = remove_aux_symbols(text) + text = collapse_whitespace(text) + return text + +def lowercase(text): + return text.lower() + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text).strip() + +def replace_symbols(text, lang="en"): + + text = text.replace(";", ",") + text = text.replace("-", " ") if lang != "ca" else text.replace("-", "") + text = text.replace(":", ",") + if lang == "en": + text = text.replace("&", " and ") + elif lang == "fr": + text = text.replace("&", " et ") + elif lang == "pt": + text = text.replace("&", " e ") + elif lang == "ca": + text = text.replace("&", " i ") + text = text.replace("'", "") + return text + +def remove_aux_symbols(text): + text = re.sub(r"[\<\>\(\)\[\]\"]+", "", text) + return text \ No newline at end of file diff --git a/models/hne_male/jit_infer.py b/models/hne_male/jit_infer.py new file mode 100644 index 0000000000000000000000000000000000000000..57a4c6d5675abd23d114fffdf5e658b33a934edb --- /dev/null +++ b/models/hne_male/jit_infer.py @@ -0,0 +1,31 @@ +import os +from extra import TTSTokenizer, VitsConfig, CharactersConfig, VitsCharacters +import torch +import numpy as np + +#ch female +letters="खछगचऊुलणशढ़इौज़झठैढजफ़औ्ड़फूेानटॅयव़ऋदप.थअँऑआघहतषरसभउञडएईऐक़ िओ?धी,ॉंख़कोबमृ" +model="ch_male_vits_30hrs.pt" +text = "पेरिविंकल के जड़, उपजी अउ पत्त्ता मन ह बिकट उपयोगी हे" + +config = VitsConfig( + text_cleaner="multilingual_cleaners", + characters=CharactersConfig( + characters_class=VitsCharacters, + pad="", + eos="", + bos="", + blank="", + characters=letters, + punctuations="!¡'(),-.:;¿? ", + phonemes=None) + ) +tokenizer, config = TTSTokenizer.init_from_config(config) + +x = tokenizer.text_to_ids(text) +x = torch.from_numpy(np.array(x)).unsqueeze(0) +net = torch.jit.load(model) +with torch.no_grad(): + out2 = net(x) +import soundfile as sf +sf.write("jit.wav", out2.squeeze().cpu().numpy(), 22050) \ No newline at end of file diff --git a/models/kn_female/chars.txt b/models/kn_female/chars.txt new file mode 100644 index 0000000000000000000000000000000000000000..a0730191ac5f596ce9cc1a9942e7cce798a55a7d --- /dev/null +++ b/models/kn_female/chars.txt @@ -0,0 +1 @@ +ುಹಥದೕಜಈಇೂಕಬಎಭಐಯಘಛೊತ್ಖಗಿೃಾಓಷವಉ?ೋಂಞಔಒೆ,ಊಏಳಠಫೇೈ!ಣಪ.'ಡರಚಮಧಝಅಢಸಶ ನಲಆಟೌ"ೀ diff --git a/models/kn_female/extra.py b/models/kn_female/extra.py new file mode 100644 index 0000000000000000000000000000000000000000..c7db561351da270a7c3931bfe0afefa7bc6d4853 --- /dev/null +++ b/models/kn_female/extra.py @@ -0,0 +1,787 @@ +from typing import Callable, Dict, List, Union +from dataclasses import asdict, dataclass, field + + +import re +from dataclasses import replace +from typing import Dict +_whitespace_re = re.compile(r"\s+") + +from dataclasses import dataclass, field +from typing import List + +# from TTS.tts.configs.shared_configs import BaseTTSConfig +# from TTS.tts.models.vits import VitsArgs, VitsAudioConfig + +@dataclass +class CharactersConfig(): + + characters_class: str = None + + # using BaseVocabulary + vocab_dict: Dict = None + + # using on BaseCharacters + pad: str = None + eos: str = None + bos: str = None + blank: str = None + characters: str = None + punctuations: str = None + phonemes: str = None + is_unique: bool = True # for backwards compatibility of models trained with char sets with duplicates + is_sorted: bool = True + + +@dataclass +class BaseTTSConfig(): + + # audio: BaseAudioConfig = field(default_factory=BaseAudioConfig) + # phoneme settings + use_phonemes: bool = False + phonemizer: str = None + phoneme_language: str = None + compute_input_seq_cache: bool = False + text_cleaner: str = None + enable_eos_bos_chars: bool = False + test_sentences_file: str = "" + phoneme_cache_path: str = None + # vocabulary parameters + characters: CharactersConfig = None + add_blank: bool = False + # training params + batch_group_size: int = 0 + loss_masking: bool = None + # dataloading + min_audio_len: int = 1 + max_audio_len: int = float("inf") + min_text_len: int = 1 + max_text_len: int = float("inf") + compute_f0: bool = False + compute_energy: bool = False + compute_linear_spec: bool = False + precompute_num_workers: int = 0 + use_noise_augment: bool = False + start_by_longest: bool = False + shuffle: bool = False + drop_last: bool = False + # dataset + datasets: str = None + # optimizer + optimizer: str = "radam" + optimizer_params: dict = None + # scheduler + lr_scheduler: str = None + lr_scheduler_params: dict = field(default_factory=lambda: {}) + # testing + test_sentences: List[str] = field(default_factory=lambda: []) + # evaluation + eval_split_max_size: int = None + eval_split_size: float = 0.01 + # weighted samplers + use_speaker_weighted_sampler: bool = False + speaker_weighted_sampler_alpha: float = 1.0 + use_language_weighted_sampler: bool = False + language_weighted_sampler_alpha: float = 1.0 + use_length_weighted_sampler: bool = False + length_weighted_sampler_alpha: float = 1.0 + + +@dataclass +class VitsAudioConfig(): + fft_size: int = 1024 + sample_rate: int = 22050 + win_length: int = 1024 + hop_length: int = 256 + num_mels: int = 80 + mel_fmin: int = 0 + mel_fmax: int = None + +@dataclass +class VitsArgs(): + num_chars: int = 100 + out_channels: int = 513 + spec_segment_size: int = 32 + hidden_channels: int = 192 + hidden_channels_ffn_text_encoder: int = 768 + num_heads_text_encoder: int = 2 + num_layers_text_encoder: int = 6 + kernel_size_text_encoder: int = 3 + dropout_p_text_encoder: float = 0.1 + dropout_p_duration_predictor: float = 0.5 + kernel_size_posterior_encoder: int = 5 + dilation_rate_posterior_encoder: int = 1 + num_layers_posterior_encoder: int = 16 + kernel_size_flow: int = 5 + dilation_rate_flow: int = 1 + num_layers_flow: int = 4 + resblock_type_decoder: str = "1" + resblock_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [3, 7, 11]) + resblock_dilation_sizes_decoder: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]) + upsample_rates_decoder: List[int] = field(default_factory=lambda: [8, 8, 2, 2]) + upsample_initial_channel_decoder: int = 512 + upsample_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [16, 16, 4, 4]) + periods_multi_period_discriminator: List[int] = field(default_factory=lambda: [2, 3, 5, 7, 11]) + use_sdp: bool = True + noise_scale: float = 1.0 + inference_noise_scale: float = 0.667 + length_scale: float = 1 + noise_scale_dp: float = 1.0 + inference_noise_scale_dp: float = 1.0 + max_inference_len: int = None + init_discriminator: bool = True + use_spectral_norm_disriminator: bool = False + use_speaker_embedding: bool = False + num_speakers: int = 0 + speakers_file: str = None + d_vector_file: List[str] = None + speaker_embedding_channels: int = 256 + use_d_vector_file: bool = False + d_vector_dim: int = 0 + detach_dp_input: bool = True + use_language_embedding: bool = False + embedded_language_dim: int = 4 + num_languages: int = 0 + language_ids_file: str = None + use_speaker_encoder_as_loss: bool = False + speaker_encoder_config_path: str = "" + speaker_encoder_model_path: str = "" + condition_dp_on_speaker: bool = True + freeze_encoder: bool = False + freeze_DP: bool = False + freeze_PE: bool = False + freeze_flow_decoder: bool = False + freeze_waveform_decoder: bool = False + encoder_sample_rate: int = None + interpolate_z: bool = True + reinit_DP: bool = False + reinit_text_encoder: bool = False +@dataclass +class VitsConfig(BaseTTSConfig): + + model: str = "vits" + # model specific params + model_args: VitsArgs = field(default_factory=VitsArgs) + audio: VitsAudioConfig = field(default_factory=VitsAudioConfig) + + # optimizer + grad_clip: List[float] = field(default_factory=lambda: [1000, 1000]) + lr_gen: float = 0.0002 + lr_disc: float = 0.0002 + lr_scheduler_gen: str = "ExponentialLR" + lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + lr_scheduler_disc: str = "ExponentialLR" + lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + scheduler_after_epoch: bool = True + optimizer: str = "AdamW" + optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01}) + + # loss params + kl_loss_alpha: float = 1.0 + disc_loss_alpha: float = 1.0 + gen_loss_alpha: float = 1.0 + feat_loss_alpha: float = 1.0 + mel_loss_alpha: float = 45.0 + dur_loss_alpha: float = 1.0 + speaker_encoder_loss_alpha: float = 1.0 + + # data loader params + return_wav: bool = True + compute_linear_spec: bool = True + + # sampler params + use_weighted_sampler: bool = False # TODO: move it to the base config + weighted_sampler_attrs: dict = field(default_factory=lambda: {}) + weighted_sampler_multipliers: dict = field(default_factory=lambda: {}) + + # overrides + r: int = 1 # DO NOT CHANGE + add_blank: bool = True + + # testing + test_sentences: List[List] = field( + default_factory=lambda: [ + ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."], + ["Be a voice, not an echo."], + ["I'm sorry Dave. I'm afraid I can't do that."], + ["This cake is great. It's so delicious and moist."], + ["Prior to November 22, 1963."], + ] + ) + + # multi-speaker settings + # use speaker embedding layer + num_speakers: int = 0 + use_speaker_embedding: bool = False + speakers_file: str = None + speaker_embedding_channels: int = 256 + language_ids_file: str = None + use_language_embedding: bool = False + + # use d-vectors + use_d_vector_file: bool = False + d_vector_file: List[str] = None + d_vector_dim: int = None + + def __post_init__(self): + pass + # for key, val in self.model_args.items(): + # if hasattr(self, key): + # self[key] = val + + + + + +def parse_symbols(): + return { + "pad": _pad, + "eos": _eos, + "bos": _bos, + "characters": _characters, + "punctuations": _punctuations, + "phonemes": _phonemes, + } + + +# DEFAULT SET OF GRAPHEMES +_pad = "" +_eos = "" +_bos = "" +_blank = "" # TODO: check if we need this alongside with PAD +_characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +_punctuations = "!'(),-.:;? " + + +# DEFAULT SET OF IPA PHONEMES +# Phonemes definition (All IPA characters) +_vowels = "iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻ" +_non_pulmonic_consonants = "ʘɓǀɗǃʄǂɠǁʛ" +_pulmonic_consonants = "pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟ" +_suprasegmentals = "ˈˌːˑ" +_other_symbols = "ʍwɥʜʢʡɕʑɺɧʲ" +_diacrilics = "ɚ˞ɫ" +_phonemes = _vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics + + +class BaseVocabulary: + """Base Vocabulary class. + + This class only needs a vocabulary dictionary without specifying the characters. + + Args: + vocab (Dict): A dictionary of characters and their corresponding indices. + """ + + def __init__(self, vocab: Dict, pad: str = None, blank: str = None, bos: str = None, eos: str = None): + self.vocab = vocab + self.pad = pad + self.blank = blank + self.bos = bos + self.eos = eos + + @property + def pad_id(self) -> int: + """Return the index of the padding character. If the padding character is not specified, return the length + of the vocabulary.""" + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + """Return the index of the blank character. If the blank character is not specified, return the length of + the vocabulary.""" + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def bos_id(self) -> int: + """Return the index of the bos character. If the bos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def eos_id(self) -> int: + """Return the index of the eos character. If the eos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def vocab(self): + """Return the vocabulary dictionary.""" + return self._vocab + + @vocab.setter + def vocab(self, vocab): + """Set the vocabulary dictionary and character mapping dictionaries.""" + self._vocab, self._char_to_id, self._id_to_char = None, None, None + if vocab is not None: + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self._vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self._vocab) # pylint: disable=unnecessary-comprehension + } + + @staticmethod + def init_from_config(config, **kwargs): + """Initialize from the given config.""" + if config.characters is not None and "vocab_dict" in config.characters and config.characters.vocab_dict: + return ( + BaseVocabulary( + config.characters.vocab_dict, + config.characters.pad, + config.characters.blank, + config.characters.bos, + config.characters.eos, + ), + config, + ) + return BaseVocabulary(**kwargs), config + + def to_config(self): + return CharactersConfig( + vocab_dict=self._vocab, + pad=self.pad, + eos=self.eos, + bos=self.bos, + blank=self.blank, + is_unique=False, + is_sorted=False, + ) + + @property + def num_chars(self): + """Return number of tokens in the vocabulary.""" + return len(self._vocab) + + def char_to_id(self, char: str) -> int: + """Map a character to an token ID.""" + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + """Map an token ID to a character.""" + return self._id_to_char[idx] + + +class BaseCharacters: + + + def __init__( + self, + characters: str = None, + punctuations: str = None, + pad: str = None, + eos: str = None, + bos: str = None, + blank: str = None, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + self._characters = characters + self._punctuations = punctuations + self._pad = pad + self._eos = eos + self._bos = bos + self._blank = blank + self.is_unique = is_unique + self.is_sorted = is_sorted + self._create_vocab() + + @property + def pad_id(self) -> int: + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def eos_id(self) -> int: + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def bos_id(self) -> int: + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, characters): + self._characters = characters + self._create_vocab() + + @property + def punctuations(self): + return self._punctuations + + @punctuations.setter + def punctuations(self, punctuations): + self._punctuations = punctuations + self._create_vocab() + + @property + def pad(self): + return self._pad + + @pad.setter + def pad(self, pad): + self._pad = pad + self._create_vocab() + + @property + def eos(self): + return self._eos + + @eos.setter + def eos(self, eos): + self._eos = eos + self._create_vocab() + + @property + def bos(self): + return self._bos + + @bos.setter + def bos(self, bos): + self._bos = bos + self._create_vocab() + + @property + def blank(self): + return self._blank + + @blank.setter + def blank(self, blank): + self._blank = blank + self._create_vocab() + + @property + def vocab(self): + return self._vocab + + @vocab.setter + def vocab(self, vocab): + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self.vocab) # pylint: disable=unnecessary-comprehension + } + + @property + def num_chars(self): + return len(self._vocab) + + def _create_vocab(self): + _vocab = self._characters + if self.is_unique: + _vocab = list(set(_vocab)) + if self.is_sorted: + _vocab = sorted(_vocab) + _vocab = list(_vocab) + _vocab = [self._blank] + _vocab if self._blank is not None and len(self._blank) > 0 else _vocab + _vocab = [self._bos] + _vocab if self._bos is not None and len(self._bos) > 0 else _vocab + _vocab = [self._eos] + _vocab if self._eos is not None and len(self._eos) > 0 else _vocab + _vocab = [self._pad] + _vocab if self._pad is not None and len(self._pad) > 0 else _vocab + self.vocab = _vocab + list(self._punctuations) + if self.is_unique: + duplicates = {x for x in self.vocab if self.vocab.count(x) > 1} + assert ( + len(self.vocab) == len(self._char_to_id) == len(self._id_to_char) + ), f" [!] There are duplicate characters in the character set. {duplicates}" + + def char_to_id(self, char: str) -> int: + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + return self._id_to_char[idx] + + def print_log(self, level: int = 0): + """ + Prints the vocabulary in a nice format. + """ + indent = "\t" * level + print(f"{indent}| > Characters: {self._characters}") + print(f"{indent}| > Punctuations: {self._punctuations}") + print(f"{indent}| > Pad: {self._pad}") + print(f"{indent}| > EOS: {self._eos}") + print(f"{indent}| > BOS: {self._bos}") + print(f"{indent}| > Blank: {self._blank}") + print(f"{indent}| > Vocab: {self.vocab}") + print(f"{indent}| > Num chars: {self.num_chars}") + + @staticmethod + def init_from_config(config: "Coqpit"): # pylint: disable=unused-argument + """Init your character class from a config. + + Implement this method for your subclass. + """ + # use character set from config + if config.characters is not None: + return BaseCharacters(**config.characters), config + # return default character set + characters = BaseCharacters() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=self._eos, + bos=self._bos, + blank=self._blank, + is_unique=self.is_unique, + is_sorted=self.is_sorted, + ) + + +class IPAPhonemes(BaseCharacters): + + + def __init__( + self, + characters: str = _phonemes, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a IPAPhonemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + # band-aid for compatibility with old models + if "characters" in config and config.characters is not None: + if "phonemes" in config.characters and config.characters.phonemes is not None: + config.characters["characters"] = config.characters["phonemes"] + return ( + IPAPhonemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + # use character set from config + if config.characters is not None: + return IPAPhonemes(**config.characters), config + # return default character set + characters = IPAPhonemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +class Graphemes(BaseCharacters): + + + def __init__( + self, + characters: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a Graphemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + if config.characters is not None: + # band-aid for compatibility with old models + if "phonemes" in config.characters: + return ( + Graphemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + return Graphemes(**config.characters), config + characters = Graphemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +if __name__ == "__main__": + gr = Graphemes() + ph = IPAPhonemes() + gr.print_log() + ph.print_log() + + +class VitsCharacters(BaseCharacters): + """Characters class for VITs model for compatibility with pre-trained models""" + + def __init__( + self, + graphemes: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + ipa_characters: str = _phonemes, + ) -> None: + if ipa_characters is not None: + graphemes += ipa_characters + super().__init__(graphemes, punctuations, pad, None, None, "", is_unique=False, is_sorted=True) + + def _create_vocab(self): + self._vocab = [self._pad] + list(self._punctuations) + list(self._characters) + [self._blank] + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + # pylint: disable=unnecessary-comprehension + self._id_to_char = {idx: char for idx, char in enumerate(self.vocab)} + + @staticmethod + def init_from_config(config): + _pad = config.characters.pad + _punctuations = config.characters.punctuations + _letters = config.characters.characters + _letters_ipa = config.characters.phonemes + return ( + VitsCharacters(graphemes=_letters, ipa_characters=_letters_ipa, punctuations=_punctuations, pad=_pad), + config, + ) + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=None, + bos=None, + blank=self._blank, + is_unique=False, + is_sorted=True, + ) + +class TTSTokenizer: + def __init__( + self, + text_cleaner: Callable = None, + characters: "BaseCharacters" = None, + ): + self.text_cleaner = text_cleaner + self.characters = characters + self.not_found_characters = [] + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, new_characters): + self._characters = new_characters + self.pad_id = self.characters.char_to_id(self.characters.pad) if self.characters.pad else None + self.blank_id = self.characters.char_to_id(self.characters.blank) if self.characters.blank else None + + def encode(self, text: str) -> List[int]: + """Encodes a string of text as a sequence of IDs.""" + token_ids = [] + for char in text: + try: + idx = self.characters.char_to_id(char) + token_ids.append(idx) + except KeyError: + # discard but store not found characters + if char not in self.not_found_characters: + self.not_found_characters.append(char) + print(text) + print(f" [!] Character {repr(char)} not found in the vocabulary. Discarding it.") + return token_ids + + def text_to_ids(self, text: str, language: str = None) -> List[int]: # pylint: disable=unused-argument + text = self.text_cleaner(text) + text = self.encode(text) + text = self.intersperse_blank_char(text, True) + return text + + def pad_with_bos_eos(self, char_sequence: List[str]): + """Pads a sequence with the special BOS and EOS characters.""" + return [self.characters.bos_id] + list(char_sequence) + [self.characters.eos_id] + + def intersperse_blank_char(self, char_sequence: List[str], use_blank_char: bool = False): + """Intersperses the blank character between characters in a sequence. + + Use the ```blank``` character if defined else use the ```pad``` character. + """ + char_to_use = self.characters.blank_id if use_blank_char else self.characters.pad + result = [char_to_use] * (len(char_sequence) * 2 + 1) + result[1::2] = char_sequence + return result + + @staticmethod + def init_from_config(config: "Coqpit", characters: "BaseCharacters" = None): + text_cleaner = multilingual_cleaners + CharactersClass = VitsCharacters + characters, new_config = CharactersClass.init_from_config(config) + # new_config.characters.characters_class = get_import_path(characters) + new_config.characters.characters_class = VitsCharacters + return ( + TTSTokenizer(text_cleaner, characters),new_config) + + +def multilingual_cleaners(text): + """Pipeline for multilingual text""" + text = lowercase(text) + text = replace_symbols(text, lang=None) + text = remove_aux_symbols(text) + text = collapse_whitespace(text) + return text + +def lowercase(text): + return text.lower() + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text).strip() + +def replace_symbols(text, lang="en"): + + text = text.replace(";", ",") + text = text.replace("-", " ") if lang != "ca" else text.replace("-", "") + text = text.replace(":", ",") + if lang == "en": + text = text.replace("&", " and ") + elif lang == "fr": + text = text.replace("&", " et ") + elif lang == "pt": + text = text.replace("&", " e ") + elif lang == "ca": + text = text.replace("&", " i ") + text = text.replace("'", "") + return text + +def remove_aux_symbols(text): + text = re.sub(r"[\<\>\(\)\[\]\"]+", "", text) + return text \ No newline at end of file diff --git a/models/kn_female/jit_infer.py b/models/kn_female/jit_infer.py new file mode 100644 index 0000000000000000000000000000000000000000..837cf8312a5c6a97035ed96b08a8a31245ca8c41 --- /dev/null +++ b/models/kn_female/jit_infer.py @@ -0,0 +1,32 @@ +import os +from extra import TTSTokenizer, VitsConfig, CharactersConfig, VitsCharacters +import torch +import numpy as np + +#ch female +with open("chars.txt", 'r') as f: + letters = f.read().strip('\n') +model="kn_female_vits_30hrs.pt" +text = "ಬಿಸ್ಫೆನಾಲ್ಎ ಗೆ ಶಿಶುವು ಒಡ್ಡಿಕೊಂಡಾಗ ಅದು, ಲೈಂಗಿಕವಾಗಿ ದ್ವಿರೂಪಿ ಮೆದುಳು ರಚನೆಯ ಮೇಲೆ ಗಾಡ ಪರಿಣಾಮ ಬೀರಬಹುದು ಎಂದು ವರದಿ ಹೇಳುತ್ತದೆ." + +config = VitsConfig( + text_cleaner="multilingual_cleaners", + characters=CharactersConfig( + characters_class=VitsCharacters, + pad="", + eos="", + bos="", + blank="", + characters=letters, + punctuations="!¡'(),-.:;¿? ", + phonemes=None) + ) +tokenizer, config = TTSTokenizer.init_from_config(config) + +x = tokenizer.text_to_ids(text) +x = torch.from_numpy(np.array(x)).unsqueeze(0) +net = torch.jit.load(model) +with torch.no_grad(): + out2 = net(x) +import soundfile as sf +sf.write("jit.wav", out2.squeeze().cpu().numpy(), 22050) \ No newline at end of file diff --git a/models/kn_female/kn_female_vits_30hrs.pt b/models/kn_female/kn_female_vits_30hrs.pt new file mode 100644 index 0000000000000000000000000000000000000000..7124249dd0a6fd2dce870de28c8e47d1d04dba89 --- /dev/null +++ b/models/kn_female/kn_female_vits_30hrs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:49be422a46afc7714a8ea1cab589d986c3bc61939faa5f1d5d6f9f80a263c51c +size 333252998 diff --git a/models/kn_male/.gitattributes b/models/kn_male/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b --- /dev/null +++ b/models/kn_male/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/models/kn_male/README.md b/models/kn_male/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b187bb7e7d837a367ccd0862441947ad412c77f7 --- /dev/null +++ b/models/kn_male/README.md @@ -0,0 +1,3 @@ +--- +license: cc-by-4.0 +--- diff --git a/models/kn_male/chars.txt b/models/kn_male/chars.txt new file mode 100644 index 0000000000000000000000000000000000000000..9460740387efca17908ddc0c2e7f1167cea6e0cf --- /dev/null +++ b/models/kn_male/chars.txt @@ -0,0 +1 @@ +ುಹಥದೕಜಈಇೂಕಬಭಎಐಯಘಛೊತ್ಗಖಿೃಾಉಷವಓ?ೋಂಞಔ,ೆಒಊಏಳಠಫೇೈ!ಣ.ಪ'ಡರಚಮಧಆಝಅಢಸಶ ನಲಟೌ"ೀ diff --git a/models/kn_male/extra.py b/models/kn_male/extra.py new file mode 100644 index 0000000000000000000000000000000000000000..c7db561351da270a7c3931bfe0afefa7bc6d4853 --- /dev/null +++ b/models/kn_male/extra.py @@ -0,0 +1,787 @@ +from typing import Callable, Dict, List, Union +from dataclasses import asdict, dataclass, field + + +import re +from dataclasses import replace +from typing import Dict +_whitespace_re = re.compile(r"\s+") + +from dataclasses import dataclass, field +from typing import List + +# from TTS.tts.configs.shared_configs import BaseTTSConfig +# from TTS.tts.models.vits import VitsArgs, VitsAudioConfig + +@dataclass +class CharactersConfig(): + + characters_class: str = None + + # using BaseVocabulary + vocab_dict: Dict = None + + # using on BaseCharacters + pad: str = None + eos: str = None + bos: str = None + blank: str = None + characters: str = None + punctuations: str = None + phonemes: str = None + is_unique: bool = True # for backwards compatibility of models trained with char sets with duplicates + is_sorted: bool = True + + +@dataclass +class BaseTTSConfig(): + + # audio: BaseAudioConfig = field(default_factory=BaseAudioConfig) + # phoneme settings + use_phonemes: bool = False + phonemizer: str = None + phoneme_language: str = None + compute_input_seq_cache: bool = False + text_cleaner: str = None + enable_eos_bos_chars: bool = False + test_sentences_file: str = "" + phoneme_cache_path: str = None + # vocabulary parameters + characters: CharactersConfig = None + add_blank: bool = False + # training params + batch_group_size: int = 0 + loss_masking: bool = None + # dataloading + min_audio_len: int = 1 + max_audio_len: int = float("inf") + min_text_len: int = 1 + max_text_len: int = float("inf") + compute_f0: bool = False + compute_energy: bool = False + compute_linear_spec: bool = False + precompute_num_workers: int = 0 + use_noise_augment: bool = False + start_by_longest: bool = False + shuffle: bool = False + drop_last: bool = False + # dataset + datasets: str = None + # optimizer + optimizer: str = "radam" + optimizer_params: dict = None + # scheduler + lr_scheduler: str = None + lr_scheduler_params: dict = field(default_factory=lambda: {}) + # testing + test_sentences: List[str] = field(default_factory=lambda: []) + # evaluation + eval_split_max_size: int = None + eval_split_size: float = 0.01 + # weighted samplers + use_speaker_weighted_sampler: bool = False + speaker_weighted_sampler_alpha: float = 1.0 + use_language_weighted_sampler: bool = False + language_weighted_sampler_alpha: float = 1.0 + use_length_weighted_sampler: bool = False + length_weighted_sampler_alpha: float = 1.0 + + +@dataclass +class VitsAudioConfig(): + fft_size: int = 1024 + sample_rate: int = 22050 + win_length: int = 1024 + hop_length: int = 256 + num_mels: int = 80 + mel_fmin: int = 0 + mel_fmax: int = None + +@dataclass +class VitsArgs(): + num_chars: int = 100 + out_channels: int = 513 + spec_segment_size: int = 32 + hidden_channels: int = 192 + hidden_channels_ffn_text_encoder: int = 768 + num_heads_text_encoder: int = 2 + num_layers_text_encoder: int = 6 + kernel_size_text_encoder: int = 3 + dropout_p_text_encoder: float = 0.1 + dropout_p_duration_predictor: float = 0.5 + kernel_size_posterior_encoder: int = 5 + dilation_rate_posterior_encoder: int = 1 + num_layers_posterior_encoder: int = 16 + kernel_size_flow: int = 5 + dilation_rate_flow: int = 1 + num_layers_flow: int = 4 + resblock_type_decoder: str = "1" + resblock_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [3, 7, 11]) + resblock_dilation_sizes_decoder: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]) + upsample_rates_decoder: List[int] = field(default_factory=lambda: [8, 8, 2, 2]) + upsample_initial_channel_decoder: int = 512 + upsample_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [16, 16, 4, 4]) + periods_multi_period_discriminator: List[int] = field(default_factory=lambda: [2, 3, 5, 7, 11]) + use_sdp: bool = True + noise_scale: float = 1.0 + inference_noise_scale: float = 0.667 + length_scale: float = 1 + noise_scale_dp: float = 1.0 + inference_noise_scale_dp: float = 1.0 + max_inference_len: int = None + init_discriminator: bool = True + use_spectral_norm_disriminator: bool = False + use_speaker_embedding: bool = False + num_speakers: int = 0 + speakers_file: str = None + d_vector_file: List[str] = None + speaker_embedding_channels: int = 256 + use_d_vector_file: bool = False + d_vector_dim: int = 0 + detach_dp_input: bool = True + use_language_embedding: bool = False + embedded_language_dim: int = 4 + num_languages: int = 0 + language_ids_file: str = None + use_speaker_encoder_as_loss: bool = False + speaker_encoder_config_path: str = "" + speaker_encoder_model_path: str = "" + condition_dp_on_speaker: bool = True + freeze_encoder: bool = False + freeze_DP: bool = False + freeze_PE: bool = False + freeze_flow_decoder: bool = False + freeze_waveform_decoder: bool = False + encoder_sample_rate: int = None + interpolate_z: bool = True + reinit_DP: bool = False + reinit_text_encoder: bool = False +@dataclass +class VitsConfig(BaseTTSConfig): + + model: str = "vits" + # model specific params + model_args: VitsArgs = field(default_factory=VitsArgs) + audio: VitsAudioConfig = field(default_factory=VitsAudioConfig) + + # optimizer + grad_clip: List[float] = field(default_factory=lambda: [1000, 1000]) + lr_gen: float = 0.0002 + lr_disc: float = 0.0002 + lr_scheduler_gen: str = "ExponentialLR" + lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + lr_scheduler_disc: str = "ExponentialLR" + lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + scheduler_after_epoch: bool = True + optimizer: str = "AdamW" + optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01}) + + # loss params + kl_loss_alpha: float = 1.0 + disc_loss_alpha: float = 1.0 + gen_loss_alpha: float = 1.0 + feat_loss_alpha: float = 1.0 + mel_loss_alpha: float = 45.0 + dur_loss_alpha: float = 1.0 + speaker_encoder_loss_alpha: float = 1.0 + + # data loader params + return_wav: bool = True + compute_linear_spec: bool = True + + # sampler params + use_weighted_sampler: bool = False # TODO: move it to the base config + weighted_sampler_attrs: dict = field(default_factory=lambda: {}) + weighted_sampler_multipliers: dict = field(default_factory=lambda: {}) + + # overrides + r: int = 1 # DO NOT CHANGE + add_blank: bool = True + + # testing + test_sentences: List[List] = field( + default_factory=lambda: [ + ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."], + ["Be a voice, not an echo."], + ["I'm sorry Dave. I'm afraid I can't do that."], + ["This cake is great. It's so delicious and moist."], + ["Prior to November 22, 1963."], + ] + ) + + # multi-speaker settings + # use speaker embedding layer + num_speakers: int = 0 + use_speaker_embedding: bool = False + speakers_file: str = None + speaker_embedding_channels: int = 256 + language_ids_file: str = None + use_language_embedding: bool = False + + # use d-vectors + use_d_vector_file: bool = False + d_vector_file: List[str] = None + d_vector_dim: int = None + + def __post_init__(self): + pass + # for key, val in self.model_args.items(): + # if hasattr(self, key): + # self[key] = val + + + + + +def parse_symbols(): + return { + "pad": _pad, + "eos": _eos, + "bos": _bos, + "characters": _characters, + "punctuations": _punctuations, + "phonemes": _phonemes, + } + + +# DEFAULT SET OF GRAPHEMES +_pad = "" +_eos = "" +_bos = "" +_blank = "" # TODO: check if we need this alongside with PAD +_characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +_punctuations = "!'(),-.:;? " + + +# DEFAULT SET OF IPA PHONEMES +# Phonemes definition (All IPA characters) +_vowels = "iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻ" +_non_pulmonic_consonants = "ʘɓǀɗǃʄǂɠǁʛ" +_pulmonic_consonants = "pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟ" +_suprasegmentals = "ˈˌːˑ" +_other_symbols = "ʍwɥʜʢʡɕʑɺɧʲ" +_diacrilics = "ɚ˞ɫ" +_phonemes = _vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics + + +class BaseVocabulary: + """Base Vocabulary class. + + This class only needs a vocabulary dictionary without specifying the characters. + + Args: + vocab (Dict): A dictionary of characters and their corresponding indices. + """ + + def __init__(self, vocab: Dict, pad: str = None, blank: str = None, bos: str = None, eos: str = None): + self.vocab = vocab + self.pad = pad + self.blank = blank + self.bos = bos + self.eos = eos + + @property + def pad_id(self) -> int: + """Return the index of the padding character. If the padding character is not specified, return the length + of the vocabulary.""" + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + """Return the index of the blank character. If the blank character is not specified, return the length of + the vocabulary.""" + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def bos_id(self) -> int: + """Return the index of the bos character. If the bos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def eos_id(self) -> int: + """Return the index of the eos character. If the eos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def vocab(self): + """Return the vocabulary dictionary.""" + return self._vocab + + @vocab.setter + def vocab(self, vocab): + """Set the vocabulary dictionary and character mapping dictionaries.""" + self._vocab, self._char_to_id, self._id_to_char = None, None, None + if vocab is not None: + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self._vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self._vocab) # pylint: disable=unnecessary-comprehension + } + + @staticmethod + def init_from_config(config, **kwargs): + """Initialize from the given config.""" + if config.characters is not None and "vocab_dict" in config.characters and config.characters.vocab_dict: + return ( + BaseVocabulary( + config.characters.vocab_dict, + config.characters.pad, + config.characters.blank, + config.characters.bos, + config.characters.eos, + ), + config, + ) + return BaseVocabulary(**kwargs), config + + def to_config(self): + return CharactersConfig( + vocab_dict=self._vocab, + pad=self.pad, + eos=self.eos, + bos=self.bos, + blank=self.blank, + is_unique=False, + is_sorted=False, + ) + + @property + def num_chars(self): + """Return number of tokens in the vocabulary.""" + return len(self._vocab) + + def char_to_id(self, char: str) -> int: + """Map a character to an token ID.""" + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + """Map an token ID to a character.""" + return self._id_to_char[idx] + + +class BaseCharacters: + + + def __init__( + self, + characters: str = None, + punctuations: str = None, + pad: str = None, + eos: str = None, + bos: str = None, + blank: str = None, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + self._characters = characters + self._punctuations = punctuations + self._pad = pad + self._eos = eos + self._bos = bos + self._blank = blank + self.is_unique = is_unique + self.is_sorted = is_sorted + self._create_vocab() + + @property + def pad_id(self) -> int: + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def eos_id(self) -> int: + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def bos_id(self) -> int: + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, characters): + self._characters = characters + self._create_vocab() + + @property + def punctuations(self): + return self._punctuations + + @punctuations.setter + def punctuations(self, punctuations): + self._punctuations = punctuations + self._create_vocab() + + @property + def pad(self): + return self._pad + + @pad.setter + def pad(self, pad): + self._pad = pad + self._create_vocab() + + @property + def eos(self): + return self._eos + + @eos.setter + def eos(self, eos): + self._eos = eos + self._create_vocab() + + @property + def bos(self): + return self._bos + + @bos.setter + def bos(self, bos): + self._bos = bos + self._create_vocab() + + @property + def blank(self): + return self._blank + + @blank.setter + def blank(self, blank): + self._blank = blank + self._create_vocab() + + @property + def vocab(self): + return self._vocab + + @vocab.setter + def vocab(self, vocab): + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self.vocab) # pylint: disable=unnecessary-comprehension + } + + @property + def num_chars(self): + return len(self._vocab) + + def _create_vocab(self): + _vocab = self._characters + if self.is_unique: + _vocab = list(set(_vocab)) + if self.is_sorted: + _vocab = sorted(_vocab) + _vocab = list(_vocab) + _vocab = [self._blank] + _vocab if self._blank is not None and len(self._blank) > 0 else _vocab + _vocab = [self._bos] + _vocab if self._bos is not None and len(self._bos) > 0 else _vocab + _vocab = [self._eos] + _vocab if self._eos is not None and len(self._eos) > 0 else _vocab + _vocab = [self._pad] + _vocab if self._pad is not None and len(self._pad) > 0 else _vocab + self.vocab = _vocab + list(self._punctuations) + if self.is_unique: + duplicates = {x for x in self.vocab if self.vocab.count(x) > 1} + assert ( + len(self.vocab) == len(self._char_to_id) == len(self._id_to_char) + ), f" [!] There are duplicate characters in the character set. {duplicates}" + + def char_to_id(self, char: str) -> int: + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + return self._id_to_char[idx] + + def print_log(self, level: int = 0): + """ + Prints the vocabulary in a nice format. + """ + indent = "\t" * level + print(f"{indent}| > Characters: {self._characters}") + print(f"{indent}| > Punctuations: {self._punctuations}") + print(f"{indent}| > Pad: {self._pad}") + print(f"{indent}| > EOS: {self._eos}") + print(f"{indent}| > BOS: {self._bos}") + print(f"{indent}| > Blank: {self._blank}") + print(f"{indent}| > Vocab: {self.vocab}") + print(f"{indent}| > Num chars: {self.num_chars}") + + @staticmethod + def init_from_config(config: "Coqpit"): # pylint: disable=unused-argument + """Init your character class from a config. + + Implement this method for your subclass. + """ + # use character set from config + if config.characters is not None: + return BaseCharacters(**config.characters), config + # return default character set + characters = BaseCharacters() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=self._eos, + bos=self._bos, + blank=self._blank, + is_unique=self.is_unique, + is_sorted=self.is_sorted, + ) + + +class IPAPhonemes(BaseCharacters): + + + def __init__( + self, + characters: str = _phonemes, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a IPAPhonemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + # band-aid for compatibility with old models + if "characters" in config and config.characters is not None: + if "phonemes" in config.characters and config.characters.phonemes is not None: + config.characters["characters"] = config.characters["phonemes"] + return ( + IPAPhonemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + # use character set from config + if config.characters is not None: + return IPAPhonemes(**config.characters), config + # return default character set + characters = IPAPhonemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +class Graphemes(BaseCharacters): + + + def __init__( + self, + characters: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a Graphemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + if config.characters is not None: + # band-aid for compatibility with old models + if "phonemes" in config.characters: + return ( + Graphemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + return Graphemes(**config.characters), config + characters = Graphemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +if __name__ == "__main__": + gr = Graphemes() + ph = IPAPhonemes() + gr.print_log() + ph.print_log() + + +class VitsCharacters(BaseCharacters): + """Characters class for VITs model for compatibility with pre-trained models""" + + def __init__( + self, + graphemes: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + ipa_characters: str = _phonemes, + ) -> None: + if ipa_characters is not None: + graphemes += ipa_characters + super().__init__(graphemes, punctuations, pad, None, None, "", is_unique=False, is_sorted=True) + + def _create_vocab(self): + self._vocab = [self._pad] + list(self._punctuations) + list(self._characters) + [self._blank] + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + # pylint: disable=unnecessary-comprehension + self._id_to_char = {idx: char for idx, char in enumerate(self.vocab)} + + @staticmethod + def init_from_config(config): + _pad = config.characters.pad + _punctuations = config.characters.punctuations + _letters = config.characters.characters + _letters_ipa = config.characters.phonemes + return ( + VitsCharacters(graphemes=_letters, ipa_characters=_letters_ipa, punctuations=_punctuations, pad=_pad), + config, + ) + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=None, + bos=None, + blank=self._blank, + is_unique=False, + is_sorted=True, + ) + +class TTSTokenizer: + def __init__( + self, + text_cleaner: Callable = None, + characters: "BaseCharacters" = None, + ): + self.text_cleaner = text_cleaner + self.characters = characters + self.not_found_characters = [] + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, new_characters): + self._characters = new_characters + self.pad_id = self.characters.char_to_id(self.characters.pad) if self.characters.pad else None + self.blank_id = self.characters.char_to_id(self.characters.blank) if self.characters.blank else None + + def encode(self, text: str) -> List[int]: + """Encodes a string of text as a sequence of IDs.""" + token_ids = [] + for char in text: + try: + idx = self.characters.char_to_id(char) + token_ids.append(idx) + except KeyError: + # discard but store not found characters + if char not in self.not_found_characters: + self.not_found_characters.append(char) + print(text) + print(f" [!] Character {repr(char)} not found in the vocabulary. Discarding it.") + return token_ids + + def text_to_ids(self, text: str, language: str = None) -> List[int]: # pylint: disable=unused-argument + text = self.text_cleaner(text) + text = self.encode(text) + text = self.intersperse_blank_char(text, True) + return text + + def pad_with_bos_eos(self, char_sequence: List[str]): + """Pads a sequence with the special BOS and EOS characters.""" + return [self.characters.bos_id] + list(char_sequence) + [self.characters.eos_id] + + def intersperse_blank_char(self, char_sequence: List[str], use_blank_char: bool = False): + """Intersperses the blank character between characters in a sequence. + + Use the ```blank``` character if defined else use the ```pad``` character. + """ + char_to_use = self.characters.blank_id if use_blank_char else self.characters.pad + result = [char_to_use] * (len(char_sequence) * 2 + 1) + result[1::2] = char_sequence + return result + + @staticmethod + def init_from_config(config: "Coqpit", characters: "BaseCharacters" = None): + text_cleaner = multilingual_cleaners + CharactersClass = VitsCharacters + characters, new_config = CharactersClass.init_from_config(config) + # new_config.characters.characters_class = get_import_path(characters) + new_config.characters.characters_class = VitsCharacters + return ( + TTSTokenizer(text_cleaner, characters),new_config) + + +def multilingual_cleaners(text): + """Pipeline for multilingual text""" + text = lowercase(text) + text = replace_symbols(text, lang=None) + text = remove_aux_symbols(text) + text = collapse_whitespace(text) + return text + +def lowercase(text): + return text.lower() + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text).strip() + +def replace_symbols(text, lang="en"): + + text = text.replace(";", ",") + text = text.replace("-", " ") if lang != "ca" else text.replace("-", "") + text = text.replace(":", ",") + if lang == "en": + text = text.replace("&", " and ") + elif lang == "fr": + text = text.replace("&", " et ") + elif lang == "pt": + text = text.replace("&", " e ") + elif lang == "ca": + text = text.replace("&", " i ") + text = text.replace("'", "") + return text + +def remove_aux_symbols(text): + text = re.sub(r"[\<\>\(\)\[\]\"]+", "", text) + return text \ No newline at end of file diff --git a/models/kn_male/jit_infer.py b/models/kn_male/jit_infer.py new file mode 100644 index 0000000000000000000000000000000000000000..776f1962975487e1c0b0287553447c95f2c36411 --- /dev/null +++ b/models/kn_male/jit_infer.py @@ -0,0 +1,32 @@ +import os +from extra import TTSTokenizer, VitsConfig, CharactersConfig, VitsCharacters +import torch +import numpy as np + +#ch female +with open("chars.txt", 'r') as f: + letters = f.read().strip('\n') +model="kn_male_vits_30hrs.pt" +text = "ಬಿಸ್ಫೆನಾಲ್ಎ ಗೆ ಶಿಶುವು ಒಡ್ಡಿಕೊಂಡಾಗ ಅದು, ಲೈಂಗಿಕವಾಗಿ ದ್ವಿರೂಪಿ ಮೆದುಳು ರಚನೆಯ ಮೇಲೆ ಗಾಡ ಪರಿಣಾಮ ಬೀರಬಹುದು ಎಂದು ವರದಿ ಹೇಳುತ್ತದೆ." + +config = VitsConfig( + text_cleaner="multilingual_cleaners", + characters=CharactersConfig( + characters_class=VitsCharacters, + pad="", + eos="", + bos="", + blank="", + characters=letters, + punctuations="!¡'(),-.:;¿? ", + phonemes=None) + ) +tokenizer, config = TTSTokenizer.init_from_config(config) + +x = tokenizer.text_to_ids(text) +x = torch.from_numpy(np.array(x)).unsqueeze(0) +net = torch.jit.load(model) +with torch.no_grad(): + out2 = net(x) +import soundfile as sf +sf.write("jit.wav", out2.squeeze().cpu().numpy(), 22050) \ No newline at end of file diff --git a/models/kn_male/kn_male_vits_30hrs.pt b/models/kn_male/kn_male_vits_30hrs.pt new file mode 100644 index 0000000000000000000000000000000000000000..85be314c5b0aabbc53801b829141e0a946554063 --- /dev/null +++ b/models/kn_male/kn_male_vits_30hrs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b2a7e16b3509df1be518e0616bcb3ce6eb9c4e59d7fafed075ee57426befdef9 +size 333247564 diff --git a/models/mag_female/.gitattributes b/models/mag_female/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b --- /dev/null +++ b/models/mag_female/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/models/mag_female/README.md b/models/mag_female/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b187bb7e7d837a367ccd0862441947ad412c77f7 --- /dev/null +++ b/models/mag_female/README.md @@ -0,0 +1,3 @@ +--- +license: cc-by-4.0 +--- diff --git a/models/mag_female/chars.txt b/models/mag_female/chars.txt new file mode 100644 index 0000000000000000000000000000000000000000..533c640c612f628885d974a5f4e66ce327c0391b --- /dev/null +++ b/models/mag_female/chars.txt @@ -0,0 +1 @@ +ओग?ढक़फबथुझ़ख़.तङफ़घआरोऊ'खअणढ़ूईाीनौएषदे"यभडछ, ंटहइवउम्ँठधॉपड़ऋ!ऑिऽकैऐऔशजृलञचसज़ diff --git a/models/mag_female/extra.py b/models/mag_female/extra.py new file mode 100644 index 0000000000000000000000000000000000000000..c7db561351da270a7c3931bfe0afefa7bc6d4853 --- /dev/null +++ b/models/mag_female/extra.py @@ -0,0 +1,787 @@ +from typing import Callable, Dict, List, Union +from dataclasses import asdict, dataclass, field + + +import re +from dataclasses import replace +from typing import Dict +_whitespace_re = re.compile(r"\s+") + +from dataclasses import dataclass, field +from typing import List + +# from TTS.tts.configs.shared_configs import BaseTTSConfig +# from TTS.tts.models.vits import VitsArgs, VitsAudioConfig + +@dataclass +class CharactersConfig(): + + characters_class: str = None + + # using BaseVocabulary + vocab_dict: Dict = None + + # using on BaseCharacters + pad: str = None + eos: str = None + bos: str = None + blank: str = None + characters: str = None + punctuations: str = None + phonemes: str = None + is_unique: bool = True # for backwards compatibility of models trained with char sets with duplicates + is_sorted: bool = True + + +@dataclass +class BaseTTSConfig(): + + # audio: BaseAudioConfig = field(default_factory=BaseAudioConfig) + # phoneme settings + use_phonemes: bool = False + phonemizer: str = None + phoneme_language: str = None + compute_input_seq_cache: bool = False + text_cleaner: str = None + enable_eos_bos_chars: bool = False + test_sentences_file: str = "" + phoneme_cache_path: str = None + # vocabulary parameters + characters: CharactersConfig = None + add_blank: bool = False + # training params + batch_group_size: int = 0 + loss_masking: bool = None + # dataloading + min_audio_len: int = 1 + max_audio_len: int = float("inf") + min_text_len: int = 1 + max_text_len: int = float("inf") + compute_f0: bool = False + compute_energy: bool = False + compute_linear_spec: bool = False + precompute_num_workers: int = 0 + use_noise_augment: bool = False + start_by_longest: bool = False + shuffle: bool = False + drop_last: bool = False + # dataset + datasets: str = None + # optimizer + optimizer: str = "radam" + optimizer_params: dict = None + # scheduler + lr_scheduler: str = None + lr_scheduler_params: dict = field(default_factory=lambda: {}) + # testing + test_sentences: List[str] = field(default_factory=lambda: []) + # evaluation + eval_split_max_size: int = None + eval_split_size: float = 0.01 + # weighted samplers + use_speaker_weighted_sampler: bool = False + speaker_weighted_sampler_alpha: float = 1.0 + use_language_weighted_sampler: bool = False + language_weighted_sampler_alpha: float = 1.0 + use_length_weighted_sampler: bool = False + length_weighted_sampler_alpha: float = 1.0 + + +@dataclass +class VitsAudioConfig(): + fft_size: int = 1024 + sample_rate: int = 22050 + win_length: int = 1024 + hop_length: int = 256 + num_mels: int = 80 + mel_fmin: int = 0 + mel_fmax: int = None + +@dataclass +class VitsArgs(): + num_chars: int = 100 + out_channels: int = 513 + spec_segment_size: int = 32 + hidden_channels: int = 192 + hidden_channels_ffn_text_encoder: int = 768 + num_heads_text_encoder: int = 2 + num_layers_text_encoder: int = 6 + kernel_size_text_encoder: int = 3 + dropout_p_text_encoder: float = 0.1 + dropout_p_duration_predictor: float = 0.5 + kernel_size_posterior_encoder: int = 5 + dilation_rate_posterior_encoder: int = 1 + num_layers_posterior_encoder: int = 16 + kernel_size_flow: int = 5 + dilation_rate_flow: int = 1 + num_layers_flow: int = 4 + resblock_type_decoder: str = "1" + resblock_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [3, 7, 11]) + resblock_dilation_sizes_decoder: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]) + upsample_rates_decoder: List[int] = field(default_factory=lambda: [8, 8, 2, 2]) + upsample_initial_channel_decoder: int = 512 + upsample_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [16, 16, 4, 4]) + periods_multi_period_discriminator: List[int] = field(default_factory=lambda: [2, 3, 5, 7, 11]) + use_sdp: bool = True + noise_scale: float = 1.0 + inference_noise_scale: float = 0.667 + length_scale: float = 1 + noise_scale_dp: float = 1.0 + inference_noise_scale_dp: float = 1.0 + max_inference_len: int = None + init_discriminator: bool = True + use_spectral_norm_disriminator: bool = False + use_speaker_embedding: bool = False + num_speakers: int = 0 + speakers_file: str = None + d_vector_file: List[str] = None + speaker_embedding_channels: int = 256 + use_d_vector_file: bool = False + d_vector_dim: int = 0 + detach_dp_input: bool = True + use_language_embedding: bool = False + embedded_language_dim: int = 4 + num_languages: int = 0 + language_ids_file: str = None + use_speaker_encoder_as_loss: bool = False + speaker_encoder_config_path: str = "" + speaker_encoder_model_path: str = "" + condition_dp_on_speaker: bool = True + freeze_encoder: bool = False + freeze_DP: bool = False + freeze_PE: bool = False + freeze_flow_decoder: bool = False + freeze_waveform_decoder: bool = False + encoder_sample_rate: int = None + interpolate_z: bool = True + reinit_DP: bool = False + reinit_text_encoder: bool = False +@dataclass +class VitsConfig(BaseTTSConfig): + + model: str = "vits" + # model specific params + model_args: VitsArgs = field(default_factory=VitsArgs) + audio: VitsAudioConfig = field(default_factory=VitsAudioConfig) + + # optimizer + grad_clip: List[float] = field(default_factory=lambda: [1000, 1000]) + lr_gen: float = 0.0002 + lr_disc: float = 0.0002 + lr_scheduler_gen: str = "ExponentialLR" + lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + lr_scheduler_disc: str = "ExponentialLR" + lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + scheduler_after_epoch: bool = True + optimizer: str = "AdamW" + optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01}) + + # loss params + kl_loss_alpha: float = 1.0 + disc_loss_alpha: float = 1.0 + gen_loss_alpha: float = 1.0 + feat_loss_alpha: float = 1.0 + mel_loss_alpha: float = 45.0 + dur_loss_alpha: float = 1.0 + speaker_encoder_loss_alpha: float = 1.0 + + # data loader params + return_wav: bool = True + compute_linear_spec: bool = True + + # sampler params + use_weighted_sampler: bool = False # TODO: move it to the base config + weighted_sampler_attrs: dict = field(default_factory=lambda: {}) + weighted_sampler_multipliers: dict = field(default_factory=lambda: {}) + + # overrides + r: int = 1 # DO NOT CHANGE + add_blank: bool = True + + # testing + test_sentences: List[List] = field( + default_factory=lambda: [ + ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."], + ["Be a voice, not an echo."], + ["I'm sorry Dave. I'm afraid I can't do that."], + ["This cake is great. It's so delicious and moist."], + ["Prior to November 22, 1963."], + ] + ) + + # multi-speaker settings + # use speaker embedding layer + num_speakers: int = 0 + use_speaker_embedding: bool = False + speakers_file: str = None + speaker_embedding_channels: int = 256 + language_ids_file: str = None + use_language_embedding: bool = False + + # use d-vectors + use_d_vector_file: bool = False + d_vector_file: List[str] = None + d_vector_dim: int = None + + def __post_init__(self): + pass + # for key, val in self.model_args.items(): + # if hasattr(self, key): + # self[key] = val + + + + + +def parse_symbols(): + return { + "pad": _pad, + "eos": _eos, + "bos": _bos, + "characters": _characters, + "punctuations": _punctuations, + "phonemes": _phonemes, + } + + +# DEFAULT SET OF GRAPHEMES +_pad = "" +_eos = "" +_bos = "" +_blank = "" # TODO: check if we need this alongside with PAD +_characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +_punctuations = "!'(),-.:;? " + + +# DEFAULT SET OF IPA PHONEMES +# Phonemes definition (All IPA characters) +_vowels = "iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻ" +_non_pulmonic_consonants = "ʘɓǀɗǃʄǂɠǁʛ" +_pulmonic_consonants = "pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟ" +_suprasegmentals = "ˈˌːˑ" +_other_symbols = "ʍwɥʜʢʡɕʑɺɧʲ" +_diacrilics = "ɚ˞ɫ" +_phonemes = _vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics + + +class BaseVocabulary: + """Base Vocabulary class. + + This class only needs a vocabulary dictionary without specifying the characters. + + Args: + vocab (Dict): A dictionary of characters and their corresponding indices. + """ + + def __init__(self, vocab: Dict, pad: str = None, blank: str = None, bos: str = None, eos: str = None): + self.vocab = vocab + self.pad = pad + self.blank = blank + self.bos = bos + self.eos = eos + + @property + def pad_id(self) -> int: + """Return the index of the padding character. If the padding character is not specified, return the length + of the vocabulary.""" + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + """Return the index of the blank character. If the blank character is not specified, return the length of + the vocabulary.""" + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def bos_id(self) -> int: + """Return the index of the bos character. If the bos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def eos_id(self) -> int: + """Return the index of the eos character. If the eos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def vocab(self): + """Return the vocabulary dictionary.""" + return self._vocab + + @vocab.setter + def vocab(self, vocab): + """Set the vocabulary dictionary and character mapping dictionaries.""" + self._vocab, self._char_to_id, self._id_to_char = None, None, None + if vocab is not None: + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self._vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self._vocab) # pylint: disable=unnecessary-comprehension + } + + @staticmethod + def init_from_config(config, **kwargs): + """Initialize from the given config.""" + if config.characters is not None and "vocab_dict" in config.characters and config.characters.vocab_dict: + return ( + BaseVocabulary( + config.characters.vocab_dict, + config.characters.pad, + config.characters.blank, + config.characters.bos, + config.characters.eos, + ), + config, + ) + return BaseVocabulary(**kwargs), config + + def to_config(self): + return CharactersConfig( + vocab_dict=self._vocab, + pad=self.pad, + eos=self.eos, + bos=self.bos, + blank=self.blank, + is_unique=False, + is_sorted=False, + ) + + @property + def num_chars(self): + """Return number of tokens in the vocabulary.""" + return len(self._vocab) + + def char_to_id(self, char: str) -> int: + """Map a character to an token ID.""" + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + """Map an token ID to a character.""" + return self._id_to_char[idx] + + +class BaseCharacters: + + + def __init__( + self, + characters: str = None, + punctuations: str = None, + pad: str = None, + eos: str = None, + bos: str = None, + blank: str = None, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + self._characters = characters + self._punctuations = punctuations + self._pad = pad + self._eos = eos + self._bos = bos + self._blank = blank + self.is_unique = is_unique + self.is_sorted = is_sorted + self._create_vocab() + + @property + def pad_id(self) -> int: + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def eos_id(self) -> int: + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def bos_id(self) -> int: + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, characters): + self._characters = characters + self._create_vocab() + + @property + def punctuations(self): + return self._punctuations + + @punctuations.setter + def punctuations(self, punctuations): + self._punctuations = punctuations + self._create_vocab() + + @property + def pad(self): + return self._pad + + @pad.setter + def pad(self, pad): + self._pad = pad + self._create_vocab() + + @property + def eos(self): + return self._eos + + @eos.setter + def eos(self, eos): + self._eos = eos + self._create_vocab() + + @property + def bos(self): + return self._bos + + @bos.setter + def bos(self, bos): + self._bos = bos + self._create_vocab() + + @property + def blank(self): + return self._blank + + @blank.setter + def blank(self, blank): + self._blank = blank + self._create_vocab() + + @property + def vocab(self): + return self._vocab + + @vocab.setter + def vocab(self, vocab): + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self.vocab) # pylint: disable=unnecessary-comprehension + } + + @property + def num_chars(self): + return len(self._vocab) + + def _create_vocab(self): + _vocab = self._characters + if self.is_unique: + _vocab = list(set(_vocab)) + if self.is_sorted: + _vocab = sorted(_vocab) + _vocab = list(_vocab) + _vocab = [self._blank] + _vocab if self._blank is not None and len(self._blank) > 0 else _vocab + _vocab = [self._bos] + _vocab if self._bos is not None and len(self._bos) > 0 else _vocab + _vocab = [self._eos] + _vocab if self._eos is not None and len(self._eos) > 0 else _vocab + _vocab = [self._pad] + _vocab if self._pad is not None and len(self._pad) > 0 else _vocab + self.vocab = _vocab + list(self._punctuations) + if self.is_unique: + duplicates = {x for x in self.vocab if self.vocab.count(x) > 1} + assert ( + len(self.vocab) == len(self._char_to_id) == len(self._id_to_char) + ), f" [!] There are duplicate characters in the character set. {duplicates}" + + def char_to_id(self, char: str) -> int: + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + return self._id_to_char[idx] + + def print_log(self, level: int = 0): + """ + Prints the vocabulary in a nice format. + """ + indent = "\t" * level + print(f"{indent}| > Characters: {self._characters}") + print(f"{indent}| > Punctuations: {self._punctuations}") + print(f"{indent}| > Pad: {self._pad}") + print(f"{indent}| > EOS: {self._eos}") + print(f"{indent}| > BOS: {self._bos}") + print(f"{indent}| > Blank: {self._blank}") + print(f"{indent}| > Vocab: {self.vocab}") + print(f"{indent}| > Num chars: {self.num_chars}") + + @staticmethod + def init_from_config(config: "Coqpit"): # pylint: disable=unused-argument + """Init your character class from a config. + + Implement this method for your subclass. + """ + # use character set from config + if config.characters is not None: + return BaseCharacters(**config.characters), config + # return default character set + characters = BaseCharacters() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=self._eos, + bos=self._bos, + blank=self._blank, + is_unique=self.is_unique, + is_sorted=self.is_sorted, + ) + + +class IPAPhonemes(BaseCharacters): + + + def __init__( + self, + characters: str = _phonemes, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a IPAPhonemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + # band-aid for compatibility with old models + if "characters" in config and config.characters is not None: + if "phonemes" in config.characters and config.characters.phonemes is not None: + config.characters["characters"] = config.characters["phonemes"] + return ( + IPAPhonemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + # use character set from config + if config.characters is not None: + return IPAPhonemes(**config.characters), config + # return default character set + characters = IPAPhonemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +class Graphemes(BaseCharacters): + + + def __init__( + self, + characters: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a Graphemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + if config.characters is not None: + # band-aid for compatibility with old models + if "phonemes" in config.characters: + return ( + Graphemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + return Graphemes(**config.characters), config + characters = Graphemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +if __name__ == "__main__": + gr = Graphemes() + ph = IPAPhonemes() + gr.print_log() + ph.print_log() + + +class VitsCharacters(BaseCharacters): + """Characters class for VITs model for compatibility with pre-trained models""" + + def __init__( + self, + graphemes: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + ipa_characters: str = _phonemes, + ) -> None: + if ipa_characters is not None: + graphemes += ipa_characters + super().__init__(graphemes, punctuations, pad, None, None, "", is_unique=False, is_sorted=True) + + def _create_vocab(self): + self._vocab = [self._pad] + list(self._punctuations) + list(self._characters) + [self._blank] + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + # pylint: disable=unnecessary-comprehension + self._id_to_char = {idx: char for idx, char in enumerate(self.vocab)} + + @staticmethod + def init_from_config(config): + _pad = config.characters.pad + _punctuations = config.characters.punctuations + _letters = config.characters.characters + _letters_ipa = config.characters.phonemes + return ( + VitsCharacters(graphemes=_letters, ipa_characters=_letters_ipa, punctuations=_punctuations, pad=_pad), + config, + ) + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=None, + bos=None, + blank=self._blank, + is_unique=False, + is_sorted=True, + ) + +class TTSTokenizer: + def __init__( + self, + text_cleaner: Callable = None, + characters: "BaseCharacters" = None, + ): + self.text_cleaner = text_cleaner + self.characters = characters + self.not_found_characters = [] + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, new_characters): + self._characters = new_characters + self.pad_id = self.characters.char_to_id(self.characters.pad) if self.characters.pad else None + self.blank_id = self.characters.char_to_id(self.characters.blank) if self.characters.blank else None + + def encode(self, text: str) -> List[int]: + """Encodes a string of text as a sequence of IDs.""" + token_ids = [] + for char in text: + try: + idx = self.characters.char_to_id(char) + token_ids.append(idx) + except KeyError: + # discard but store not found characters + if char not in self.not_found_characters: + self.not_found_characters.append(char) + print(text) + print(f" [!] Character {repr(char)} not found in the vocabulary. Discarding it.") + return token_ids + + def text_to_ids(self, text: str, language: str = None) -> List[int]: # pylint: disable=unused-argument + text = self.text_cleaner(text) + text = self.encode(text) + text = self.intersperse_blank_char(text, True) + return text + + def pad_with_bos_eos(self, char_sequence: List[str]): + """Pads a sequence with the special BOS and EOS characters.""" + return [self.characters.bos_id] + list(char_sequence) + [self.characters.eos_id] + + def intersperse_blank_char(self, char_sequence: List[str], use_blank_char: bool = False): + """Intersperses the blank character between characters in a sequence. + + Use the ```blank``` character if defined else use the ```pad``` character. + """ + char_to_use = self.characters.blank_id if use_blank_char else self.characters.pad + result = [char_to_use] * (len(char_sequence) * 2 + 1) + result[1::2] = char_sequence + return result + + @staticmethod + def init_from_config(config: "Coqpit", characters: "BaseCharacters" = None): + text_cleaner = multilingual_cleaners + CharactersClass = VitsCharacters + characters, new_config = CharactersClass.init_from_config(config) + # new_config.characters.characters_class = get_import_path(characters) + new_config.characters.characters_class = VitsCharacters + return ( + TTSTokenizer(text_cleaner, characters),new_config) + + +def multilingual_cleaners(text): + """Pipeline for multilingual text""" + text = lowercase(text) + text = replace_symbols(text, lang=None) + text = remove_aux_symbols(text) + text = collapse_whitespace(text) + return text + +def lowercase(text): + return text.lower() + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text).strip() + +def replace_symbols(text, lang="en"): + + text = text.replace(";", ",") + text = text.replace("-", " ") if lang != "ca" else text.replace("-", "") + text = text.replace(":", ",") + if lang == "en": + text = text.replace("&", " and ") + elif lang == "fr": + text = text.replace("&", " et ") + elif lang == "pt": + text = text.replace("&", " e ") + elif lang == "ca": + text = text.replace("&", " i ") + text = text.replace("'", "") + return text + +def remove_aux_symbols(text): + text = re.sub(r"[\<\>\(\)\[\]\"]+", "", text) + return text \ No newline at end of file diff --git a/models/mag_female/jit_infer.py b/models/mag_female/jit_infer.py new file mode 100644 index 0000000000000000000000000000000000000000..1d7c1ad531f9be2a448f0befa3fb9f54fd8b1ddc --- /dev/null +++ b/models/mag_female/jit_infer.py @@ -0,0 +1,33 @@ +import os +from extra import TTSTokenizer, VitsConfig, CharactersConfig, VitsCharacters +import torch +import numpy as np + +#ch female +with open("chars.txt", 'r') as f: + letters = f.read().strip('\n') +model="mg_female_vits_30hrs.pt" +# text = " হলেও আমাদের সবার সার্বিক শৃঙ্খলা বোধের উন্নতি হবে" +text = "भेजना चाहते हैं हिंदी में मैसेज लेकिन नहीं आती टाइपिंग?" + +config = VitsConfig( + text_cleaner="multilingual_cleaners", + characters=CharactersConfig( + characters_class=VitsCharacters, + pad="", + eos="", + bos="", + blank="", + characters=letters, + punctuations="!¡'(),-.:;¿? ", + phonemes=None) + ) +tokenizer, config = TTSTokenizer.init_from_config(config) + +x = tokenizer.text_to_ids(text) +x = torch.from_numpy(np.array(x)).unsqueeze(0) +net = torch.jit.load(model) +with torch.no_grad(): + out2 = net(x) +import soundfile as sf +sf.write("jit.wav", out2.squeeze().cpu().numpy(), 22050) \ No newline at end of file diff --git a/models/mag_female/mg_female_vits_30hrs.pt b/models/mag_female/mg_female_vits_30hrs.pt new file mode 100644 index 0000000000000000000000000000000000000000..d9c539eae13ea21c733462502d3f47c8159d1e55 --- /dev/null +++ b/models/mag_female/mg_female_vits_30hrs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e4226ca6b9bc05a27ac4b1c37c53a3f58108bc4c1f1913f9a80d16f805428178 +size 333261318 diff --git a/models/mag_male/.gitattributes b/models/mag_male/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b --- /dev/null +++ b/models/mag_male/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/models/mag_male/README.md b/models/mag_male/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b187bb7e7d837a367ccd0862441947ad412c77f7 --- /dev/null +++ b/models/mag_male/README.md @@ -0,0 +1,3 @@ +--- +license: cc-by-4.0 +--- diff --git a/models/mag_male/chars.txt b/models/mag_male/chars.txt new file mode 100644 index 0000000000000000000000000000000000000000..6b97c3e31b3241d62d3752f00dba1d841c3696e2 --- /dev/null +++ b/models/mag_male/chars.txt @@ -0,0 +1 @@ +ओग?ढख़फबुथझ़क़.तङफ़घआरग़ोऊ'खअढ़णूईाीनौएषदेयडभछ, ंटवहइठमउ्धँॉपड़ऋ!औिऽकैऐऑशजृलञचसज़ diff --git a/models/mag_male/extra.py b/models/mag_male/extra.py new file mode 100644 index 0000000000000000000000000000000000000000..c7db561351da270a7c3931bfe0afefa7bc6d4853 --- /dev/null +++ b/models/mag_male/extra.py @@ -0,0 +1,787 @@ +from typing import Callable, Dict, List, Union +from dataclasses import asdict, dataclass, field + + +import re +from dataclasses import replace +from typing import Dict +_whitespace_re = re.compile(r"\s+") + +from dataclasses import dataclass, field +from typing import List + +# from TTS.tts.configs.shared_configs import BaseTTSConfig +# from TTS.tts.models.vits import VitsArgs, VitsAudioConfig + +@dataclass +class CharactersConfig(): + + characters_class: str = None + + # using BaseVocabulary + vocab_dict: Dict = None + + # using on BaseCharacters + pad: str = None + eos: str = None + bos: str = None + blank: str = None + characters: str = None + punctuations: str = None + phonemes: str = None + is_unique: bool = True # for backwards compatibility of models trained with char sets with duplicates + is_sorted: bool = True + + +@dataclass +class BaseTTSConfig(): + + # audio: BaseAudioConfig = field(default_factory=BaseAudioConfig) + # phoneme settings + use_phonemes: bool = False + phonemizer: str = None + phoneme_language: str = None + compute_input_seq_cache: bool = False + text_cleaner: str = None + enable_eos_bos_chars: bool = False + test_sentences_file: str = "" + phoneme_cache_path: str = None + # vocabulary parameters + characters: CharactersConfig = None + add_blank: bool = False + # training params + batch_group_size: int = 0 + loss_masking: bool = None + # dataloading + min_audio_len: int = 1 + max_audio_len: int = float("inf") + min_text_len: int = 1 + max_text_len: int = float("inf") + compute_f0: bool = False + compute_energy: bool = False + compute_linear_spec: bool = False + precompute_num_workers: int = 0 + use_noise_augment: bool = False + start_by_longest: bool = False + shuffle: bool = False + drop_last: bool = False + # dataset + datasets: str = None + # optimizer + optimizer: str = "radam" + optimizer_params: dict = None + # scheduler + lr_scheduler: str = None + lr_scheduler_params: dict = field(default_factory=lambda: {}) + # testing + test_sentences: List[str] = field(default_factory=lambda: []) + # evaluation + eval_split_max_size: int = None + eval_split_size: float = 0.01 + # weighted samplers + use_speaker_weighted_sampler: bool = False + speaker_weighted_sampler_alpha: float = 1.0 + use_language_weighted_sampler: bool = False + language_weighted_sampler_alpha: float = 1.0 + use_length_weighted_sampler: bool = False + length_weighted_sampler_alpha: float = 1.0 + + +@dataclass +class VitsAudioConfig(): + fft_size: int = 1024 + sample_rate: int = 22050 + win_length: int = 1024 + hop_length: int = 256 + num_mels: int = 80 + mel_fmin: int = 0 + mel_fmax: int = None + +@dataclass +class VitsArgs(): + num_chars: int = 100 + out_channels: int = 513 + spec_segment_size: int = 32 + hidden_channels: int = 192 + hidden_channels_ffn_text_encoder: int = 768 + num_heads_text_encoder: int = 2 + num_layers_text_encoder: int = 6 + kernel_size_text_encoder: int = 3 + dropout_p_text_encoder: float = 0.1 + dropout_p_duration_predictor: float = 0.5 + kernel_size_posterior_encoder: int = 5 + dilation_rate_posterior_encoder: int = 1 + num_layers_posterior_encoder: int = 16 + kernel_size_flow: int = 5 + dilation_rate_flow: int = 1 + num_layers_flow: int = 4 + resblock_type_decoder: str = "1" + resblock_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [3, 7, 11]) + resblock_dilation_sizes_decoder: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]) + upsample_rates_decoder: List[int] = field(default_factory=lambda: [8, 8, 2, 2]) + upsample_initial_channel_decoder: int = 512 + upsample_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [16, 16, 4, 4]) + periods_multi_period_discriminator: List[int] = field(default_factory=lambda: [2, 3, 5, 7, 11]) + use_sdp: bool = True + noise_scale: float = 1.0 + inference_noise_scale: float = 0.667 + length_scale: float = 1 + noise_scale_dp: float = 1.0 + inference_noise_scale_dp: float = 1.0 + max_inference_len: int = None + init_discriminator: bool = True + use_spectral_norm_disriminator: bool = False + use_speaker_embedding: bool = False + num_speakers: int = 0 + speakers_file: str = None + d_vector_file: List[str] = None + speaker_embedding_channels: int = 256 + use_d_vector_file: bool = False + d_vector_dim: int = 0 + detach_dp_input: bool = True + use_language_embedding: bool = False + embedded_language_dim: int = 4 + num_languages: int = 0 + language_ids_file: str = None + use_speaker_encoder_as_loss: bool = False + speaker_encoder_config_path: str = "" + speaker_encoder_model_path: str = "" + condition_dp_on_speaker: bool = True + freeze_encoder: bool = False + freeze_DP: bool = False + freeze_PE: bool = False + freeze_flow_decoder: bool = False + freeze_waveform_decoder: bool = False + encoder_sample_rate: int = None + interpolate_z: bool = True + reinit_DP: bool = False + reinit_text_encoder: bool = False +@dataclass +class VitsConfig(BaseTTSConfig): + + model: str = "vits" + # model specific params + model_args: VitsArgs = field(default_factory=VitsArgs) + audio: VitsAudioConfig = field(default_factory=VitsAudioConfig) + + # optimizer + grad_clip: List[float] = field(default_factory=lambda: [1000, 1000]) + lr_gen: float = 0.0002 + lr_disc: float = 0.0002 + lr_scheduler_gen: str = "ExponentialLR" + lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + lr_scheduler_disc: str = "ExponentialLR" + lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + scheduler_after_epoch: bool = True + optimizer: str = "AdamW" + optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01}) + + # loss params + kl_loss_alpha: float = 1.0 + disc_loss_alpha: float = 1.0 + gen_loss_alpha: float = 1.0 + feat_loss_alpha: float = 1.0 + mel_loss_alpha: float = 45.0 + dur_loss_alpha: float = 1.0 + speaker_encoder_loss_alpha: float = 1.0 + + # data loader params + return_wav: bool = True + compute_linear_spec: bool = True + + # sampler params + use_weighted_sampler: bool = False # TODO: move it to the base config + weighted_sampler_attrs: dict = field(default_factory=lambda: {}) + weighted_sampler_multipliers: dict = field(default_factory=lambda: {}) + + # overrides + r: int = 1 # DO NOT CHANGE + add_blank: bool = True + + # testing + test_sentences: List[List] = field( + default_factory=lambda: [ + ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."], + ["Be a voice, not an echo."], + ["I'm sorry Dave. I'm afraid I can't do that."], + ["This cake is great. It's so delicious and moist."], + ["Prior to November 22, 1963."], + ] + ) + + # multi-speaker settings + # use speaker embedding layer + num_speakers: int = 0 + use_speaker_embedding: bool = False + speakers_file: str = None + speaker_embedding_channels: int = 256 + language_ids_file: str = None + use_language_embedding: bool = False + + # use d-vectors + use_d_vector_file: bool = False + d_vector_file: List[str] = None + d_vector_dim: int = None + + def __post_init__(self): + pass + # for key, val in self.model_args.items(): + # if hasattr(self, key): + # self[key] = val + + + + + +def parse_symbols(): + return { + "pad": _pad, + "eos": _eos, + "bos": _bos, + "characters": _characters, + "punctuations": _punctuations, + "phonemes": _phonemes, + } + + +# DEFAULT SET OF GRAPHEMES +_pad = "" +_eos = "" +_bos = "" +_blank = "" # TODO: check if we need this alongside with PAD +_characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +_punctuations = "!'(),-.:;? " + + +# DEFAULT SET OF IPA PHONEMES +# Phonemes definition (All IPA characters) +_vowels = "iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻ" +_non_pulmonic_consonants = "ʘɓǀɗǃʄǂɠǁʛ" +_pulmonic_consonants = "pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟ" +_suprasegmentals = "ˈˌːˑ" +_other_symbols = "ʍwɥʜʢʡɕʑɺɧʲ" +_diacrilics = "ɚ˞ɫ" +_phonemes = _vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics + + +class BaseVocabulary: + """Base Vocabulary class. + + This class only needs a vocabulary dictionary without specifying the characters. + + Args: + vocab (Dict): A dictionary of characters and their corresponding indices. + """ + + def __init__(self, vocab: Dict, pad: str = None, blank: str = None, bos: str = None, eos: str = None): + self.vocab = vocab + self.pad = pad + self.blank = blank + self.bos = bos + self.eos = eos + + @property + def pad_id(self) -> int: + """Return the index of the padding character. If the padding character is not specified, return the length + of the vocabulary.""" + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + """Return the index of the blank character. If the blank character is not specified, return the length of + the vocabulary.""" + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def bos_id(self) -> int: + """Return the index of the bos character. If the bos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def eos_id(self) -> int: + """Return the index of the eos character. If the eos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def vocab(self): + """Return the vocabulary dictionary.""" + return self._vocab + + @vocab.setter + def vocab(self, vocab): + """Set the vocabulary dictionary and character mapping dictionaries.""" + self._vocab, self._char_to_id, self._id_to_char = None, None, None + if vocab is not None: + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self._vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self._vocab) # pylint: disable=unnecessary-comprehension + } + + @staticmethod + def init_from_config(config, **kwargs): + """Initialize from the given config.""" + if config.characters is not None and "vocab_dict" in config.characters and config.characters.vocab_dict: + return ( + BaseVocabulary( + config.characters.vocab_dict, + config.characters.pad, + config.characters.blank, + config.characters.bos, + config.characters.eos, + ), + config, + ) + return BaseVocabulary(**kwargs), config + + def to_config(self): + return CharactersConfig( + vocab_dict=self._vocab, + pad=self.pad, + eos=self.eos, + bos=self.bos, + blank=self.blank, + is_unique=False, + is_sorted=False, + ) + + @property + def num_chars(self): + """Return number of tokens in the vocabulary.""" + return len(self._vocab) + + def char_to_id(self, char: str) -> int: + """Map a character to an token ID.""" + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + """Map an token ID to a character.""" + return self._id_to_char[idx] + + +class BaseCharacters: + + + def __init__( + self, + characters: str = None, + punctuations: str = None, + pad: str = None, + eos: str = None, + bos: str = None, + blank: str = None, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + self._characters = characters + self._punctuations = punctuations + self._pad = pad + self._eos = eos + self._bos = bos + self._blank = blank + self.is_unique = is_unique + self.is_sorted = is_sorted + self._create_vocab() + + @property + def pad_id(self) -> int: + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def eos_id(self) -> int: + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def bos_id(self) -> int: + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, characters): + self._characters = characters + self._create_vocab() + + @property + def punctuations(self): + return self._punctuations + + @punctuations.setter + def punctuations(self, punctuations): + self._punctuations = punctuations + self._create_vocab() + + @property + def pad(self): + return self._pad + + @pad.setter + def pad(self, pad): + self._pad = pad + self._create_vocab() + + @property + def eos(self): + return self._eos + + @eos.setter + def eos(self, eos): + self._eos = eos + self._create_vocab() + + @property + def bos(self): + return self._bos + + @bos.setter + def bos(self, bos): + self._bos = bos + self._create_vocab() + + @property + def blank(self): + return self._blank + + @blank.setter + def blank(self, blank): + self._blank = blank + self._create_vocab() + + @property + def vocab(self): + return self._vocab + + @vocab.setter + def vocab(self, vocab): + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self.vocab) # pylint: disable=unnecessary-comprehension + } + + @property + def num_chars(self): + return len(self._vocab) + + def _create_vocab(self): + _vocab = self._characters + if self.is_unique: + _vocab = list(set(_vocab)) + if self.is_sorted: + _vocab = sorted(_vocab) + _vocab = list(_vocab) + _vocab = [self._blank] + _vocab if self._blank is not None and len(self._blank) > 0 else _vocab + _vocab = [self._bos] + _vocab if self._bos is not None and len(self._bos) > 0 else _vocab + _vocab = [self._eos] + _vocab if self._eos is not None and len(self._eos) > 0 else _vocab + _vocab = [self._pad] + _vocab if self._pad is not None and len(self._pad) > 0 else _vocab + self.vocab = _vocab + list(self._punctuations) + if self.is_unique: + duplicates = {x for x in self.vocab if self.vocab.count(x) > 1} + assert ( + len(self.vocab) == len(self._char_to_id) == len(self._id_to_char) + ), f" [!] There are duplicate characters in the character set. {duplicates}" + + def char_to_id(self, char: str) -> int: + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + return self._id_to_char[idx] + + def print_log(self, level: int = 0): + """ + Prints the vocabulary in a nice format. + """ + indent = "\t" * level + print(f"{indent}| > Characters: {self._characters}") + print(f"{indent}| > Punctuations: {self._punctuations}") + print(f"{indent}| > Pad: {self._pad}") + print(f"{indent}| > EOS: {self._eos}") + print(f"{indent}| > BOS: {self._bos}") + print(f"{indent}| > Blank: {self._blank}") + print(f"{indent}| > Vocab: {self.vocab}") + print(f"{indent}| > Num chars: {self.num_chars}") + + @staticmethod + def init_from_config(config: "Coqpit"): # pylint: disable=unused-argument + """Init your character class from a config. + + Implement this method for your subclass. + """ + # use character set from config + if config.characters is not None: + return BaseCharacters(**config.characters), config + # return default character set + characters = BaseCharacters() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=self._eos, + bos=self._bos, + blank=self._blank, + is_unique=self.is_unique, + is_sorted=self.is_sorted, + ) + + +class IPAPhonemes(BaseCharacters): + + + def __init__( + self, + characters: str = _phonemes, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a IPAPhonemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + # band-aid for compatibility with old models + if "characters" in config and config.characters is not None: + if "phonemes" in config.characters and config.characters.phonemes is not None: + config.characters["characters"] = config.characters["phonemes"] + return ( + IPAPhonemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + # use character set from config + if config.characters is not None: + return IPAPhonemes(**config.characters), config + # return default character set + characters = IPAPhonemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +class Graphemes(BaseCharacters): + + + def __init__( + self, + characters: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a Graphemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + if config.characters is not None: + # band-aid for compatibility with old models + if "phonemes" in config.characters: + return ( + Graphemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + return Graphemes(**config.characters), config + characters = Graphemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +if __name__ == "__main__": + gr = Graphemes() + ph = IPAPhonemes() + gr.print_log() + ph.print_log() + + +class VitsCharacters(BaseCharacters): + """Characters class for VITs model for compatibility with pre-trained models""" + + def __init__( + self, + graphemes: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + ipa_characters: str = _phonemes, + ) -> None: + if ipa_characters is not None: + graphemes += ipa_characters + super().__init__(graphemes, punctuations, pad, None, None, "", is_unique=False, is_sorted=True) + + def _create_vocab(self): + self._vocab = [self._pad] + list(self._punctuations) + list(self._characters) + [self._blank] + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + # pylint: disable=unnecessary-comprehension + self._id_to_char = {idx: char for idx, char in enumerate(self.vocab)} + + @staticmethod + def init_from_config(config): + _pad = config.characters.pad + _punctuations = config.characters.punctuations + _letters = config.characters.characters + _letters_ipa = config.characters.phonemes + return ( + VitsCharacters(graphemes=_letters, ipa_characters=_letters_ipa, punctuations=_punctuations, pad=_pad), + config, + ) + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=None, + bos=None, + blank=self._blank, + is_unique=False, + is_sorted=True, + ) + +class TTSTokenizer: + def __init__( + self, + text_cleaner: Callable = None, + characters: "BaseCharacters" = None, + ): + self.text_cleaner = text_cleaner + self.characters = characters + self.not_found_characters = [] + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, new_characters): + self._characters = new_characters + self.pad_id = self.characters.char_to_id(self.characters.pad) if self.characters.pad else None + self.blank_id = self.characters.char_to_id(self.characters.blank) if self.characters.blank else None + + def encode(self, text: str) -> List[int]: + """Encodes a string of text as a sequence of IDs.""" + token_ids = [] + for char in text: + try: + idx = self.characters.char_to_id(char) + token_ids.append(idx) + except KeyError: + # discard but store not found characters + if char not in self.not_found_characters: + self.not_found_characters.append(char) + print(text) + print(f" [!] Character {repr(char)} not found in the vocabulary. Discarding it.") + return token_ids + + def text_to_ids(self, text: str, language: str = None) -> List[int]: # pylint: disable=unused-argument + text = self.text_cleaner(text) + text = self.encode(text) + text = self.intersperse_blank_char(text, True) + return text + + def pad_with_bos_eos(self, char_sequence: List[str]): + """Pads a sequence with the special BOS and EOS characters.""" + return [self.characters.bos_id] + list(char_sequence) + [self.characters.eos_id] + + def intersperse_blank_char(self, char_sequence: List[str], use_blank_char: bool = False): + """Intersperses the blank character between characters in a sequence. + + Use the ```blank``` character if defined else use the ```pad``` character. + """ + char_to_use = self.characters.blank_id if use_blank_char else self.characters.pad + result = [char_to_use] * (len(char_sequence) * 2 + 1) + result[1::2] = char_sequence + return result + + @staticmethod + def init_from_config(config: "Coqpit", characters: "BaseCharacters" = None): + text_cleaner = multilingual_cleaners + CharactersClass = VitsCharacters + characters, new_config = CharactersClass.init_from_config(config) + # new_config.characters.characters_class = get_import_path(characters) + new_config.characters.characters_class = VitsCharacters + return ( + TTSTokenizer(text_cleaner, characters),new_config) + + +def multilingual_cleaners(text): + """Pipeline for multilingual text""" + text = lowercase(text) + text = replace_symbols(text, lang=None) + text = remove_aux_symbols(text) + text = collapse_whitespace(text) + return text + +def lowercase(text): + return text.lower() + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text).strip() + +def replace_symbols(text, lang="en"): + + text = text.replace(";", ",") + text = text.replace("-", " ") if lang != "ca" else text.replace("-", "") + text = text.replace(":", ",") + if lang == "en": + text = text.replace("&", " and ") + elif lang == "fr": + text = text.replace("&", " et ") + elif lang == "pt": + text = text.replace("&", " e ") + elif lang == "ca": + text = text.replace("&", " i ") + text = text.replace("'", "") + return text + +def remove_aux_symbols(text): + text = re.sub(r"[\<\>\(\)\[\]\"]+", "", text) + return text \ No newline at end of file diff --git a/models/mag_male/jit_infer.py b/models/mag_male/jit_infer.py new file mode 100644 index 0000000000000000000000000000000000000000..10aea2af3b5cdd72b05e8489ba41239ce40f1b61 --- /dev/null +++ b/models/mag_male/jit_infer.py @@ -0,0 +1,33 @@ +import os +from extra import TTSTokenizer, VitsConfig, CharactersConfig, VitsCharacters +import torch +import numpy as np + +#ch female +with open("chars.txt", 'r') as f: + letters = f.read().strip('\n') +model="mg_male_vits_30hrs.pt" +# text = " হলেও আমাদের সবার সার্বিক শৃঙ্খলা বোধের উন্নতি হবে" +text = "भेजना चाहते हैं हिंदी में मैसेज लेकिन नहीं आती टाइपिंग?" + +config = VitsConfig( + text_cleaner="multilingual_cleaners", + characters=CharactersConfig( + characters_class=VitsCharacters, + pad="", + eos="", + bos="", + blank="", + characters=letters, + punctuations="!¡'(),-.:;¿? ", + phonemes=None) + ) +tokenizer, config = TTSTokenizer.init_from_config(config) + +x = tokenizer.text_to_ids(text) +x = torch.from_numpy(np.array(x)).unsqueeze(0) +net = torch.jit.load(model) +with torch.no_grad(): + out2 = net(x) +import soundfile as sf +sf.write("jit.wav", out2.squeeze().cpu().numpy(), 22050) \ No newline at end of file diff --git a/models/mag_male/mg_male_vits_30hrs.pt b/models/mag_male/mg_male_vits_30hrs.pt new file mode 100644 index 0000000000000000000000000000000000000000..3aae249c61bd7858595e06333ac92ae7987a0f35 --- /dev/null +++ b/models/mag_male/mg_male_vits_30hrs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4e4105200532fde9960eb22756d393aacbbb21b04adb89e4eb681f4ff4f4e7ed +size 333255692 diff --git a/models/mai_female/.gitattributes b/models/mai_female/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b --- /dev/null +++ b/models/mai_female/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/models/mai_female/README.md b/models/mai_female/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b187bb7e7d837a367ccd0862441947ad412c77f7 --- /dev/null +++ b/models/mai_female/README.md @@ -0,0 +1,3 @@ +--- +license: cc-by-4.0 +--- diff --git a/models/mai_female/chars.txt b/models/mai_female/chars.txt new file mode 100644 index 0000000000000000000000000000000000000000..5d1e3710e0214de271c1902b987c7de333438aaf --- /dev/null +++ b/models/mai_female/chars.txt @@ -0,0 +1 @@ +गफ़ङोऊूौइँउठऋऐऑलञढग़अखणयछ,फ़वमड़औिशचज़ओ?थतॅढ़ाीनदॊभडटधऽैख़क़बुझ.घआर॑'ईएषे ंह्ॠॉप!कजृस diff --git a/models/mai_female/extra.py b/models/mai_female/extra.py new file mode 100644 index 0000000000000000000000000000000000000000..c7db561351da270a7c3931bfe0afefa7bc6d4853 --- /dev/null +++ b/models/mai_female/extra.py @@ -0,0 +1,787 @@ +from typing import Callable, Dict, List, Union +from dataclasses import asdict, dataclass, field + + +import re +from dataclasses import replace +from typing import Dict +_whitespace_re = re.compile(r"\s+") + +from dataclasses import dataclass, field +from typing import List + +# from TTS.tts.configs.shared_configs import BaseTTSConfig +# from TTS.tts.models.vits import VitsArgs, VitsAudioConfig + +@dataclass +class CharactersConfig(): + + characters_class: str = None + + # using BaseVocabulary + vocab_dict: Dict = None + + # using on BaseCharacters + pad: str = None + eos: str = None + bos: str = None + blank: str = None + characters: str = None + punctuations: str = None + phonemes: str = None + is_unique: bool = True # for backwards compatibility of models trained with char sets with duplicates + is_sorted: bool = True + + +@dataclass +class BaseTTSConfig(): + + # audio: BaseAudioConfig = field(default_factory=BaseAudioConfig) + # phoneme settings + use_phonemes: bool = False + phonemizer: str = None + phoneme_language: str = None + compute_input_seq_cache: bool = False + text_cleaner: str = None + enable_eos_bos_chars: bool = False + test_sentences_file: str = "" + phoneme_cache_path: str = None + # vocabulary parameters + characters: CharactersConfig = None + add_blank: bool = False + # training params + batch_group_size: int = 0 + loss_masking: bool = None + # dataloading + min_audio_len: int = 1 + max_audio_len: int = float("inf") + min_text_len: int = 1 + max_text_len: int = float("inf") + compute_f0: bool = False + compute_energy: bool = False + compute_linear_spec: bool = False + precompute_num_workers: int = 0 + use_noise_augment: bool = False + start_by_longest: bool = False + shuffle: bool = False + drop_last: bool = False + # dataset + datasets: str = None + # optimizer + optimizer: str = "radam" + optimizer_params: dict = None + # scheduler + lr_scheduler: str = None + lr_scheduler_params: dict = field(default_factory=lambda: {}) + # testing + test_sentences: List[str] = field(default_factory=lambda: []) + # evaluation + eval_split_max_size: int = None + eval_split_size: float = 0.01 + # weighted samplers + use_speaker_weighted_sampler: bool = False + speaker_weighted_sampler_alpha: float = 1.0 + use_language_weighted_sampler: bool = False + language_weighted_sampler_alpha: float = 1.0 + use_length_weighted_sampler: bool = False + length_weighted_sampler_alpha: float = 1.0 + + +@dataclass +class VitsAudioConfig(): + fft_size: int = 1024 + sample_rate: int = 22050 + win_length: int = 1024 + hop_length: int = 256 + num_mels: int = 80 + mel_fmin: int = 0 + mel_fmax: int = None + +@dataclass +class VitsArgs(): + num_chars: int = 100 + out_channels: int = 513 + spec_segment_size: int = 32 + hidden_channels: int = 192 + hidden_channels_ffn_text_encoder: int = 768 + num_heads_text_encoder: int = 2 + num_layers_text_encoder: int = 6 + kernel_size_text_encoder: int = 3 + dropout_p_text_encoder: float = 0.1 + dropout_p_duration_predictor: float = 0.5 + kernel_size_posterior_encoder: int = 5 + dilation_rate_posterior_encoder: int = 1 + num_layers_posterior_encoder: int = 16 + kernel_size_flow: int = 5 + dilation_rate_flow: int = 1 + num_layers_flow: int = 4 + resblock_type_decoder: str = "1" + resblock_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [3, 7, 11]) + resblock_dilation_sizes_decoder: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]) + upsample_rates_decoder: List[int] = field(default_factory=lambda: [8, 8, 2, 2]) + upsample_initial_channel_decoder: int = 512 + upsample_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [16, 16, 4, 4]) + periods_multi_period_discriminator: List[int] = field(default_factory=lambda: [2, 3, 5, 7, 11]) + use_sdp: bool = True + noise_scale: float = 1.0 + inference_noise_scale: float = 0.667 + length_scale: float = 1 + noise_scale_dp: float = 1.0 + inference_noise_scale_dp: float = 1.0 + max_inference_len: int = None + init_discriminator: bool = True + use_spectral_norm_disriminator: bool = False + use_speaker_embedding: bool = False + num_speakers: int = 0 + speakers_file: str = None + d_vector_file: List[str] = None + speaker_embedding_channels: int = 256 + use_d_vector_file: bool = False + d_vector_dim: int = 0 + detach_dp_input: bool = True + use_language_embedding: bool = False + embedded_language_dim: int = 4 + num_languages: int = 0 + language_ids_file: str = None + use_speaker_encoder_as_loss: bool = False + speaker_encoder_config_path: str = "" + speaker_encoder_model_path: str = "" + condition_dp_on_speaker: bool = True + freeze_encoder: bool = False + freeze_DP: bool = False + freeze_PE: bool = False + freeze_flow_decoder: bool = False + freeze_waveform_decoder: bool = False + encoder_sample_rate: int = None + interpolate_z: bool = True + reinit_DP: bool = False + reinit_text_encoder: bool = False +@dataclass +class VitsConfig(BaseTTSConfig): + + model: str = "vits" + # model specific params + model_args: VitsArgs = field(default_factory=VitsArgs) + audio: VitsAudioConfig = field(default_factory=VitsAudioConfig) + + # optimizer + grad_clip: List[float] = field(default_factory=lambda: [1000, 1000]) + lr_gen: float = 0.0002 + lr_disc: float = 0.0002 + lr_scheduler_gen: str = "ExponentialLR" + lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + lr_scheduler_disc: str = "ExponentialLR" + lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + scheduler_after_epoch: bool = True + optimizer: str = "AdamW" + optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01}) + + # loss params + kl_loss_alpha: float = 1.0 + disc_loss_alpha: float = 1.0 + gen_loss_alpha: float = 1.0 + feat_loss_alpha: float = 1.0 + mel_loss_alpha: float = 45.0 + dur_loss_alpha: float = 1.0 + speaker_encoder_loss_alpha: float = 1.0 + + # data loader params + return_wav: bool = True + compute_linear_spec: bool = True + + # sampler params + use_weighted_sampler: bool = False # TODO: move it to the base config + weighted_sampler_attrs: dict = field(default_factory=lambda: {}) + weighted_sampler_multipliers: dict = field(default_factory=lambda: {}) + + # overrides + r: int = 1 # DO NOT CHANGE + add_blank: bool = True + + # testing + test_sentences: List[List] = field( + default_factory=lambda: [ + ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."], + ["Be a voice, not an echo."], + ["I'm sorry Dave. I'm afraid I can't do that."], + ["This cake is great. It's so delicious and moist."], + ["Prior to November 22, 1963."], + ] + ) + + # multi-speaker settings + # use speaker embedding layer + num_speakers: int = 0 + use_speaker_embedding: bool = False + speakers_file: str = None + speaker_embedding_channels: int = 256 + language_ids_file: str = None + use_language_embedding: bool = False + + # use d-vectors + use_d_vector_file: bool = False + d_vector_file: List[str] = None + d_vector_dim: int = None + + def __post_init__(self): + pass + # for key, val in self.model_args.items(): + # if hasattr(self, key): + # self[key] = val + + + + + +def parse_symbols(): + return { + "pad": _pad, + "eos": _eos, + "bos": _bos, + "characters": _characters, + "punctuations": _punctuations, + "phonemes": _phonemes, + } + + +# DEFAULT SET OF GRAPHEMES +_pad = "" +_eos = "" +_bos = "" +_blank = "" # TODO: check if we need this alongside with PAD +_characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +_punctuations = "!'(),-.:;? " + + +# DEFAULT SET OF IPA PHONEMES +# Phonemes definition (All IPA characters) +_vowels = "iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻ" +_non_pulmonic_consonants = "ʘɓǀɗǃʄǂɠǁʛ" +_pulmonic_consonants = "pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟ" +_suprasegmentals = "ˈˌːˑ" +_other_symbols = "ʍwɥʜʢʡɕʑɺɧʲ" +_diacrilics = "ɚ˞ɫ" +_phonemes = _vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics + + +class BaseVocabulary: + """Base Vocabulary class. + + This class only needs a vocabulary dictionary without specifying the characters. + + Args: + vocab (Dict): A dictionary of characters and their corresponding indices. + """ + + def __init__(self, vocab: Dict, pad: str = None, blank: str = None, bos: str = None, eos: str = None): + self.vocab = vocab + self.pad = pad + self.blank = blank + self.bos = bos + self.eos = eos + + @property + def pad_id(self) -> int: + """Return the index of the padding character. If the padding character is not specified, return the length + of the vocabulary.""" + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + """Return the index of the blank character. If the blank character is not specified, return the length of + the vocabulary.""" + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def bos_id(self) -> int: + """Return the index of the bos character. If the bos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def eos_id(self) -> int: + """Return the index of the eos character. If the eos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def vocab(self): + """Return the vocabulary dictionary.""" + return self._vocab + + @vocab.setter + def vocab(self, vocab): + """Set the vocabulary dictionary and character mapping dictionaries.""" + self._vocab, self._char_to_id, self._id_to_char = None, None, None + if vocab is not None: + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self._vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self._vocab) # pylint: disable=unnecessary-comprehension + } + + @staticmethod + def init_from_config(config, **kwargs): + """Initialize from the given config.""" + if config.characters is not None and "vocab_dict" in config.characters and config.characters.vocab_dict: + return ( + BaseVocabulary( + config.characters.vocab_dict, + config.characters.pad, + config.characters.blank, + config.characters.bos, + config.characters.eos, + ), + config, + ) + return BaseVocabulary(**kwargs), config + + def to_config(self): + return CharactersConfig( + vocab_dict=self._vocab, + pad=self.pad, + eos=self.eos, + bos=self.bos, + blank=self.blank, + is_unique=False, + is_sorted=False, + ) + + @property + def num_chars(self): + """Return number of tokens in the vocabulary.""" + return len(self._vocab) + + def char_to_id(self, char: str) -> int: + """Map a character to an token ID.""" + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + """Map an token ID to a character.""" + return self._id_to_char[idx] + + +class BaseCharacters: + + + def __init__( + self, + characters: str = None, + punctuations: str = None, + pad: str = None, + eos: str = None, + bos: str = None, + blank: str = None, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + self._characters = characters + self._punctuations = punctuations + self._pad = pad + self._eos = eos + self._bos = bos + self._blank = blank + self.is_unique = is_unique + self.is_sorted = is_sorted + self._create_vocab() + + @property + def pad_id(self) -> int: + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def eos_id(self) -> int: + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def bos_id(self) -> int: + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, characters): + self._characters = characters + self._create_vocab() + + @property + def punctuations(self): + return self._punctuations + + @punctuations.setter + def punctuations(self, punctuations): + self._punctuations = punctuations + self._create_vocab() + + @property + def pad(self): + return self._pad + + @pad.setter + def pad(self, pad): + self._pad = pad + self._create_vocab() + + @property + def eos(self): + return self._eos + + @eos.setter + def eos(self, eos): + self._eos = eos + self._create_vocab() + + @property + def bos(self): + return self._bos + + @bos.setter + def bos(self, bos): + self._bos = bos + self._create_vocab() + + @property + def blank(self): + return self._blank + + @blank.setter + def blank(self, blank): + self._blank = blank + self._create_vocab() + + @property + def vocab(self): + return self._vocab + + @vocab.setter + def vocab(self, vocab): + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self.vocab) # pylint: disable=unnecessary-comprehension + } + + @property + def num_chars(self): + return len(self._vocab) + + def _create_vocab(self): + _vocab = self._characters + if self.is_unique: + _vocab = list(set(_vocab)) + if self.is_sorted: + _vocab = sorted(_vocab) + _vocab = list(_vocab) + _vocab = [self._blank] + _vocab if self._blank is not None and len(self._blank) > 0 else _vocab + _vocab = [self._bos] + _vocab if self._bos is not None and len(self._bos) > 0 else _vocab + _vocab = [self._eos] + _vocab if self._eos is not None and len(self._eos) > 0 else _vocab + _vocab = [self._pad] + _vocab if self._pad is not None and len(self._pad) > 0 else _vocab + self.vocab = _vocab + list(self._punctuations) + if self.is_unique: + duplicates = {x for x in self.vocab if self.vocab.count(x) > 1} + assert ( + len(self.vocab) == len(self._char_to_id) == len(self._id_to_char) + ), f" [!] There are duplicate characters in the character set. {duplicates}" + + def char_to_id(self, char: str) -> int: + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + return self._id_to_char[idx] + + def print_log(self, level: int = 0): + """ + Prints the vocabulary in a nice format. + """ + indent = "\t" * level + print(f"{indent}| > Characters: {self._characters}") + print(f"{indent}| > Punctuations: {self._punctuations}") + print(f"{indent}| > Pad: {self._pad}") + print(f"{indent}| > EOS: {self._eos}") + print(f"{indent}| > BOS: {self._bos}") + print(f"{indent}| > Blank: {self._blank}") + print(f"{indent}| > Vocab: {self.vocab}") + print(f"{indent}| > Num chars: {self.num_chars}") + + @staticmethod + def init_from_config(config: "Coqpit"): # pylint: disable=unused-argument + """Init your character class from a config. + + Implement this method for your subclass. + """ + # use character set from config + if config.characters is not None: + return BaseCharacters(**config.characters), config + # return default character set + characters = BaseCharacters() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=self._eos, + bos=self._bos, + blank=self._blank, + is_unique=self.is_unique, + is_sorted=self.is_sorted, + ) + + +class IPAPhonemes(BaseCharacters): + + + def __init__( + self, + characters: str = _phonemes, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a IPAPhonemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + # band-aid for compatibility with old models + if "characters" in config and config.characters is not None: + if "phonemes" in config.characters and config.characters.phonemes is not None: + config.characters["characters"] = config.characters["phonemes"] + return ( + IPAPhonemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + # use character set from config + if config.characters is not None: + return IPAPhonemes(**config.characters), config + # return default character set + characters = IPAPhonemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +class Graphemes(BaseCharacters): + + + def __init__( + self, + characters: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a Graphemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + if config.characters is not None: + # band-aid for compatibility with old models + if "phonemes" in config.characters: + return ( + Graphemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + return Graphemes(**config.characters), config + characters = Graphemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +if __name__ == "__main__": + gr = Graphemes() + ph = IPAPhonemes() + gr.print_log() + ph.print_log() + + +class VitsCharacters(BaseCharacters): + """Characters class for VITs model for compatibility with pre-trained models""" + + def __init__( + self, + graphemes: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + ipa_characters: str = _phonemes, + ) -> None: + if ipa_characters is not None: + graphemes += ipa_characters + super().__init__(graphemes, punctuations, pad, None, None, "", is_unique=False, is_sorted=True) + + def _create_vocab(self): + self._vocab = [self._pad] + list(self._punctuations) + list(self._characters) + [self._blank] + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + # pylint: disable=unnecessary-comprehension + self._id_to_char = {idx: char for idx, char in enumerate(self.vocab)} + + @staticmethod + def init_from_config(config): + _pad = config.characters.pad + _punctuations = config.characters.punctuations + _letters = config.characters.characters + _letters_ipa = config.characters.phonemes + return ( + VitsCharacters(graphemes=_letters, ipa_characters=_letters_ipa, punctuations=_punctuations, pad=_pad), + config, + ) + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=None, + bos=None, + blank=self._blank, + is_unique=False, + is_sorted=True, + ) + +class TTSTokenizer: + def __init__( + self, + text_cleaner: Callable = None, + characters: "BaseCharacters" = None, + ): + self.text_cleaner = text_cleaner + self.characters = characters + self.not_found_characters = [] + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, new_characters): + self._characters = new_characters + self.pad_id = self.characters.char_to_id(self.characters.pad) if self.characters.pad else None + self.blank_id = self.characters.char_to_id(self.characters.blank) if self.characters.blank else None + + def encode(self, text: str) -> List[int]: + """Encodes a string of text as a sequence of IDs.""" + token_ids = [] + for char in text: + try: + idx = self.characters.char_to_id(char) + token_ids.append(idx) + except KeyError: + # discard but store not found characters + if char not in self.not_found_characters: + self.not_found_characters.append(char) + print(text) + print(f" [!] Character {repr(char)} not found in the vocabulary. Discarding it.") + return token_ids + + def text_to_ids(self, text: str, language: str = None) -> List[int]: # pylint: disable=unused-argument + text = self.text_cleaner(text) + text = self.encode(text) + text = self.intersperse_blank_char(text, True) + return text + + def pad_with_bos_eos(self, char_sequence: List[str]): + """Pads a sequence with the special BOS and EOS characters.""" + return [self.characters.bos_id] + list(char_sequence) + [self.characters.eos_id] + + def intersperse_blank_char(self, char_sequence: List[str], use_blank_char: bool = False): + """Intersperses the blank character between characters in a sequence. + + Use the ```blank``` character if defined else use the ```pad``` character. + """ + char_to_use = self.characters.blank_id if use_blank_char else self.characters.pad + result = [char_to_use] * (len(char_sequence) * 2 + 1) + result[1::2] = char_sequence + return result + + @staticmethod + def init_from_config(config: "Coqpit", characters: "BaseCharacters" = None): + text_cleaner = multilingual_cleaners + CharactersClass = VitsCharacters + characters, new_config = CharactersClass.init_from_config(config) + # new_config.characters.characters_class = get_import_path(characters) + new_config.characters.characters_class = VitsCharacters + return ( + TTSTokenizer(text_cleaner, characters),new_config) + + +def multilingual_cleaners(text): + """Pipeline for multilingual text""" + text = lowercase(text) + text = replace_symbols(text, lang=None) + text = remove_aux_symbols(text) + text = collapse_whitespace(text) + return text + +def lowercase(text): + return text.lower() + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text).strip() + +def replace_symbols(text, lang="en"): + + text = text.replace(";", ",") + text = text.replace("-", " ") if lang != "ca" else text.replace("-", "") + text = text.replace(":", ",") + if lang == "en": + text = text.replace("&", " and ") + elif lang == "fr": + text = text.replace("&", " et ") + elif lang == "pt": + text = text.replace("&", " e ") + elif lang == "ca": + text = text.replace("&", " i ") + text = text.replace("'", "") + return text + +def remove_aux_symbols(text): + text = re.sub(r"[\<\>\(\)\[\]\"]+", "", text) + return text \ No newline at end of file diff --git a/models/mai_female/jit_infer.py b/models/mai_female/jit_infer.py new file mode 100644 index 0000000000000000000000000000000000000000..8ee233d792b9751d0ca9ed91b18cac9d3bfa0543 --- /dev/null +++ b/models/mai_female/jit_infer.py @@ -0,0 +1,34 @@ +import os +from extra import TTSTokenizer, VitsConfig, CharactersConfig, VitsCharacters +import torch +import numpy as np + +#ch female +with open("chars.txt", 'r') as f: + letters = f.read().strip('\n') +model="mt_female_vits_30hrs.pt" +# text = " হলেও আমাদের সবার সার্বিক শৃঙ্খলা বোধের উন্নতি হবে" +text = "भेजना चाहते हैं हिंदी में मैसेज लेकिन नहीं आती टाइपिंग?" + + +config = VitsConfig( + text_cleaner="multilingual_cleaners", + characters=CharactersConfig( + characters_class=VitsCharacters, + pad="", + eos="", + bos="", + blank="", + characters=letters, + punctuations="!¡'(),-.:;¿? ", + phonemes=None) + ) +tokenizer, config = TTSTokenizer.init_from_config(config) + +x = tokenizer.text_to_ids(text) +x = torch.from_numpy(np.array(x)).unsqueeze(0) +net = torch.jit.load(model) +with torch.no_grad(): + out2 = net(x) +import soundfile as sf +sf.write("jit.wav", out2.squeeze().cpu().numpy(), 22050) \ No newline at end of file diff --git a/models/mai_female/mt_female_vits_30hrs.pt b/models/mai_female/mt_female_vits_30hrs.pt new file mode 100644 index 0000000000000000000000000000000000000000..2a85de66917240f1e6179b07702eafd86dbf376d --- /dev/null +++ b/models/mai_female/mt_female_vits_30hrs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8e8099493e5e8d2267e5dfea922afeef1c8c680c8372480f3bbd15218f60f7d6 +size 333264262 diff --git a/models/mai_male/.gitattributes b/models/mai_male/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b --- /dev/null +++ b/models/mai_male/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/models/mai_male/README.md b/models/mai_male/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b187bb7e7d837a367ccd0862441947ad412c77f7 --- /dev/null +++ b/models/mai_male/README.md @@ -0,0 +1,3 @@ +--- +license: cc-by-4.0 +--- diff --git a/models/mai_male/chars.txt b/models/mai_male/chars.txt new file mode 100644 index 0000000000000000000000000000000000000000..955f58edee34cb606d7818c30ff79d99367f8644 --- /dev/null +++ b/models/mai_male/chars.txt @@ -0,0 +1 @@ +गफ़ङोऊूौइँउठऋऑऐलञढग़खअणयछ,फ़वमड़औिशचज़ओ?थतॅढ़ीानदभडटधऽैख़क़बुझ.घआर॑'ईएषे" ंह्ॠॉप!कजृस diff --git a/models/mai_male/extra.py b/models/mai_male/extra.py new file mode 100644 index 0000000000000000000000000000000000000000..c7db561351da270a7c3931bfe0afefa7bc6d4853 --- /dev/null +++ b/models/mai_male/extra.py @@ -0,0 +1,787 @@ +from typing import Callable, Dict, List, Union +from dataclasses import asdict, dataclass, field + + +import re +from dataclasses import replace +from typing import Dict +_whitespace_re = re.compile(r"\s+") + +from dataclasses import dataclass, field +from typing import List + +# from TTS.tts.configs.shared_configs import BaseTTSConfig +# from TTS.tts.models.vits import VitsArgs, VitsAudioConfig + +@dataclass +class CharactersConfig(): + + characters_class: str = None + + # using BaseVocabulary + vocab_dict: Dict = None + + # using on BaseCharacters + pad: str = None + eos: str = None + bos: str = None + blank: str = None + characters: str = None + punctuations: str = None + phonemes: str = None + is_unique: bool = True # for backwards compatibility of models trained with char sets with duplicates + is_sorted: bool = True + + +@dataclass +class BaseTTSConfig(): + + # audio: BaseAudioConfig = field(default_factory=BaseAudioConfig) + # phoneme settings + use_phonemes: bool = False + phonemizer: str = None + phoneme_language: str = None + compute_input_seq_cache: bool = False + text_cleaner: str = None + enable_eos_bos_chars: bool = False + test_sentences_file: str = "" + phoneme_cache_path: str = None + # vocabulary parameters + characters: CharactersConfig = None + add_blank: bool = False + # training params + batch_group_size: int = 0 + loss_masking: bool = None + # dataloading + min_audio_len: int = 1 + max_audio_len: int = float("inf") + min_text_len: int = 1 + max_text_len: int = float("inf") + compute_f0: bool = False + compute_energy: bool = False + compute_linear_spec: bool = False + precompute_num_workers: int = 0 + use_noise_augment: bool = False + start_by_longest: bool = False + shuffle: bool = False + drop_last: bool = False + # dataset + datasets: str = None + # optimizer + optimizer: str = "radam" + optimizer_params: dict = None + # scheduler + lr_scheduler: str = None + lr_scheduler_params: dict = field(default_factory=lambda: {}) + # testing + test_sentences: List[str] = field(default_factory=lambda: []) + # evaluation + eval_split_max_size: int = None + eval_split_size: float = 0.01 + # weighted samplers + use_speaker_weighted_sampler: bool = False + speaker_weighted_sampler_alpha: float = 1.0 + use_language_weighted_sampler: bool = False + language_weighted_sampler_alpha: float = 1.0 + use_length_weighted_sampler: bool = False + length_weighted_sampler_alpha: float = 1.0 + + +@dataclass +class VitsAudioConfig(): + fft_size: int = 1024 + sample_rate: int = 22050 + win_length: int = 1024 + hop_length: int = 256 + num_mels: int = 80 + mel_fmin: int = 0 + mel_fmax: int = None + +@dataclass +class VitsArgs(): + num_chars: int = 100 + out_channels: int = 513 + spec_segment_size: int = 32 + hidden_channels: int = 192 + hidden_channels_ffn_text_encoder: int = 768 + num_heads_text_encoder: int = 2 + num_layers_text_encoder: int = 6 + kernel_size_text_encoder: int = 3 + dropout_p_text_encoder: float = 0.1 + dropout_p_duration_predictor: float = 0.5 + kernel_size_posterior_encoder: int = 5 + dilation_rate_posterior_encoder: int = 1 + num_layers_posterior_encoder: int = 16 + kernel_size_flow: int = 5 + dilation_rate_flow: int = 1 + num_layers_flow: int = 4 + resblock_type_decoder: str = "1" + resblock_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [3, 7, 11]) + resblock_dilation_sizes_decoder: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]) + upsample_rates_decoder: List[int] = field(default_factory=lambda: [8, 8, 2, 2]) + upsample_initial_channel_decoder: int = 512 + upsample_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [16, 16, 4, 4]) + periods_multi_period_discriminator: List[int] = field(default_factory=lambda: [2, 3, 5, 7, 11]) + use_sdp: bool = True + noise_scale: float = 1.0 + inference_noise_scale: float = 0.667 + length_scale: float = 1 + noise_scale_dp: float = 1.0 + inference_noise_scale_dp: float = 1.0 + max_inference_len: int = None + init_discriminator: bool = True + use_spectral_norm_disriminator: bool = False + use_speaker_embedding: bool = False + num_speakers: int = 0 + speakers_file: str = None + d_vector_file: List[str] = None + speaker_embedding_channels: int = 256 + use_d_vector_file: bool = False + d_vector_dim: int = 0 + detach_dp_input: bool = True + use_language_embedding: bool = False + embedded_language_dim: int = 4 + num_languages: int = 0 + language_ids_file: str = None + use_speaker_encoder_as_loss: bool = False + speaker_encoder_config_path: str = "" + speaker_encoder_model_path: str = "" + condition_dp_on_speaker: bool = True + freeze_encoder: bool = False + freeze_DP: bool = False + freeze_PE: bool = False + freeze_flow_decoder: bool = False + freeze_waveform_decoder: bool = False + encoder_sample_rate: int = None + interpolate_z: bool = True + reinit_DP: bool = False + reinit_text_encoder: bool = False +@dataclass +class VitsConfig(BaseTTSConfig): + + model: str = "vits" + # model specific params + model_args: VitsArgs = field(default_factory=VitsArgs) + audio: VitsAudioConfig = field(default_factory=VitsAudioConfig) + + # optimizer + grad_clip: List[float] = field(default_factory=lambda: [1000, 1000]) + lr_gen: float = 0.0002 + lr_disc: float = 0.0002 + lr_scheduler_gen: str = "ExponentialLR" + lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + lr_scheduler_disc: str = "ExponentialLR" + lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + scheduler_after_epoch: bool = True + optimizer: str = "AdamW" + optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01}) + + # loss params + kl_loss_alpha: float = 1.0 + disc_loss_alpha: float = 1.0 + gen_loss_alpha: float = 1.0 + feat_loss_alpha: float = 1.0 + mel_loss_alpha: float = 45.0 + dur_loss_alpha: float = 1.0 + speaker_encoder_loss_alpha: float = 1.0 + + # data loader params + return_wav: bool = True + compute_linear_spec: bool = True + + # sampler params + use_weighted_sampler: bool = False # TODO: move it to the base config + weighted_sampler_attrs: dict = field(default_factory=lambda: {}) + weighted_sampler_multipliers: dict = field(default_factory=lambda: {}) + + # overrides + r: int = 1 # DO NOT CHANGE + add_blank: bool = True + + # testing + test_sentences: List[List] = field( + default_factory=lambda: [ + ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."], + ["Be a voice, not an echo."], + ["I'm sorry Dave. I'm afraid I can't do that."], + ["This cake is great. It's so delicious and moist."], + ["Prior to November 22, 1963."], + ] + ) + + # multi-speaker settings + # use speaker embedding layer + num_speakers: int = 0 + use_speaker_embedding: bool = False + speakers_file: str = None + speaker_embedding_channels: int = 256 + language_ids_file: str = None + use_language_embedding: bool = False + + # use d-vectors + use_d_vector_file: bool = False + d_vector_file: List[str] = None + d_vector_dim: int = None + + def __post_init__(self): + pass + # for key, val in self.model_args.items(): + # if hasattr(self, key): + # self[key] = val + + + + + +def parse_symbols(): + return { + "pad": _pad, + "eos": _eos, + "bos": _bos, + "characters": _characters, + "punctuations": _punctuations, + "phonemes": _phonemes, + } + + +# DEFAULT SET OF GRAPHEMES +_pad = "" +_eos = "" +_bos = "" +_blank = "" # TODO: check if we need this alongside with PAD +_characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +_punctuations = "!'(),-.:;? " + + +# DEFAULT SET OF IPA PHONEMES +# Phonemes definition (All IPA characters) +_vowels = "iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻ" +_non_pulmonic_consonants = "ʘɓǀɗǃʄǂɠǁʛ" +_pulmonic_consonants = "pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟ" +_suprasegmentals = "ˈˌːˑ" +_other_symbols = "ʍwɥʜʢʡɕʑɺɧʲ" +_diacrilics = "ɚ˞ɫ" +_phonemes = _vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics + + +class BaseVocabulary: + """Base Vocabulary class. + + This class only needs a vocabulary dictionary without specifying the characters. + + Args: + vocab (Dict): A dictionary of characters and their corresponding indices. + """ + + def __init__(self, vocab: Dict, pad: str = None, blank: str = None, bos: str = None, eos: str = None): + self.vocab = vocab + self.pad = pad + self.blank = blank + self.bos = bos + self.eos = eos + + @property + def pad_id(self) -> int: + """Return the index of the padding character. If the padding character is not specified, return the length + of the vocabulary.""" + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + """Return the index of the blank character. If the blank character is not specified, return the length of + the vocabulary.""" + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def bos_id(self) -> int: + """Return the index of the bos character. If the bos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def eos_id(self) -> int: + """Return the index of the eos character. If the eos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def vocab(self): + """Return the vocabulary dictionary.""" + return self._vocab + + @vocab.setter + def vocab(self, vocab): + """Set the vocabulary dictionary and character mapping dictionaries.""" + self._vocab, self._char_to_id, self._id_to_char = None, None, None + if vocab is not None: + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self._vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self._vocab) # pylint: disable=unnecessary-comprehension + } + + @staticmethod + def init_from_config(config, **kwargs): + """Initialize from the given config.""" + if config.characters is not None and "vocab_dict" in config.characters and config.characters.vocab_dict: + return ( + BaseVocabulary( + config.characters.vocab_dict, + config.characters.pad, + config.characters.blank, + config.characters.bos, + config.characters.eos, + ), + config, + ) + return BaseVocabulary(**kwargs), config + + def to_config(self): + return CharactersConfig( + vocab_dict=self._vocab, + pad=self.pad, + eos=self.eos, + bos=self.bos, + blank=self.blank, + is_unique=False, + is_sorted=False, + ) + + @property + def num_chars(self): + """Return number of tokens in the vocabulary.""" + return len(self._vocab) + + def char_to_id(self, char: str) -> int: + """Map a character to an token ID.""" + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + """Map an token ID to a character.""" + return self._id_to_char[idx] + + +class BaseCharacters: + + + def __init__( + self, + characters: str = None, + punctuations: str = None, + pad: str = None, + eos: str = None, + bos: str = None, + blank: str = None, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + self._characters = characters + self._punctuations = punctuations + self._pad = pad + self._eos = eos + self._bos = bos + self._blank = blank + self.is_unique = is_unique + self.is_sorted = is_sorted + self._create_vocab() + + @property + def pad_id(self) -> int: + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def eos_id(self) -> int: + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def bos_id(self) -> int: + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, characters): + self._characters = characters + self._create_vocab() + + @property + def punctuations(self): + return self._punctuations + + @punctuations.setter + def punctuations(self, punctuations): + self._punctuations = punctuations + self._create_vocab() + + @property + def pad(self): + return self._pad + + @pad.setter + def pad(self, pad): + self._pad = pad + self._create_vocab() + + @property + def eos(self): + return self._eos + + @eos.setter + def eos(self, eos): + self._eos = eos + self._create_vocab() + + @property + def bos(self): + return self._bos + + @bos.setter + def bos(self, bos): + self._bos = bos + self._create_vocab() + + @property + def blank(self): + return self._blank + + @blank.setter + def blank(self, blank): + self._blank = blank + self._create_vocab() + + @property + def vocab(self): + return self._vocab + + @vocab.setter + def vocab(self, vocab): + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self.vocab) # pylint: disable=unnecessary-comprehension + } + + @property + def num_chars(self): + return len(self._vocab) + + def _create_vocab(self): + _vocab = self._characters + if self.is_unique: + _vocab = list(set(_vocab)) + if self.is_sorted: + _vocab = sorted(_vocab) + _vocab = list(_vocab) + _vocab = [self._blank] + _vocab if self._blank is not None and len(self._blank) > 0 else _vocab + _vocab = [self._bos] + _vocab if self._bos is not None and len(self._bos) > 0 else _vocab + _vocab = [self._eos] + _vocab if self._eos is not None and len(self._eos) > 0 else _vocab + _vocab = [self._pad] + _vocab if self._pad is not None and len(self._pad) > 0 else _vocab + self.vocab = _vocab + list(self._punctuations) + if self.is_unique: + duplicates = {x for x in self.vocab if self.vocab.count(x) > 1} + assert ( + len(self.vocab) == len(self._char_to_id) == len(self._id_to_char) + ), f" [!] There are duplicate characters in the character set. {duplicates}" + + def char_to_id(self, char: str) -> int: + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + return self._id_to_char[idx] + + def print_log(self, level: int = 0): + """ + Prints the vocabulary in a nice format. + """ + indent = "\t" * level + print(f"{indent}| > Characters: {self._characters}") + print(f"{indent}| > Punctuations: {self._punctuations}") + print(f"{indent}| > Pad: {self._pad}") + print(f"{indent}| > EOS: {self._eos}") + print(f"{indent}| > BOS: {self._bos}") + print(f"{indent}| > Blank: {self._blank}") + print(f"{indent}| > Vocab: {self.vocab}") + print(f"{indent}| > Num chars: {self.num_chars}") + + @staticmethod + def init_from_config(config: "Coqpit"): # pylint: disable=unused-argument + """Init your character class from a config. + + Implement this method for your subclass. + """ + # use character set from config + if config.characters is not None: + return BaseCharacters(**config.characters), config + # return default character set + characters = BaseCharacters() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=self._eos, + bos=self._bos, + blank=self._blank, + is_unique=self.is_unique, + is_sorted=self.is_sorted, + ) + + +class IPAPhonemes(BaseCharacters): + + + def __init__( + self, + characters: str = _phonemes, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a IPAPhonemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + # band-aid for compatibility with old models + if "characters" in config and config.characters is not None: + if "phonemes" in config.characters and config.characters.phonemes is not None: + config.characters["characters"] = config.characters["phonemes"] + return ( + IPAPhonemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + # use character set from config + if config.characters is not None: + return IPAPhonemes(**config.characters), config + # return default character set + characters = IPAPhonemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +class Graphemes(BaseCharacters): + + + def __init__( + self, + characters: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a Graphemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + if config.characters is not None: + # band-aid for compatibility with old models + if "phonemes" in config.characters: + return ( + Graphemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + return Graphemes(**config.characters), config + characters = Graphemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +if __name__ == "__main__": + gr = Graphemes() + ph = IPAPhonemes() + gr.print_log() + ph.print_log() + + +class VitsCharacters(BaseCharacters): + """Characters class for VITs model for compatibility with pre-trained models""" + + def __init__( + self, + graphemes: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + ipa_characters: str = _phonemes, + ) -> None: + if ipa_characters is not None: + graphemes += ipa_characters + super().__init__(graphemes, punctuations, pad, None, None, "", is_unique=False, is_sorted=True) + + def _create_vocab(self): + self._vocab = [self._pad] + list(self._punctuations) + list(self._characters) + [self._blank] + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + # pylint: disable=unnecessary-comprehension + self._id_to_char = {idx: char for idx, char in enumerate(self.vocab)} + + @staticmethod + def init_from_config(config): + _pad = config.characters.pad + _punctuations = config.characters.punctuations + _letters = config.characters.characters + _letters_ipa = config.characters.phonemes + return ( + VitsCharacters(graphemes=_letters, ipa_characters=_letters_ipa, punctuations=_punctuations, pad=_pad), + config, + ) + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=None, + bos=None, + blank=self._blank, + is_unique=False, + is_sorted=True, + ) + +class TTSTokenizer: + def __init__( + self, + text_cleaner: Callable = None, + characters: "BaseCharacters" = None, + ): + self.text_cleaner = text_cleaner + self.characters = characters + self.not_found_characters = [] + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, new_characters): + self._characters = new_characters + self.pad_id = self.characters.char_to_id(self.characters.pad) if self.characters.pad else None + self.blank_id = self.characters.char_to_id(self.characters.blank) if self.characters.blank else None + + def encode(self, text: str) -> List[int]: + """Encodes a string of text as a sequence of IDs.""" + token_ids = [] + for char in text: + try: + idx = self.characters.char_to_id(char) + token_ids.append(idx) + except KeyError: + # discard but store not found characters + if char not in self.not_found_characters: + self.not_found_characters.append(char) + print(text) + print(f" [!] Character {repr(char)} not found in the vocabulary. Discarding it.") + return token_ids + + def text_to_ids(self, text: str, language: str = None) -> List[int]: # pylint: disable=unused-argument + text = self.text_cleaner(text) + text = self.encode(text) + text = self.intersperse_blank_char(text, True) + return text + + def pad_with_bos_eos(self, char_sequence: List[str]): + """Pads a sequence with the special BOS and EOS characters.""" + return [self.characters.bos_id] + list(char_sequence) + [self.characters.eos_id] + + def intersperse_blank_char(self, char_sequence: List[str], use_blank_char: bool = False): + """Intersperses the blank character between characters in a sequence. + + Use the ```blank``` character if defined else use the ```pad``` character. + """ + char_to_use = self.characters.blank_id if use_blank_char else self.characters.pad + result = [char_to_use] * (len(char_sequence) * 2 + 1) + result[1::2] = char_sequence + return result + + @staticmethod + def init_from_config(config: "Coqpit", characters: "BaseCharacters" = None): + text_cleaner = multilingual_cleaners + CharactersClass = VitsCharacters + characters, new_config = CharactersClass.init_from_config(config) + # new_config.characters.characters_class = get_import_path(characters) + new_config.characters.characters_class = VitsCharacters + return ( + TTSTokenizer(text_cleaner, characters),new_config) + + +def multilingual_cleaners(text): + """Pipeline for multilingual text""" + text = lowercase(text) + text = replace_symbols(text, lang=None) + text = remove_aux_symbols(text) + text = collapse_whitespace(text) + return text + +def lowercase(text): + return text.lower() + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text).strip() + +def replace_symbols(text, lang="en"): + + text = text.replace(";", ",") + text = text.replace("-", " ") if lang != "ca" else text.replace("-", "") + text = text.replace(":", ",") + if lang == "en": + text = text.replace("&", " and ") + elif lang == "fr": + text = text.replace("&", " et ") + elif lang == "pt": + text = text.replace("&", " e ") + elif lang == "ca": + text = text.replace("&", " i ") + text = text.replace("'", "") + return text + +def remove_aux_symbols(text): + text = re.sub(r"[\<\>\(\)\[\]\"]+", "", text) + return text \ No newline at end of file diff --git a/models/mai_male/jit_infer.py b/models/mai_male/jit_infer.py new file mode 100644 index 0000000000000000000000000000000000000000..3e7c6a438d62a2dba4219bcde21ab7da5845e763 --- /dev/null +++ b/models/mai_male/jit_infer.py @@ -0,0 +1,33 @@ +import os +from extra import TTSTokenizer, VitsConfig, CharactersConfig, VitsCharacters +import torch +import numpy as np + +#ch female +with open("chars.txt", 'r') as f: + letters = f.read().strip('\n') +model="mt_male_vits_30hrs.pt" +# text = " হলেও আমাদের সবার সার্বিক শৃঙ্খলা বোধের উন্নতি হবে" +text = "भेजना चाहते हैं हिंदी में मैसेज लेकिन नहीं आती टाइपिंग?" + +config = VitsConfig( + text_cleaner="multilingual_cleaners", + characters=CharactersConfig( + characters_class=VitsCharacters, + pad="", + eos="", + bos="", + blank="", + characters=letters, + punctuations="!¡'(),-.:;¿? ", + phonemes=None) + ) +tokenizer, config = TTSTokenizer.init_from_config(config) + +x = tokenizer.text_to_ids(text) +x = torch.from_numpy(np.array(x)).unsqueeze(0) +net = torch.jit.load(model) +with torch.no_grad(): + out2 = net(x) +import soundfile as sf +sf.write("jit.wav", out2.squeeze().cpu().numpy(), 22050) \ No newline at end of file diff --git a/models/mai_male/mt_male_vits_30hrs.pt b/models/mai_male/mt_male_vits_30hrs.pt new file mode 100644 index 0000000000000000000000000000000000000000..817d978ac1368aaac5be6b7b23bcdee07f69fbcc --- /dev/null +++ b/models/mai_male/mt_male_vits_30hrs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cb77b01fea9f06c9ab51cb580509ed513d73954704fb75ef6bb1a71ede919501 +size 333258444 diff --git a/models/mr_female/.gitattributes b/models/mr_female/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b --- /dev/null +++ b/models/mr_female/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/models/mr_female/README.md b/models/mr_female/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b187bb7e7d837a367ccd0862441947ad412c77f7 --- /dev/null +++ b/models/mr_female/README.md @@ -0,0 +1,3 @@ +--- +license: cc-by-4.0 +--- diff --git a/models/mr_female/chars.txt b/models/mr_female/chars.txt new file mode 100644 index 0000000000000000000000000000000000000000..a73a53e48ffc4d92d743c8041dfdca5c00aa49cf --- /dev/null +++ b/models/mr_female/chars.txt @@ -0,0 +1 @@ +ऴॄछथई४श?अष खठदेणमघूऐवऎगसत!चफ}हयऔरए६ॲॠडोऊधट३पॊढआ,{ॐीभओॅाु२न"७ौब'ळलझिं°ँॉृय़उङ़ञै्क८.ऋऑऍॆ०ः९५इऱज१ diff --git a/models/mr_female/extra.py b/models/mr_female/extra.py new file mode 100644 index 0000000000000000000000000000000000000000..c7db561351da270a7c3931bfe0afefa7bc6d4853 --- /dev/null +++ b/models/mr_female/extra.py @@ -0,0 +1,787 @@ +from typing import Callable, Dict, List, Union +from dataclasses import asdict, dataclass, field + + +import re +from dataclasses import replace +from typing import Dict +_whitespace_re = re.compile(r"\s+") + +from dataclasses import dataclass, field +from typing import List + +# from TTS.tts.configs.shared_configs import BaseTTSConfig +# from TTS.tts.models.vits import VitsArgs, VitsAudioConfig + +@dataclass +class CharactersConfig(): + + characters_class: str = None + + # using BaseVocabulary + vocab_dict: Dict = None + + # using on BaseCharacters + pad: str = None + eos: str = None + bos: str = None + blank: str = None + characters: str = None + punctuations: str = None + phonemes: str = None + is_unique: bool = True # for backwards compatibility of models trained with char sets with duplicates + is_sorted: bool = True + + +@dataclass +class BaseTTSConfig(): + + # audio: BaseAudioConfig = field(default_factory=BaseAudioConfig) + # phoneme settings + use_phonemes: bool = False + phonemizer: str = None + phoneme_language: str = None + compute_input_seq_cache: bool = False + text_cleaner: str = None + enable_eos_bos_chars: bool = False + test_sentences_file: str = "" + phoneme_cache_path: str = None + # vocabulary parameters + characters: CharactersConfig = None + add_blank: bool = False + # training params + batch_group_size: int = 0 + loss_masking: bool = None + # dataloading + min_audio_len: int = 1 + max_audio_len: int = float("inf") + min_text_len: int = 1 + max_text_len: int = float("inf") + compute_f0: bool = False + compute_energy: bool = False + compute_linear_spec: bool = False + precompute_num_workers: int = 0 + use_noise_augment: bool = False + start_by_longest: bool = False + shuffle: bool = False + drop_last: bool = False + # dataset + datasets: str = None + # optimizer + optimizer: str = "radam" + optimizer_params: dict = None + # scheduler + lr_scheduler: str = None + lr_scheduler_params: dict = field(default_factory=lambda: {}) + # testing + test_sentences: List[str] = field(default_factory=lambda: []) + # evaluation + eval_split_max_size: int = None + eval_split_size: float = 0.01 + # weighted samplers + use_speaker_weighted_sampler: bool = False + speaker_weighted_sampler_alpha: float = 1.0 + use_language_weighted_sampler: bool = False + language_weighted_sampler_alpha: float = 1.0 + use_length_weighted_sampler: bool = False + length_weighted_sampler_alpha: float = 1.0 + + +@dataclass +class VitsAudioConfig(): + fft_size: int = 1024 + sample_rate: int = 22050 + win_length: int = 1024 + hop_length: int = 256 + num_mels: int = 80 + mel_fmin: int = 0 + mel_fmax: int = None + +@dataclass +class VitsArgs(): + num_chars: int = 100 + out_channels: int = 513 + spec_segment_size: int = 32 + hidden_channels: int = 192 + hidden_channels_ffn_text_encoder: int = 768 + num_heads_text_encoder: int = 2 + num_layers_text_encoder: int = 6 + kernel_size_text_encoder: int = 3 + dropout_p_text_encoder: float = 0.1 + dropout_p_duration_predictor: float = 0.5 + kernel_size_posterior_encoder: int = 5 + dilation_rate_posterior_encoder: int = 1 + num_layers_posterior_encoder: int = 16 + kernel_size_flow: int = 5 + dilation_rate_flow: int = 1 + num_layers_flow: int = 4 + resblock_type_decoder: str = "1" + resblock_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [3, 7, 11]) + resblock_dilation_sizes_decoder: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]) + upsample_rates_decoder: List[int] = field(default_factory=lambda: [8, 8, 2, 2]) + upsample_initial_channel_decoder: int = 512 + upsample_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [16, 16, 4, 4]) + periods_multi_period_discriminator: List[int] = field(default_factory=lambda: [2, 3, 5, 7, 11]) + use_sdp: bool = True + noise_scale: float = 1.0 + inference_noise_scale: float = 0.667 + length_scale: float = 1 + noise_scale_dp: float = 1.0 + inference_noise_scale_dp: float = 1.0 + max_inference_len: int = None + init_discriminator: bool = True + use_spectral_norm_disriminator: bool = False + use_speaker_embedding: bool = False + num_speakers: int = 0 + speakers_file: str = None + d_vector_file: List[str] = None + speaker_embedding_channels: int = 256 + use_d_vector_file: bool = False + d_vector_dim: int = 0 + detach_dp_input: bool = True + use_language_embedding: bool = False + embedded_language_dim: int = 4 + num_languages: int = 0 + language_ids_file: str = None + use_speaker_encoder_as_loss: bool = False + speaker_encoder_config_path: str = "" + speaker_encoder_model_path: str = "" + condition_dp_on_speaker: bool = True + freeze_encoder: bool = False + freeze_DP: bool = False + freeze_PE: bool = False + freeze_flow_decoder: bool = False + freeze_waveform_decoder: bool = False + encoder_sample_rate: int = None + interpolate_z: bool = True + reinit_DP: bool = False + reinit_text_encoder: bool = False +@dataclass +class VitsConfig(BaseTTSConfig): + + model: str = "vits" + # model specific params + model_args: VitsArgs = field(default_factory=VitsArgs) + audio: VitsAudioConfig = field(default_factory=VitsAudioConfig) + + # optimizer + grad_clip: List[float] = field(default_factory=lambda: [1000, 1000]) + lr_gen: float = 0.0002 + lr_disc: float = 0.0002 + lr_scheduler_gen: str = "ExponentialLR" + lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + lr_scheduler_disc: str = "ExponentialLR" + lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + scheduler_after_epoch: bool = True + optimizer: str = "AdamW" + optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01}) + + # loss params + kl_loss_alpha: float = 1.0 + disc_loss_alpha: float = 1.0 + gen_loss_alpha: float = 1.0 + feat_loss_alpha: float = 1.0 + mel_loss_alpha: float = 45.0 + dur_loss_alpha: float = 1.0 + speaker_encoder_loss_alpha: float = 1.0 + + # data loader params + return_wav: bool = True + compute_linear_spec: bool = True + + # sampler params + use_weighted_sampler: bool = False # TODO: move it to the base config + weighted_sampler_attrs: dict = field(default_factory=lambda: {}) + weighted_sampler_multipliers: dict = field(default_factory=lambda: {}) + + # overrides + r: int = 1 # DO NOT CHANGE + add_blank: bool = True + + # testing + test_sentences: List[List] = field( + default_factory=lambda: [ + ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."], + ["Be a voice, not an echo."], + ["I'm sorry Dave. I'm afraid I can't do that."], + ["This cake is great. It's so delicious and moist."], + ["Prior to November 22, 1963."], + ] + ) + + # multi-speaker settings + # use speaker embedding layer + num_speakers: int = 0 + use_speaker_embedding: bool = False + speakers_file: str = None + speaker_embedding_channels: int = 256 + language_ids_file: str = None + use_language_embedding: bool = False + + # use d-vectors + use_d_vector_file: bool = False + d_vector_file: List[str] = None + d_vector_dim: int = None + + def __post_init__(self): + pass + # for key, val in self.model_args.items(): + # if hasattr(self, key): + # self[key] = val + + + + + +def parse_symbols(): + return { + "pad": _pad, + "eos": _eos, + "bos": _bos, + "characters": _characters, + "punctuations": _punctuations, + "phonemes": _phonemes, + } + + +# DEFAULT SET OF GRAPHEMES +_pad = "" +_eos = "" +_bos = "" +_blank = "" # TODO: check if we need this alongside with PAD +_characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +_punctuations = "!'(),-.:;? " + + +# DEFAULT SET OF IPA PHONEMES +# Phonemes definition (All IPA characters) +_vowels = "iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻ" +_non_pulmonic_consonants = "ʘɓǀɗǃʄǂɠǁʛ" +_pulmonic_consonants = "pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟ" +_suprasegmentals = "ˈˌːˑ" +_other_symbols = "ʍwɥʜʢʡɕʑɺɧʲ" +_diacrilics = "ɚ˞ɫ" +_phonemes = _vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics + + +class BaseVocabulary: + """Base Vocabulary class. + + This class only needs a vocabulary dictionary without specifying the characters. + + Args: + vocab (Dict): A dictionary of characters and their corresponding indices. + """ + + def __init__(self, vocab: Dict, pad: str = None, blank: str = None, bos: str = None, eos: str = None): + self.vocab = vocab + self.pad = pad + self.blank = blank + self.bos = bos + self.eos = eos + + @property + def pad_id(self) -> int: + """Return the index of the padding character. If the padding character is not specified, return the length + of the vocabulary.""" + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + """Return the index of the blank character. If the blank character is not specified, return the length of + the vocabulary.""" + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def bos_id(self) -> int: + """Return the index of the bos character. If the bos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def eos_id(self) -> int: + """Return the index of the eos character. If the eos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def vocab(self): + """Return the vocabulary dictionary.""" + return self._vocab + + @vocab.setter + def vocab(self, vocab): + """Set the vocabulary dictionary and character mapping dictionaries.""" + self._vocab, self._char_to_id, self._id_to_char = None, None, None + if vocab is not None: + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self._vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self._vocab) # pylint: disable=unnecessary-comprehension + } + + @staticmethod + def init_from_config(config, **kwargs): + """Initialize from the given config.""" + if config.characters is not None and "vocab_dict" in config.characters and config.characters.vocab_dict: + return ( + BaseVocabulary( + config.characters.vocab_dict, + config.characters.pad, + config.characters.blank, + config.characters.bos, + config.characters.eos, + ), + config, + ) + return BaseVocabulary(**kwargs), config + + def to_config(self): + return CharactersConfig( + vocab_dict=self._vocab, + pad=self.pad, + eos=self.eos, + bos=self.bos, + blank=self.blank, + is_unique=False, + is_sorted=False, + ) + + @property + def num_chars(self): + """Return number of tokens in the vocabulary.""" + return len(self._vocab) + + def char_to_id(self, char: str) -> int: + """Map a character to an token ID.""" + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + """Map an token ID to a character.""" + return self._id_to_char[idx] + + +class BaseCharacters: + + + def __init__( + self, + characters: str = None, + punctuations: str = None, + pad: str = None, + eos: str = None, + bos: str = None, + blank: str = None, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + self._characters = characters + self._punctuations = punctuations + self._pad = pad + self._eos = eos + self._bos = bos + self._blank = blank + self.is_unique = is_unique + self.is_sorted = is_sorted + self._create_vocab() + + @property + def pad_id(self) -> int: + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def eos_id(self) -> int: + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def bos_id(self) -> int: + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, characters): + self._characters = characters + self._create_vocab() + + @property + def punctuations(self): + return self._punctuations + + @punctuations.setter + def punctuations(self, punctuations): + self._punctuations = punctuations + self._create_vocab() + + @property + def pad(self): + return self._pad + + @pad.setter + def pad(self, pad): + self._pad = pad + self._create_vocab() + + @property + def eos(self): + return self._eos + + @eos.setter + def eos(self, eos): + self._eos = eos + self._create_vocab() + + @property + def bos(self): + return self._bos + + @bos.setter + def bos(self, bos): + self._bos = bos + self._create_vocab() + + @property + def blank(self): + return self._blank + + @blank.setter + def blank(self, blank): + self._blank = blank + self._create_vocab() + + @property + def vocab(self): + return self._vocab + + @vocab.setter + def vocab(self, vocab): + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self.vocab) # pylint: disable=unnecessary-comprehension + } + + @property + def num_chars(self): + return len(self._vocab) + + def _create_vocab(self): + _vocab = self._characters + if self.is_unique: + _vocab = list(set(_vocab)) + if self.is_sorted: + _vocab = sorted(_vocab) + _vocab = list(_vocab) + _vocab = [self._blank] + _vocab if self._blank is not None and len(self._blank) > 0 else _vocab + _vocab = [self._bos] + _vocab if self._bos is not None and len(self._bos) > 0 else _vocab + _vocab = [self._eos] + _vocab if self._eos is not None and len(self._eos) > 0 else _vocab + _vocab = [self._pad] + _vocab if self._pad is not None and len(self._pad) > 0 else _vocab + self.vocab = _vocab + list(self._punctuations) + if self.is_unique: + duplicates = {x for x in self.vocab if self.vocab.count(x) > 1} + assert ( + len(self.vocab) == len(self._char_to_id) == len(self._id_to_char) + ), f" [!] There are duplicate characters in the character set. {duplicates}" + + def char_to_id(self, char: str) -> int: + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + return self._id_to_char[idx] + + def print_log(self, level: int = 0): + """ + Prints the vocabulary in a nice format. + """ + indent = "\t" * level + print(f"{indent}| > Characters: {self._characters}") + print(f"{indent}| > Punctuations: {self._punctuations}") + print(f"{indent}| > Pad: {self._pad}") + print(f"{indent}| > EOS: {self._eos}") + print(f"{indent}| > BOS: {self._bos}") + print(f"{indent}| > Blank: {self._blank}") + print(f"{indent}| > Vocab: {self.vocab}") + print(f"{indent}| > Num chars: {self.num_chars}") + + @staticmethod + def init_from_config(config: "Coqpit"): # pylint: disable=unused-argument + """Init your character class from a config. + + Implement this method for your subclass. + """ + # use character set from config + if config.characters is not None: + return BaseCharacters(**config.characters), config + # return default character set + characters = BaseCharacters() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=self._eos, + bos=self._bos, + blank=self._blank, + is_unique=self.is_unique, + is_sorted=self.is_sorted, + ) + + +class IPAPhonemes(BaseCharacters): + + + def __init__( + self, + characters: str = _phonemes, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a IPAPhonemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + # band-aid for compatibility with old models + if "characters" in config and config.characters is not None: + if "phonemes" in config.characters and config.characters.phonemes is not None: + config.characters["characters"] = config.characters["phonemes"] + return ( + IPAPhonemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + # use character set from config + if config.characters is not None: + return IPAPhonemes(**config.characters), config + # return default character set + characters = IPAPhonemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +class Graphemes(BaseCharacters): + + + def __init__( + self, + characters: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a Graphemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + if config.characters is not None: + # band-aid for compatibility with old models + if "phonemes" in config.characters: + return ( + Graphemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + return Graphemes(**config.characters), config + characters = Graphemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +if __name__ == "__main__": + gr = Graphemes() + ph = IPAPhonemes() + gr.print_log() + ph.print_log() + + +class VitsCharacters(BaseCharacters): + """Characters class for VITs model for compatibility with pre-trained models""" + + def __init__( + self, + graphemes: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + ipa_characters: str = _phonemes, + ) -> None: + if ipa_characters is not None: + graphemes += ipa_characters + super().__init__(graphemes, punctuations, pad, None, None, "", is_unique=False, is_sorted=True) + + def _create_vocab(self): + self._vocab = [self._pad] + list(self._punctuations) + list(self._characters) + [self._blank] + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + # pylint: disable=unnecessary-comprehension + self._id_to_char = {idx: char for idx, char in enumerate(self.vocab)} + + @staticmethod + def init_from_config(config): + _pad = config.characters.pad + _punctuations = config.characters.punctuations + _letters = config.characters.characters + _letters_ipa = config.characters.phonemes + return ( + VitsCharacters(graphemes=_letters, ipa_characters=_letters_ipa, punctuations=_punctuations, pad=_pad), + config, + ) + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=None, + bos=None, + blank=self._blank, + is_unique=False, + is_sorted=True, + ) + +class TTSTokenizer: + def __init__( + self, + text_cleaner: Callable = None, + characters: "BaseCharacters" = None, + ): + self.text_cleaner = text_cleaner + self.characters = characters + self.not_found_characters = [] + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, new_characters): + self._characters = new_characters + self.pad_id = self.characters.char_to_id(self.characters.pad) if self.characters.pad else None + self.blank_id = self.characters.char_to_id(self.characters.blank) if self.characters.blank else None + + def encode(self, text: str) -> List[int]: + """Encodes a string of text as a sequence of IDs.""" + token_ids = [] + for char in text: + try: + idx = self.characters.char_to_id(char) + token_ids.append(idx) + except KeyError: + # discard but store not found characters + if char not in self.not_found_characters: + self.not_found_characters.append(char) + print(text) + print(f" [!] Character {repr(char)} not found in the vocabulary. Discarding it.") + return token_ids + + def text_to_ids(self, text: str, language: str = None) -> List[int]: # pylint: disable=unused-argument + text = self.text_cleaner(text) + text = self.encode(text) + text = self.intersperse_blank_char(text, True) + return text + + def pad_with_bos_eos(self, char_sequence: List[str]): + """Pads a sequence with the special BOS and EOS characters.""" + return [self.characters.bos_id] + list(char_sequence) + [self.characters.eos_id] + + def intersperse_blank_char(self, char_sequence: List[str], use_blank_char: bool = False): + """Intersperses the blank character between characters in a sequence. + + Use the ```blank``` character if defined else use the ```pad``` character. + """ + char_to_use = self.characters.blank_id if use_blank_char else self.characters.pad + result = [char_to_use] * (len(char_sequence) * 2 + 1) + result[1::2] = char_sequence + return result + + @staticmethod + def init_from_config(config: "Coqpit", characters: "BaseCharacters" = None): + text_cleaner = multilingual_cleaners + CharactersClass = VitsCharacters + characters, new_config = CharactersClass.init_from_config(config) + # new_config.characters.characters_class = get_import_path(characters) + new_config.characters.characters_class = VitsCharacters + return ( + TTSTokenizer(text_cleaner, characters),new_config) + + +def multilingual_cleaners(text): + """Pipeline for multilingual text""" + text = lowercase(text) + text = replace_symbols(text, lang=None) + text = remove_aux_symbols(text) + text = collapse_whitespace(text) + return text + +def lowercase(text): + return text.lower() + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text).strip() + +def replace_symbols(text, lang="en"): + + text = text.replace(";", ",") + text = text.replace("-", " ") if lang != "ca" else text.replace("-", "") + text = text.replace(":", ",") + if lang == "en": + text = text.replace("&", " and ") + elif lang == "fr": + text = text.replace("&", " et ") + elif lang == "pt": + text = text.replace("&", " e ") + elif lang == "ca": + text = text.replace("&", " i ") + text = text.replace("'", "") + return text + +def remove_aux_symbols(text): + text = re.sub(r"[\<\>\(\)\[\]\"]+", "", text) + return text \ No newline at end of file diff --git a/models/mr_female/jit_infer.py b/models/mr_female/jit_infer.py new file mode 100644 index 0000000000000000000000000000000000000000..dff8ceb33c0484f567226ff0d3f185b05d12d75a --- /dev/null +++ b/models/mr_female/jit_infer.py @@ -0,0 +1,32 @@ +import os +from extra import TTSTokenizer, VitsConfig, CharactersConfig, VitsCharacters +import torch +import numpy as np + +#ch female +with open("chars.txt", 'r') as f: + letters = f.read().strip('\n') +model="mr_female_vits_30hrs.pt" +text = "फिल्म गर्दिश में अमरीश पुरी के साथ जैकी श्रॉफ, ऐश्वर्या, डिंपल कपाड़िया" + +config = VitsConfig( + text_cleaner="multilingual_cleaners", + characters=CharactersConfig( + characters_class=VitsCharacters, + pad="", + eos="", + bos="", + blank="", + characters=letters, + punctuations="!¡'(),-.:;¿? ", + phonemes=None) + ) +tokenizer, config = TTSTokenizer.init_from_config(config) + +x = tokenizer.text_to_ids(text) +x = torch.from_numpy(np.array(x)).unsqueeze(0) +net = torch.jit.load(model) +with torch.no_grad(): + out2 = net(x) +import soundfile as sf +sf.write("jit.wav", out2.squeeze().cpu().numpy(), 22050) \ No newline at end of file diff --git a/models/mr_female/mr_female_vits_30hrs.pt b/models/mr_female/mr_female_vits_30hrs.pt new file mode 100644 index 0000000000000000000000000000000000000000..951b3484ccbe15851ef292cf74d6d297005be7e1 --- /dev/null +++ b/models/mr_female/mr_female_vits_30hrs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5da210ece20c171ee09f8969d1755fb475a43c2c3166a4a088f36b9aa828dbb7 +size 333273734 diff --git a/models/mr_male/.gitattributes b/models/mr_male/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b --- /dev/null +++ b/models/mr_male/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/models/mr_male/README.md b/models/mr_male/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b187bb7e7d837a367ccd0862441947ad412c77f7 --- /dev/null +++ b/models/mr_male/README.md @@ -0,0 +1,3 @@ +--- +license: cc-by-4.0 +--- diff --git a/models/mr_male/chars.txt b/models/mr_male/chars.txt new file mode 100644 index 0000000000000000000000000000000000000000..bc298ad1833e2d2d4e74009ae26353f63fca85ac --- /dev/null +++ b/models/mr_male/chars.txt @@ -0,0 +1 @@ +फ़ॄछथईग़o४श?अष॰ खठदेणमघूऐवऒऎगसत!चफहयऔरएॲ६ॠख़डोढ़ऊधट३ड़पॊढक़आ,ॐीभज़ओऽॅऩाु२न"७ौब'ळलझिंँॉृय़उङ़ञै्कऋ.८ऑऍॆ०ः९५इऱज१ diff --git a/models/mr_male/extra.py b/models/mr_male/extra.py new file mode 100644 index 0000000000000000000000000000000000000000..c7db561351da270a7c3931bfe0afefa7bc6d4853 --- /dev/null +++ b/models/mr_male/extra.py @@ -0,0 +1,787 @@ +from typing import Callable, Dict, List, Union +from dataclasses import asdict, dataclass, field + + +import re +from dataclasses import replace +from typing import Dict +_whitespace_re = re.compile(r"\s+") + +from dataclasses import dataclass, field +from typing import List + +# from TTS.tts.configs.shared_configs import BaseTTSConfig +# from TTS.tts.models.vits import VitsArgs, VitsAudioConfig + +@dataclass +class CharactersConfig(): + + characters_class: str = None + + # using BaseVocabulary + vocab_dict: Dict = None + + # using on BaseCharacters + pad: str = None + eos: str = None + bos: str = None + blank: str = None + characters: str = None + punctuations: str = None + phonemes: str = None + is_unique: bool = True # for backwards compatibility of models trained with char sets with duplicates + is_sorted: bool = True + + +@dataclass +class BaseTTSConfig(): + + # audio: BaseAudioConfig = field(default_factory=BaseAudioConfig) + # phoneme settings + use_phonemes: bool = False + phonemizer: str = None + phoneme_language: str = None + compute_input_seq_cache: bool = False + text_cleaner: str = None + enable_eos_bos_chars: bool = False + test_sentences_file: str = "" + phoneme_cache_path: str = None + # vocabulary parameters + characters: CharactersConfig = None + add_blank: bool = False + # training params + batch_group_size: int = 0 + loss_masking: bool = None + # dataloading + min_audio_len: int = 1 + max_audio_len: int = float("inf") + min_text_len: int = 1 + max_text_len: int = float("inf") + compute_f0: bool = False + compute_energy: bool = False + compute_linear_spec: bool = False + precompute_num_workers: int = 0 + use_noise_augment: bool = False + start_by_longest: bool = False + shuffle: bool = False + drop_last: bool = False + # dataset + datasets: str = None + # optimizer + optimizer: str = "radam" + optimizer_params: dict = None + # scheduler + lr_scheduler: str = None + lr_scheduler_params: dict = field(default_factory=lambda: {}) + # testing + test_sentences: List[str] = field(default_factory=lambda: []) + # evaluation + eval_split_max_size: int = None + eval_split_size: float = 0.01 + # weighted samplers + use_speaker_weighted_sampler: bool = False + speaker_weighted_sampler_alpha: float = 1.0 + use_language_weighted_sampler: bool = False + language_weighted_sampler_alpha: float = 1.0 + use_length_weighted_sampler: bool = False + length_weighted_sampler_alpha: float = 1.0 + + +@dataclass +class VitsAudioConfig(): + fft_size: int = 1024 + sample_rate: int = 22050 + win_length: int = 1024 + hop_length: int = 256 + num_mels: int = 80 + mel_fmin: int = 0 + mel_fmax: int = None + +@dataclass +class VitsArgs(): + num_chars: int = 100 + out_channels: int = 513 + spec_segment_size: int = 32 + hidden_channels: int = 192 + hidden_channels_ffn_text_encoder: int = 768 + num_heads_text_encoder: int = 2 + num_layers_text_encoder: int = 6 + kernel_size_text_encoder: int = 3 + dropout_p_text_encoder: float = 0.1 + dropout_p_duration_predictor: float = 0.5 + kernel_size_posterior_encoder: int = 5 + dilation_rate_posterior_encoder: int = 1 + num_layers_posterior_encoder: int = 16 + kernel_size_flow: int = 5 + dilation_rate_flow: int = 1 + num_layers_flow: int = 4 + resblock_type_decoder: str = "1" + resblock_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [3, 7, 11]) + resblock_dilation_sizes_decoder: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]) + upsample_rates_decoder: List[int] = field(default_factory=lambda: [8, 8, 2, 2]) + upsample_initial_channel_decoder: int = 512 + upsample_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [16, 16, 4, 4]) + periods_multi_period_discriminator: List[int] = field(default_factory=lambda: [2, 3, 5, 7, 11]) + use_sdp: bool = True + noise_scale: float = 1.0 + inference_noise_scale: float = 0.667 + length_scale: float = 1 + noise_scale_dp: float = 1.0 + inference_noise_scale_dp: float = 1.0 + max_inference_len: int = None + init_discriminator: bool = True + use_spectral_norm_disriminator: bool = False + use_speaker_embedding: bool = False + num_speakers: int = 0 + speakers_file: str = None + d_vector_file: List[str] = None + speaker_embedding_channels: int = 256 + use_d_vector_file: bool = False + d_vector_dim: int = 0 + detach_dp_input: bool = True + use_language_embedding: bool = False + embedded_language_dim: int = 4 + num_languages: int = 0 + language_ids_file: str = None + use_speaker_encoder_as_loss: bool = False + speaker_encoder_config_path: str = "" + speaker_encoder_model_path: str = "" + condition_dp_on_speaker: bool = True + freeze_encoder: bool = False + freeze_DP: bool = False + freeze_PE: bool = False + freeze_flow_decoder: bool = False + freeze_waveform_decoder: bool = False + encoder_sample_rate: int = None + interpolate_z: bool = True + reinit_DP: bool = False + reinit_text_encoder: bool = False +@dataclass +class VitsConfig(BaseTTSConfig): + + model: str = "vits" + # model specific params + model_args: VitsArgs = field(default_factory=VitsArgs) + audio: VitsAudioConfig = field(default_factory=VitsAudioConfig) + + # optimizer + grad_clip: List[float] = field(default_factory=lambda: [1000, 1000]) + lr_gen: float = 0.0002 + lr_disc: float = 0.0002 + lr_scheduler_gen: str = "ExponentialLR" + lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + lr_scheduler_disc: str = "ExponentialLR" + lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + scheduler_after_epoch: bool = True + optimizer: str = "AdamW" + optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01}) + + # loss params + kl_loss_alpha: float = 1.0 + disc_loss_alpha: float = 1.0 + gen_loss_alpha: float = 1.0 + feat_loss_alpha: float = 1.0 + mel_loss_alpha: float = 45.0 + dur_loss_alpha: float = 1.0 + speaker_encoder_loss_alpha: float = 1.0 + + # data loader params + return_wav: bool = True + compute_linear_spec: bool = True + + # sampler params + use_weighted_sampler: bool = False # TODO: move it to the base config + weighted_sampler_attrs: dict = field(default_factory=lambda: {}) + weighted_sampler_multipliers: dict = field(default_factory=lambda: {}) + + # overrides + r: int = 1 # DO NOT CHANGE + add_blank: bool = True + + # testing + test_sentences: List[List] = field( + default_factory=lambda: [ + ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."], + ["Be a voice, not an echo."], + ["I'm sorry Dave. I'm afraid I can't do that."], + ["This cake is great. It's so delicious and moist."], + ["Prior to November 22, 1963."], + ] + ) + + # multi-speaker settings + # use speaker embedding layer + num_speakers: int = 0 + use_speaker_embedding: bool = False + speakers_file: str = None + speaker_embedding_channels: int = 256 + language_ids_file: str = None + use_language_embedding: bool = False + + # use d-vectors + use_d_vector_file: bool = False + d_vector_file: List[str] = None + d_vector_dim: int = None + + def __post_init__(self): + pass + # for key, val in self.model_args.items(): + # if hasattr(self, key): + # self[key] = val + + + + + +def parse_symbols(): + return { + "pad": _pad, + "eos": _eos, + "bos": _bos, + "characters": _characters, + "punctuations": _punctuations, + "phonemes": _phonemes, + } + + +# DEFAULT SET OF GRAPHEMES +_pad = "" +_eos = "" +_bos = "" +_blank = "" # TODO: check if we need this alongside with PAD +_characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +_punctuations = "!'(),-.:;? " + + +# DEFAULT SET OF IPA PHONEMES +# Phonemes definition (All IPA characters) +_vowels = "iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻ" +_non_pulmonic_consonants = "ʘɓǀɗǃʄǂɠǁʛ" +_pulmonic_consonants = "pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟ" +_suprasegmentals = "ˈˌːˑ" +_other_symbols = "ʍwɥʜʢʡɕʑɺɧʲ" +_diacrilics = "ɚ˞ɫ" +_phonemes = _vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics + + +class BaseVocabulary: + """Base Vocabulary class. + + This class only needs a vocabulary dictionary without specifying the characters. + + Args: + vocab (Dict): A dictionary of characters and their corresponding indices. + """ + + def __init__(self, vocab: Dict, pad: str = None, blank: str = None, bos: str = None, eos: str = None): + self.vocab = vocab + self.pad = pad + self.blank = blank + self.bos = bos + self.eos = eos + + @property + def pad_id(self) -> int: + """Return the index of the padding character. If the padding character is not specified, return the length + of the vocabulary.""" + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + """Return the index of the blank character. If the blank character is not specified, return the length of + the vocabulary.""" + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def bos_id(self) -> int: + """Return the index of the bos character. If the bos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def eos_id(self) -> int: + """Return the index of the eos character. If the eos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def vocab(self): + """Return the vocabulary dictionary.""" + return self._vocab + + @vocab.setter + def vocab(self, vocab): + """Set the vocabulary dictionary and character mapping dictionaries.""" + self._vocab, self._char_to_id, self._id_to_char = None, None, None + if vocab is not None: + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self._vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self._vocab) # pylint: disable=unnecessary-comprehension + } + + @staticmethod + def init_from_config(config, **kwargs): + """Initialize from the given config.""" + if config.characters is not None and "vocab_dict" in config.characters and config.characters.vocab_dict: + return ( + BaseVocabulary( + config.characters.vocab_dict, + config.characters.pad, + config.characters.blank, + config.characters.bos, + config.characters.eos, + ), + config, + ) + return BaseVocabulary(**kwargs), config + + def to_config(self): + return CharactersConfig( + vocab_dict=self._vocab, + pad=self.pad, + eos=self.eos, + bos=self.bos, + blank=self.blank, + is_unique=False, + is_sorted=False, + ) + + @property + def num_chars(self): + """Return number of tokens in the vocabulary.""" + return len(self._vocab) + + def char_to_id(self, char: str) -> int: + """Map a character to an token ID.""" + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + """Map an token ID to a character.""" + return self._id_to_char[idx] + + +class BaseCharacters: + + + def __init__( + self, + characters: str = None, + punctuations: str = None, + pad: str = None, + eos: str = None, + bos: str = None, + blank: str = None, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + self._characters = characters + self._punctuations = punctuations + self._pad = pad + self._eos = eos + self._bos = bos + self._blank = blank + self.is_unique = is_unique + self.is_sorted = is_sorted + self._create_vocab() + + @property + def pad_id(self) -> int: + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def eos_id(self) -> int: + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def bos_id(self) -> int: + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, characters): + self._characters = characters + self._create_vocab() + + @property + def punctuations(self): + return self._punctuations + + @punctuations.setter + def punctuations(self, punctuations): + self._punctuations = punctuations + self._create_vocab() + + @property + def pad(self): + return self._pad + + @pad.setter + def pad(self, pad): + self._pad = pad + self._create_vocab() + + @property + def eos(self): + return self._eos + + @eos.setter + def eos(self, eos): + self._eos = eos + self._create_vocab() + + @property + def bos(self): + return self._bos + + @bos.setter + def bos(self, bos): + self._bos = bos + self._create_vocab() + + @property + def blank(self): + return self._blank + + @blank.setter + def blank(self, blank): + self._blank = blank + self._create_vocab() + + @property + def vocab(self): + return self._vocab + + @vocab.setter + def vocab(self, vocab): + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self.vocab) # pylint: disable=unnecessary-comprehension + } + + @property + def num_chars(self): + return len(self._vocab) + + def _create_vocab(self): + _vocab = self._characters + if self.is_unique: + _vocab = list(set(_vocab)) + if self.is_sorted: + _vocab = sorted(_vocab) + _vocab = list(_vocab) + _vocab = [self._blank] + _vocab if self._blank is not None and len(self._blank) > 0 else _vocab + _vocab = [self._bos] + _vocab if self._bos is not None and len(self._bos) > 0 else _vocab + _vocab = [self._eos] + _vocab if self._eos is not None and len(self._eos) > 0 else _vocab + _vocab = [self._pad] + _vocab if self._pad is not None and len(self._pad) > 0 else _vocab + self.vocab = _vocab + list(self._punctuations) + if self.is_unique: + duplicates = {x for x in self.vocab if self.vocab.count(x) > 1} + assert ( + len(self.vocab) == len(self._char_to_id) == len(self._id_to_char) + ), f" [!] There are duplicate characters in the character set. {duplicates}" + + def char_to_id(self, char: str) -> int: + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + return self._id_to_char[idx] + + def print_log(self, level: int = 0): + """ + Prints the vocabulary in a nice format. + """ + indent = "\t" * level + print(f"{indent}| > Characters: {self._characters}") + print(f"{indent}| > Punctuations: {self._punctuations}") + print(f"{indent}| > Pad: {self._pad}") + print(f"{indent}| > EOS: {self._eos}") + print(f"{indent}| > BOS: {self._bos}") + print(f"{indent}| > Blank: {self._blank}") + print(f"{indent}| > Vocab: {self.vocab}") + print(f"{indent}| > Num chars: {self.num_chars}") + + @staticmethod + def init_from_config(config: "Coqpit"): # pylint: disable=unused-argument + """Init your character class from a config. + + Implement this method for your subclass. + """ + # use character set from config + if config.characters is not None: + return BaseCharacters(**config.characters), config + # return default character set + characters = BaseCharacters() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=self._eos, + bos=self._bos, + blank=self._blank, + is_unique=self.is_unique, + is_sorted=self.is_sorted, + ) + + +class IPAPhonemes(BaseCharacters): + + + def __init__( + self, + characters: str = _phonemes, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a IPAPhonemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + # band-aid for compatibility with old models + if "characters" in config and config.characters is not None: + if "phonemes" in config.characters and config.characters.phonemes is not None: + config.characters["characters"] = config.characters["phonemes"] + return ( + IPAPhonemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + # use character set from config + if config.characters is not None: + return IPAPhonemes(**config.characters), config + # return default character set + characters = IPAPhonemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +class Graphemes(BaseCharacters): + + + def __init__( + self, + characters: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a Graphemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + if config.characters is not None: + # band-aid for compatibility with old models + if "phonemes" in config.characters: + return ( + Graphemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + return Graphemes(**config.characters), config + characters = Graphemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +if __name__ == "__main__": + gr = Graphemes() + ph = IPAPhonemes() + gr.print_log() + ph.print_log() + + +class VitsCharacters(BaseCharacters): + """Characters class for VITs model for compatibility with pre-trained models""" + + def __init__( + self, + graphemes: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + ipa_characters: str = _phonemes, + ) -> None: + if ipa_characters is not None: + graphemes += ipa_characters + super().__init__(graphemes, punctuations, pad, None, None, "", is_unique=False, is_sorted=True) + + def _create_vocab(self): + self._vocab = [self._pad] + list(self._punctuations) + list(self._characters) + [self._blank] + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + # pylint: disable=unnecessary-comprehension + self._id_to_char = {idx: char for idx, char in enumerate(self.vocab)} + + @staticmethod + def init_from_config(config): + _pad = config.characters.pad + _punctuations = config.characters.punctuations + _letters = config.characters.characters + _letters_ipa = config.characters.phonemes + return ( + VitsCharacters(graphemes=_letters, ipa_characters=_letters_ipa, punctuations=_punctuations, pad=_pad), + config, + ) + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=None, + bos=None, + blank=self._blank, + is_unique=False, + is_sorted=True, + ) + +class TTSTokenizer: + def __init__( + self, + text_cleaner: Callable = None, + characters: "BaseCharacters" = None, + ): + self.text_cleaner = text_cleaner + self.characters = characters + self.not_found_characters = [] + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, new_characters): + self._characters = new_characters + self.pad_id = self.characters.char_to_id(self.characters.pad) if self.characters.pad else None + self.blank_id = self.characters.char_to_id(self.characters.blank) if self.characters.blank else None + + def encode(self, text: str) -> List[int]: + """Encodes a string of text as a sequence of IDs.""" + token_ids = [] + for char in text: + try: + idx = self.characters.char_to_id(char) + token_ids.append(idx) + except KeyError: + # discard but store not found characters + if char not in self.not_found_characters: + self.not_found_characters.append(char) + print(text) + print(f" [!] Character {repr(char)} not found in the vocabulary. Discarding it.") + return token_ids + + def text_to_ids(self, text: str, language: str = None) -> List[int]: # pylint: disable=unused-argument + text = self.text_cleaner(text) + text = self.encode(text) + text = self.intersperse_blank_char(text, True) + return text + + def pad_with_bos_eos(self, char_sequence: List[str]): + """Pads a sequence with the special BOS and EOS characters.""" + return [self.characters.bos_id] + list(char_sequence) + [self.characters.eos_id] + + def intersperse_blank_char(self, char_sequence: List[str], use_blank_char: bool = False): + """Intersperses the blank character between characters in a sequence. + + Use the ```blank``` character if defined else use the ```pad``` character. + """ + char_to_use = self.characters.blank_id if use_blank_char else self.characters.pad + result = [char_to_use] * (len(char_sequence) * 2 + 1) + result[1::2] = char_sequence + return result + + @staticmethod + def init_from_config(config: "Coqpit", characters: "BaseCharacters" = None): + text_cleaner = multilingual_cleaners + CharactersClass = VitsCharacters + characters, new_config = CharactersClass.init_from_config(config) + # new_config.characters.characters_class = get_import_path(characters) + new_config.characters.characters_class = VitsCharacters + return ( + TTSTokenizer(text_cleaner, characters),new_config) + + +def multilingual_cleaners(text): + """Pipeline for multilingual text""" + text = lowercase(text) + text = replace_symbols(text, lang=None) + text = remove_aux_symbols(text) + text = collapse_whitespace(text) + return text + +def lowercase(text): + return text.lower() + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text).strip() + +def replace_symbols(text, lang="en"): + + text = text.replace(";", ",") + text = text.replace("-", " ") if lang != "ca" else text.replace("-", "") + text = text.replace(":", ",") + if lang == "en": + text = text.replace("&", " and ") + elif lang == "fr": + text = text.replace("&", " et ") + elif lang == "pt": + text = text.replace("&", " e ") + elif lang == "ca": + text = text.replace("&", " i ") + text = text.replace("'", "") + return text + +def remove_aux_symbols(text): + text = re.sub(r"[\<\>\(\)\[\]\"]+", "", text) + return text \ No newline at end of file diff --git a/models/mr_male/jit_infer.py b/models/mr_male/jit_infer.py new file mode 100644 index 0000000000000000000000000000000000000000..15234585333a4aec39bce3c52b1f76e0d19ccc2a --- /dev/null +++ b/models/mr_male/jit_infer.py @@ -0,0 +1,32 @@ +import os +from extra import TTSTokenizer, VitsConfig, CharactersConfig, VitsCharacters +import torch +import numpy as np + +#ch female +with open("chars.txt", 'r') as f: + letters = f.read().strip('\n') +model="mr_male_vits_30hrs.pt" +text = "फिल्म गर्दिश में अमरीश पुरी के साथ जैकी श्रॉफ, ऐश्वर्या, डिंपल कपाड़िया" + +config = VitsConfig( + text_cleaner="multilingual_cleaners", + characters=CharactersConfig( + characters_class=VitsCharacters, + pad="", + eos="", + bos="", + blank="", + characters=letters, + punctuations="!¡'(),-.:;¿? ", + phonemes=None) + ) +tokenizer, config = TTSTokenizer.init_from_config(config) + +x = tokenizer.text_to_ids(text) +x = torch.from_numpy(np.array(x)).unsqueeze(0) +net = torch.jit.load(model) +with torch.no_grad(): + out2 = net(x) +import soundfile as sf +sf.write("jit.wav", out2.squeeze().cpu().numpy(), 22050) \ No newline at end of file diff --git a/models/mr_male/mr_male_vits_30hrs.pt b/models/mr_male/mr_male_vits_30hrs.pt new file mode 100644 index 0000000000000000000000000000000000000000..d5417eec77c8e77cf129914adf8cddbfe3da12a3 --- /dev/null +++ b/models/mr_male/mr_male_vits_30hrs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8de04cf53233c6018c4a1d86cad6bbabc61b14fe8601128a0b6a0ca9573d8d64 +size 333274444 diff --git a/models/te_female/.gitattributes b/models/te_female/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b --- /dev/null +++ b/models/te_female/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/models/te_female/README.md b/models/te_female/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b187bb7e7d837a367ccd0862441947ad412c77f7 --- /dev/null +++ b/models/te_female/README.md @@ -0,0 +1,3 @@ +--- +license: cc-by-4.0 +--- diff --git a/models/te_female/chars.txt b/models/te_female/chars.txt new file mode 100644 index 0000000000000000000000000000000000000000..9b1108776b56ab920bc67d48446eaa91ff89af72 --- /dev/null +++ b/models/te_female/chars.txt @@ -0,0 +1 @@ +న,ఇగబవహటంభరఖృౌూయీఉఢెఒపఓఠథదజఐఈణఫఛ'మషధేఔై?శిళఞలఘఆతడఊసఎ్ఏోచకు!"ః ొఝా.అ diff --git a/models/te_female/extra.py b/models/te_female/extra.py new file mode 100644 index 0000000000000000000000000000000000000000..c7db561351da270a7c3931bfe0afefa7bc6d4853 --- /dev/null +++ b/models/te_female/extra.py @@ -0,0 +1,787 @@ +from typing import Callable, Dict, List, Union +from dataclasses import asdict, dataclass, field + + +import re +from dataclasses import replace +from typing import Dict +_whitespace_re = re.compile(r"\s+") + +from dataclasses import dataclass, field +from typing import List + +# from TTS.tts.configs.shared_configs import BaseTTSConfig +# from TTS.tts.models.vits import VitsArgs, VitsAudioConfig + +@dataclass +class CharactersConfig(): + + characters_class: str = None + + # using BaseVocabulary + vocab_dict: Dict = None + + # using on BaseCharacters + pad: str = None + eos: str = None + bos: str = None + blank: str = None + characters: str = None + punctuations: str = None + phonemes: str = None + is_unique: bool = True # for backwards compatibility of models trained with char sets with duplicates + is_sorted: bool = True + + +@dataclass +class BaseTTSConfig(): + + # audio: BaseAudioConfig = field(default_factory=BaseAudioConfig) + # phoneme settings + use_phonemes: bool = False + phonemizer: str = None + phoneme_language: str = None + compute_input_seq_cache: bool = False + text_cleaner: str = None + enable_eos_bos_chars: bool = False + test_sentences_file: str = "" + phoneme_cache_path: str = None + # vocabulary parameters + characters: CharactersConfig = None + add_blank: bool = False + # training params + batch_group_size: int = 0 + loss_masking: bool = None + # dataloading + min_audio_len: int = 1 + max_audio_len: int = float("inf") + min_text_len: int = 1 + max_text_len: int = float("inf") + compute_f0: bool = False + compute_energy: bool = False + compute_linear_spec: bool = False + precompute_num_workers: int = 0 + use_noise_augment: bool = False + start_by_longest: bool = False + shuffle: bool = False + drop_last: bool = False + # dataset + datasets: str = None + # optimizer + optimizer: str = "radam" + optimizer_params: dict = None + # scheduler + lr_scheduler: str = None + lr_scheduler_params: dict = field(default_factory=lambda: {}) + # testing + test_sentences: List[str] = field(default_factory=lambda: []) + # evaluation + eval_split_max_size: int = None + eval_split_size: float = 0.01 + # weighted samplers + use_speaker_weighted_sampler: bool = False + speaker_weighted_sampler_alpha: float = 1.0 + use_language_weighted_sampler: bool = False + language_weighted_sampler_alpha: float = 1.0 + use_length_weighted_sampler: bool = False + length_weighted_sampler_alpha: float = 1.0 + + +@dataclass +class VitsAudioConfig(): + fft_size: int = 1024 + sample_rate: int = 22050 + win_length: int = 1024 + hop_length: int = 256 + num_mels: int = 80 + mel_fmin: int = 0 + mel_fmax: int = None + +@dataclass +class VitsArgs(): + num_chars: int = 100 + out_channels: int = 513 + spec_segment_size: int = 32 + hidden_channels: int = 192 + hidden_channels_ffn_text_encoder: int = 768 + num_heads_text_encoder: int = 2 + num_layers_text_encoder: int = 6 + kernel_size_text_encoder: int = 3 + dropout_p_text_encoder: float = 0.1 + dropout_p_duration_predictor: float = 0.5 + kernel_size_posterior_encoder: int = 5 + dilation_rate_posterior_encoder: int = 1 + num_layers_posterior_encoder: int = 16 + kernel_size_flow: int = 5 + dilation_rate_flow: int = 1 + num_layers_flow: int = 4 + resblock_type_decoder: str = "1" + resblock_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [3, 7, 11]) + resblock_dilation_sizes_decoder: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]) + upsample_rates_decoder: List[int] = field(default_factory=lambda: [8, 8, 2, 2]) + upsample_initial_channel_decoder: int = 512 + upsample_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [16, 16, 4, 4]) + periods_multi_period_discriminator: List[int] = field(default_factory=lambda: [2, 3, 5, 7, 11]) + use_sdp: bool = True + noise_scale: float = 1.0 + inference_noise_scale: float = 0.667 + length_scale: float = 1 + noise_scale_dp: float = 1.0 + inference_noise_scale_dp: float = 1.0 + max_inference_len: int = None + init_discriminator: bool = True + use_spectral_norm_disriminator: bool = False + use_speaker_embedding: bool = False + num_speakers: int = 0 + speakers_file: str = None + d_vector_file: List[str] = None + speaker_embedding_channels: int = 256 + use_d_vector_file: bool = False + d_vector_dim: int = 0 + detach_dp_input: bool = True + use_language_embedding: bool = False + embedded_language_dim: int = 4 + num_languages: int = 0 + language_ids_file: str = None + use_speaker_encoder_as_loss: bool = False + speaker_encoder_config_path: str = "" + speaker_encoder_model_path: str = "" + condition_dp_on_speaker: bool = True + freeze_encoder: bool = False + freeze_DP: bool = False + freeze_PE: bool = False + freeze_flow_decoder: bool = False + freeze_waveform_decoder: bool = False + encoder_sample_rate: int = None + interpolate_z: bool = True + reinit_DP: bool = False + reinit_text_encoder: bool = False +@dataclass +class VitsConfig(BaseTTSConfig): + + model: str = "vits" + # model specific params + model_args: VitsArgs = field(default_factory=VitsArgs) + audio: VitsAudioConfig = field(default_factory=VitsAudioConfig) + + # optimizer + grad_clip: List[float] = field(default_factory=lambda: [1000, 1000]) + lr_gen: float = 0.0002 + lr_disc: float = 0.0002 + lr_scheduler_gen: str = "ExponentialLR" + lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + lr_scheduler_disc: str = "ExponentialLR" + lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + scheduler_after_epoch: bool = True + optimizer: str = "AdamW" + optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01}) + + # loss params + kl_loss_alpha: float = 1.0 + disc_loss_alpha: float = 1.0 + gen_loss_alpha: float = 1.0 + feat_loss_alpha: float = 1.0 + mel_loss_alpha: float = 45.0 + dur_loss_alpha: float = 1.0 + speaker_encoder_loss_alpha: float = 1.0 + + # data loader params + return_wav: bool = True + compute_linear_spec: bool = True + + # sampler params + use_weighted_sampler: bool = False # TODO: move it to the base config + weighted_sampler_attrs: dict = field(default_factory=lambda: {}) + weighted_sampler_multipliers: dict = field(default_factory=lambda: {}) + + # overrides + r: int = 1 # DO NOT CHANGE + add_blank: bool = True + + # testing + test_sentences: List[List] = field( + default_factory=lambda: [ + ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."], + ["Be a voice, not an echo."], + ["I'm sorry Dave. I'm afraid I can't do that."], + ["This cake is great. It's so delicious and moist."], + ["Prior to November 22, 1963."], + ] + ) + + # multi-speaker settings + # use speaker embedding layer + num_speakers: int = 0 + use_speaker_embedding: bool = False + speakers_file: str = None + speaker_embedding_channels: int = 256 + language_ids_file: str = None + use_language_embedding: bool = False + + # use d-vectors + use_d_vector_file: bool = False + d_vector_file: List[str] = None + d_vector_dim: int = None + + def __post_init__(self): + pass + # for key, val in self.model_args.items(): + # if hasattr(self, key): + # self[key] = val + + + + + +def parse_symbols(): + return { + "pad": _pad, + "eos": _eos, + "bos": _bos, + "characters": _characters, + "punctuations": _punctuations, + "phonemes": _phonemes, + } + + +# DEFAULT SET OF GRAPHEMES +_pad = "" +_eos = "" +_bos = "" +_blank = "" # TODO: check if we need this alongside with PAD +_characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +_punctuations = "!'(),-.:;? " + + +# DEFAULT SET OF IPA PHONEMES +# Phonemes definition (All IPA characters) +_vowels = "iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻ" +_non_pulmonic_consonants = "ʘɓǀɗǃʄǂɠǁʛ" +_pulmonic_consonants = "pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟ" +_suprasegmentals = "ˈˌːˑ" +_other_symbols = "ʍwɥʜʢʡɕʑɺɧʲ" +_diacrilics = "ɚ˞ɫ" +_phonemes = _vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics + + +class BaseVocabulary: + """Base Vocabulary class. + + This class only needs a vocabulary dictionary without specifying the characters. + + Args: + vocab (Dict): A dictionary of characters and their corresponding indices. + """ + + def __init__(self, vocab: Dict, pad: str = None, blank: str = None, bos: str = None, eos: str = None): + self.vocab = vocab + self.pad = pad + self.blank = blank + self.bos = bos + self.eos = eos + + @property + def pad_id(self) -> int: + """Return the index of the padding character. If the padding character is not specified, return the length + of the vocabulary.""" + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + """Return the index of the blank character. If the blank character is not specified, return the length of + the vocabulary.""" + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def bos_id(self) -> int: + """Return the index of the bos character. If the bos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def eos_id(self) -> int: + """Return the index of the eos character. If the eos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def vocab(self): + """Return the vocabulary dictionary.""" + return self._vocab + + @vocab.setter + def vocab(self, vocab): + """Set the vocabulary dictionary and character mapping dictionaries.""" + self._vocab, self._char_to_id, self._id_to_char = None, None, None + if vocab is not None: + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self._vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self._vocab) # pylint: disable=unnecessary-comprehension + } + + @staticmethod + def init_from_config(config, **kwargs): + """Initialize from the given config.""" + if config.characters is not None and "vocab_dict" in config.characters and config.characters.vocab_dict: + return ( + BaseVocabulary( + config.characters.vocab_dict, + config.characters.pad, + config.characters.blank, + config.characters.bos, + config.characters.eos, + ), + config, + ) + return BaseVocabulary(**kwargs), config + + def to_config(self): + return CharactersConfig( + vocab_dict=self._vocab, + pad=self.pad, + eos=self.eos, + bos=self.bos, + blank=self.blank, + is_unique=False, + is_sorted=False, + ) + + @property + def num_chars(self): + """Return number of tokens in the vocabulary.""" + return len(self._vocab) + + def char_to_id(self, char: str) -> int: + """Map a character to an token ID.""" + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + """Map an token ID to a character.""" + return self._id_to_char[idx] + + +class BaseCharacters: + + + def __init__( + self, + characters: str = None, + punctuations: str = None, + pad: str = None, + eos: str = None, + bos: str = None, + blank: str = None, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + self._characters = characters + self._punctuations = punctuations + self._pad = pad + self._eos = eos + self._bos = bos + self._blank = blank + self.is_unique = is_unique + self.is_sorted = is_sorted + self._create_vocab() + + @property + def pad_id(self) -> int: + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def eos_id(self) -> int: + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def bos_id(self) -> int: + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, characters): + self._characters = characters + self._create_vocab() + + @property + def punctuations(self): + return self._punctuations + + @punctuations.setter + def punctuations(self, punctuations): + self._punctuations = punctuations + self._create_vocab() + + @property + def pad(self): + return self._pad + + @pad.setter + def pad(self, pad): + self._pad = pad + self._create_vocab() + + @property + def eos(self): + return self._eos + + @eos.setter + def eos(self, eos): + self._eos = eos + self._create_vocab() + + @property + def bos(self): + return self._bos + + @bos.setter + def bos(self, bos): + self._bos = bos + self._create_vocab() + + @property + def blank(self): + return self._blank + + @blank.setter + def blank(self, blank): + self._blank = blank + self._create_vocab() + + @property + def vocab(self): + return self._vocab + + @vocab.setter + def vocab(self, vocab): + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self.vocab) # pylint: disable=unnecessary-comprehension + } + + @property + def num_chars(self): + return len(self._vocab) + + def _create_vocab(self): + _vocab = self._characters + if self.is_unique: + _vocab = list(set(_vocab)) + if self.is_sorted: + _vocab = sorted(_vocab) + _vocab = list(_vocab) + _vocab = [self._blank] + _vocab if self._blank is not None and len(self._blank) > 0 else _vocab + _vocab = [self._bos] + _vocab if self._bos is not None and len(self._bos) > 0 else _vocab + _vocab = [self._eos] + _vocab if self._eos is not None and len(self._eos) > 0 else _vocab + _vocab = [self._pad] + _vocab if self._pad is not None and len(self._pad) > 0 else _vocab + self.vocab = _vocab + list(self._punctuations) + if self.is_unique: + duplicates = {x for x in self.vocab if self.vocab.count(x) > 1} + assert ( + len(self.vocab) == len(self._char_to_id) == len(self._id_to_char) + ), f" [!] There are duplicate characters in the character set. {duplicates}" + + def char_to_id(self, char: str) -> int: + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + return self._id_to_char[idx] + + def print_log(self, level: int = 0): + """ + Prints the vocabulary in a nice format. + """ + indent = "\t" * level + print(f"{indent}| > Characters: {self._characters}") + print(f"{indent}| > Punctuations: {self._punctuations}") + print(f"{indent}| > Pad: {self._pad}") + print(f"{indent}| > EOS: {self._eos}") + print(f"{indent}| > BOS: {self._bos}") + print(f"{indent}| > Blank: {self._blank}") + print(f"{indent}| > Vocab: {self.vocab}") + print(f"{indent}| > Num chars: {self.num_chars}") + + @staticmethod + def init_from_config(config: "Coqpit"): # pylint: disable=unused-argument + """Init your character class from a config. + + Implement this method for your subclass. + """ + # use character set from config + if config.characters is not None: + return BaseCharacters(**config.characters), config + # return default character set + characters = BaseCharacters() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=self._eos, + bos=self._bos, + blank=self._blank, + is_unique=self.is_unique, + is_sorted=self.is_sorted, + ) + + +class IPAPhonemes(BaseCharacters): + + + def __init__( + self, + characters: str = _phonemes, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a IPAPhonemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + # band-aid for compatibility with old models + if "characters" in config and config.characters is not None: + if "phonemes" in config.characters and config.characters.phonemes is not None: + config.characters["characters"] = config.characters["phonemes"] + return ( + IPAPhonemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + # use character set from config + if config.characters is not None: + return IPAPhonemes(**config.characters), config + # return default character set + characters = IPAPhonemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +class Graphemes(BaseCharacters): + + + def __init__( + self, + characters: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a Graphemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + if config.characters is not None: + # band-aid for compatibility with old models + if "phonemes" in config.characters: + return ( + Graphemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + return Graphemes(**config.characters), config + characters = Graphemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +if __name__ == "__main__": + gr = Graphemes() + ph = IPAPhonemes() + gr.print_log() + ph.print_log() + + +class VitsCharacters(BaseCharacters): + """Characters class for VITs model for compatibility with pre-trained models""" + + def __init__( + self, + graphemes: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + ipa_characters: str = _phonemes, + ) -> None: + if ipa_characters is not None: + graphemes += ipa_characters + super().__init__(graphemes, punctuations, pad, None, None, "", is_unique=False, is_sorted=True) + + def _create_vocab(self): + self._vocab = [self._pad] + list(self._punctuations) + list(self._characters) + [self._blank] + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + # pylint: disable=unnecessary-comprehension + self._id_to_char = {idx: char for idx, char in enumerate(self.vocab)} + + @staticmethod + def init_from_config(config): + _pad = config.characters.pad + _punctuations = config.characters.punctuations + _letters = config.characters.characters + _letters_ipa = config.characters.phonemes + return ( + VitsCharacters(graphemes=_letters, ipa_characters=_letters_ipa, punctuations=_punctuations, pad=_pad), + config, + ) + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=None, + bos=None, + blank=self._blank, + is_unique=False, + is_sorted=True, + ) + +class TTSTokenizer: + def __init__( + self, + text_cleaner: Callable = None, + characters: "BaseCharacters" = None, + ): + self.text_cleaner = text_cleaner + self.characters = characters + self.not_found_characters = [] + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, new_characters): + self._characters = new_characters + self.pad_id = self.characters.char_to_id(self.characters.pad) if self.characters.pad else None + self.blank_id = self.characters.char_to_id(self.characters.blank) if self.characters.blank else None + + def encode(self, text: str) -> List[int]: + """Encodes a string of text as a sequence of IDs.""" + token_ids = [] + for char in text: + try: + idx = self.characters.char_to_id(char) + token_ids.append(idx) + except KeyError: + # discard but store not found characters + if char not in self.not_found_characters: + self.not_found_characters.append(char) + print(text) + print(f" [!] Character {repr(char)} not found in the vocabulary. Discarding it.") + return token_ids + + def text_to_ids(self, text: str, language: str = None) -> List[int]: # pylint: disable=unused-argument + text = self.text_cleaner(text) + text = self.encode(text) + text = self.intersperse_blank_char(text, True) + return text + + def pad_with_bos_eos(self, char_sequence: List[str]): + """Pads a sequence with the special BOS and EOS characters.""" + return [self.characters.bos_id] + list(char_sequence) + [self.characters.eos_id] + + def intersperse_blank_char(self, char_sequence: List[str], use_blank_char: bool = False): + """Intersperses the blank character between characters in a sequence. + + Use the ```blank``` character if defined else use the ```pad``` character. + """ + char_to_use = self.characters.blank_id if use_blank_char else self.characters.pad + result = [char_to_use] * (len(char_sequence) * 2 + 1) + result[1::2] = char_sequence + return result + + @staticmethod + def init_from_config(config: "Coqpit", characters: "BaseCharacters" = None): + text_cleaner = multilingual_cleaners + CharactersClass = VitsCharacters + characters, new_config = CharactersClass.init_from_config(config) + # new_config.characters.characters_class = get_import_path(characters) + new_config.characters.characters_class = VitsCharacters + return ( + TTSTokenizer(text_cleaner, characters),new_config) + + +def multilingual_cleaners(text): + """Pipeline for multilingual text""" + text = lowercase(text) + text = replace_symbols(text, lang=None) + text = remove_aux_symbols(text) + text = collapse_whitespace(text) + return text + +def lowercase(text): + return text.lower() + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text).strip() + +def replace_symbols(text, lang="en"): + + text = text.replace(";", ",") + text = text.replace("-", " ") if lang != "ca" else text.replace("-", "") + text = text.replace(":", ",") + if lang == "en": + text = text.replace("&", " and ") + elif lang == "fr": + text = text.replace("&", " et ") + elif lang == "pt": + text = text.replace("&", " e ") + elif lang == "ca": + text = text.replace("&", " i ") + text = text.replace("'", "") + return text + +def remove_aux_symbols(text): + text = re.sub(r"[\<\>\(\)\[\]\"]+", "", text) + return text \ No newline at end of file diff --git a/models/te_female/jit_infer.py b/models/te_female/jit_infer.py new file mode 100644 index 0000000000000000000000000000000000000000..de184a532ee10dec8798017c719947ce4b08f9e3 --- /dev/null +++ b/models/te_female/jit_infer.py @@ -0,0 +1,32 @@ +import os +from extra import TTSTokenizer, VitsConfig, CharactersConfig, VitsCharacters +import torch +import numpy as np + +#ch female +with open("chars.txt", 'r') as f: + letters = f.read().strip('\n') +model="te_female_vits_30hrs.pt" +text = "ఒక ప్రాంత జనాభాలో ఉదాహరణకు ఒక సంవత్సర కాలంలో మరణాల కంటే జననాలు ఎక్కువ ఉంటే జనాభా పెరుగుతుంది." + +config = VitsConfig( + text_cleaner="multilingual_cleaners", + characters=CharactersConfig( + characters_class=VitsCharacters, + pad="", + eos="", + bos="", + blank="", + characters=letters, + punctuations="!¡'(),-.:;¿? ", + phonemes=None) + ) +tokenizer, config = TTSTokenizer.init_from_config(config) + +x = tokenizer.text_to_ids(text) +x = torch.from_numpy(np.array(x)).unsqueeze(0) +net = torch.jit.load(model) +with torch.no_grad(): + out2 = net(x) +import soundfile as sf +sf.write("jit.wav", out2.squeeze().cpu().numpy(), 22050) \ No newline at end of file diff --git a/models/te_female/te_female_vits_30hrs.pt b/models/te_female/te_female_vits_30hrs.pt new file mode 100644 index 0000000000000000000000000000000000000000..4c1dc677673a9c920819898641b5027a6b9c5d4e --- /dev/null +++ b/models/te_female/te_female_vits_30hrs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:07ed79f7b1bf93d9759e13fbbbe1e333082724f2ee5cb29cdf94f86b45e298b5 +size 333252998 diff --git a/models/te_male/.gitattributes b/models/te_male/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..a6344aac8c09253b3b630fb776ae94478aa0275b --- /dev/null +++ b/models/te_male/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/models/te_male/README.md b/models/te_male/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b187bb7e7d837a367ccd0862441947ad412c77f7 --- /dev/null +++ b/models/te_male/README.md @@ -0,0 +1,3 @@ +--- +license: cc-by-4.0 +--- diff --git a/models/te_male/chars.txt b/models/te_male/chars.txt new file mode 100644 index 0000000000000000000000000000000000000000..fdeed32b85d15e4f4353098430011fddeea19fa1 --- /dev/null +++ b/models/te_male/chars.txt @@ -0,0 +1 @@ +న,ఇగబవహటంభరఖృౌూీయఉఢఒెపఓఠథదజఐఈఫణ'ఛఁమషధేఔై?శిళఊలఘఆతడఞసఎ్ఏోచకు"ఙ ొఋఱఝా.అ diff --git a/models/te_male/extra.py b/models/te_male/extra.py new file mode 100644 index 0000000000000000000000000000000000000000..c7db561351da270a7c3931bfe0afefa7bc6d4853 --- /dev/null +++ b/models/te_male/extra.py @@ -0,0 +1,787 @@ +from typing import Callable, Dict, List, Union +from dataclasses import asdict, dataclass, field + + +import re +from dataclasses import replace +from typing import Dict +_whitespace_re = re.compile(r"\s+") + +from dataclasses import dataclass, field +from typing import List + +# from TTS.tts.configs.shared_configs import BaseTTSConfig +# from TTS.tts.models.vits import VitsArgs, VitsAudioConfig + +@dataclass +class CharactersConfig(): + + characters_class: str = None + + # using BaseVocabulary + vocab_dict: Dict = None + + # using on BaseCharacters + pad: str = None + eos: str = None + bos: str = None + blank: str = None + characters: str = None + punctuations: str = None + phonemes: str = None + is_unique: bool = True # for backwards compatibility of models trained with char sets with duplicates + is_sorted: bool = True + + +@dataclass +class BaseTTSConfig(): + + # audio: BaseAudioConfig = field(default_factory=BaseAudioConfig) + # phoneme settings + use_phonemes: bool = False + phonemizer: str = None + phoneme_language: str = None + compute_input_seq_cache: bool = False + text_cleaner: str = None + enable_eos_bos_chars: bool = False + test_sentences_file: str = "" + phoneme_cache_path: str = None + # vocabulary parameters + characters: CharactersConfig = None + add_blank: bool = False + # training params + batch_group_size: int = 0 + loss_masking: bool = None + # dataloading + min_audio_len: int = 1 + max_audio_len: int = float("inf") + min_text_len: int = 1 + max_text_len: int = float("inf") + compute_f0: bool = False + compute_energy: bool = False + compute_linear_spec: bool = False + precompute_num_workers: int = 0 + use_noise_augment: bool = False + start_by_longest: bool = False + shuffle: bool = False + drop_last: bool = False + # dataset + datasets: str = None + # optimizer + optimizer: str = "radam" + optimizer_params: dict = None + # scheduler + lr_scheduler: str = None + lr_scheduler_params: dict = field(default_factory=lambda: {}) + # testing + test_sentences: List[str] = field(default_factory=lambda: []) + # evaluation + eval_split_max_size: int = None + eval_split_size: float = 0.01 + # weighted samplers + use_speaker_weighted_sampler: bool = False + speaker_weighted_sampler_alpha: float = 1.0 + use_language_weighted_sampler: bool = False + language_weighted_sampler_alpha: float = 1.0 + use_length_weighted_sampler: bool = False + length_weighted_sampler_alpha: float = 1.0 + + +@dataclass +class VitsAudioConfig(): + fft_size: int = 1024 + sample_rate: int = 22050 + win_length: int = 1024 + hop_length: int = 256 + num_mels: int = 80 + mel_fmin: int = 0 + mel_fmax: int = None + +@dataclass +class VitsArgs(): + num_chars: int = 100 + out_channels: int = 513 + spec_segment_size: int = 32 + hidden_channels: int = 192 + hidden_channels_ffn_text_encoder: int = 768 + num_heads_text_encoder: int = 2 + num_layers_text_encoder: int = 6 + kernel_size_text_encoder: int = 3 + dropout_p_text_encoder: float = 0.1 + dropout_p_duration_predictor: float = 0.5 + kernel_size_posterior_encoder: int = 5 + dilation_rate_posterior_encoder: int = 1 + num_layers_posterior_encoder: int = 16 + kernel_size_flow: int = 5 + dilation_rate_flow: int = 1 + num_layers_flow: int = 4 + resblock_type_decoder: str = "1" + resblock_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [3, 7, 11]) + resblock_dilation_sizes_decoder: List[List[int]] = field(default_factory=lambda: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]) + upsample_rates_decoder: List[int] = field(default_factory=lambda: [8, 8, 2, 2]) + upsample_initial_channel_decoder: int = 512 + upsample_kernel_sizes_decoder: List[int] = field(default_factory=lambda: [16, 16, 4, 4]) + periods_multi_period_discriminator: List[int] = field(default_factory=lambda: [2, 3, 5, 7, 11]) + use_sdp: bool = True + noise_scale: float = 1.0 + inference_noise_scale: float = 0.667 + length_scale: float = 1 + noise_scale_dp: float = 1.0 + inference_noise_scale_dp: float = 1.0 + max_inference_len: int = None + init_discriminator: bool = True + use_spectral_norm_disriminator: bool = False + use_speaker_embedding: bool = False + num_speakers: int = 0 + speakers_file: str = None + d_vector_file: List[str] = None + speaker_embedding_channels: int = 256 + use_d_vector_file: bool = False + d_vector_dim: int = 0 + detach_dp_input: bool = True + use_language_embedding: bool = False + embedded_language_dim: int = 4 + num_languages: int = 0 + language_ids_file: str = None + use_speaker_encoder_as_loss: bool = False + speaker_encoder_config_path: str = "" + speaker_encoder_model_path: str = "" + condition_dp_on_speaker: bool = True + freeze_encoder: bool = False + freeze_DP: bool = False + freeze_PE: bool = False + freeze_flow_decoder: bool = False + freeze_waveform_decoder: bool = False + encoder_sample_rate: int = None + interpolate_z: bool = True + reinit_DP: bool = False + reinit_text_encoder: bool = False +@dataclass +class VitsConfig(BaseTTSConfig): + + model: str = "vits" + # model specific params + model_args: VitsArgs = field(default_factory=VitsArgs) + audio: VitsAudioConfig = field(default_factory=VitsAudioConfig) + + # optimizer + grad_clip: List[float] = field(default_factory=lambda: [1000, 1000]) + lr_gen: float = 0.0002 + lr_disc: float = 0.0002 + lr_scheduler_gen: str = "ExponentialLR" + lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + lr_scheduler_disc: str = "ExponentialLR" + lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1}) + scheduler_after_epoch: bool = True + optimizer: str = "AdamW" + optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01}) + + # loss params + kl_loss_alpha: float = 1.0 + disc_loss_alpha: float = 1.0 + gen_loss_alpha: float = 1.0 + feat_loss_alpha: float = 1.0 + mel_loss_alpha: float = 45.0 + dur_loss_alpha: float = 1.0 + speaker_encoder_loss_alpha: float = 1.0 + + # data loader params + return_wav: bool = True + compute_linear_spec: bool = True + + # sampler params + use_weighted_sampler: bool = False # TODO: move it to the base config + weighted_sampler_attrs: dict = field(default_factory=lambda: {}) + weighted_sampler_multipliers: dict = field(default_factory=lambda: {}) + + # overrides + r: int = 1 # DO NOT CHANGE + add_blank: bool = True + + # testing + test_sentences: List[List] = field( + default_factory=lambda: [ + ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."], + ["Be a voice, not an echo."], + ["I'm sorry Dave. I'm afraid I can't do that."], + ["This cake is great. It's so delicious and moist."], + ["Prior to November 22, 1963."], + ] + ) + + # multi-speaker settings + # use speaker embedding layer + num_speakers: int = 0 + use_speaker_embedding: bool = False + speakers_file: str = None + speaker_embedding_channels: int = 256 + language_ids_file: str = None + use_language_embedding: bool = False + + # use d-vectors + use_d_vector_file: bool = False + d_vector_file: List[str] = None + d_vector_dim: int = None + + def __post_init__(self): + pass + # for key, val in self.model_args.items(): + # if hasattr(self, key): + # self[key] = val + + + + + +def parse_symbols(): + return { + "pad": _pad, + "eos": _eos, + "bos": _bos, + "characters": _characters, + "punctuations": _punctuations, + "phonemes": _phonemes, + } + + +# DEFAULT SET OF GRAPHEMES +_pad = "" +_eos = "" +_bos = "" +_blank = "" # TODO: check if we need this alongside with PAD +_characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz" +_punctuations = "!'(),-.:;? " + + +# DEFAULT SET OF IPA PHONEMES +# Phonemes definition (All IPA characters) +_vowels = "iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻ" +_non_pulmonic_consonants = "ʘɓǀɗǃʄǂɠǁʛ" +_pulmonic_consonants = "pbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟ" +_suprasegmentals = "ˈˌːˑ" +_other_symbols = "ʍwɥʜʢʡɕʑɺɧʲ" +_diacrilics = "ɚ˞ɫ" +_phonemes = _vowels + _non_pulmonic_consonants + _pulmonic_consonants + _suprasegmentals + _other_symbols + _diacrilics + + +class BaseVocabulary: + """Base Vocabulary class. + + This class only needs a vocabulary dictionary without specifying the characters. + + Args: + vocab (Dict): A dictionary of characters and their corresponding indices. + """ + + def __init__(self, vocab: Dict, pad: str = None, blank: str = None, bos: str = None, eos: str = None): + self.vocab = vocab + self.pad = pad + self.blank = blank + self.bos = bos + self.eos = eos + + @property + def pad_id(self) -> int: + """Return the index of the padding character. If the padding character is not specified, return the length + of the vocabulary.""" + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + """Return the index of the blank character. If the blank character is not specified, return the length of + the vocabulary.""" + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def bos_id(self) -> int: + """Return the index of the bos character. If the bos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def eos_id(self) -> int: + """Return the index of the eos character. If the eos character is not specified, return the length of the + vocabulary.""" + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def vocab(self): + """Return the vocabulary dictionary.""" + return self._vocab + + @vocab.setter + def vocab(self, vocab): + """Set the vocabulary dictionary and character mapping dictionaries.""" + self._vocab, self._char_to_id, self._id_to_char = None, None, None + if vocab is not None: + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self._vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self._vocab) # pylint: disable=unnecessary-comprehension + } + + @staticmethod + def init_from_config(config, **kwargs): + """Initialize from the given config.""" + if config.characters is not None and "vocab_dict" in config.characters and config.characters.vocab_dict: + return ( + BaseVocabulary( + config.characters.vocab_dict, + config.characters.pad, + config.characters.blank, + config.characters.bos, + config.characters.eos, + ), + config, + ) + return BaseVocabulary(**kwargs), config + + def to_config(self): + return CharactersConfig( + vocab_dict=self._vocab, + pad=self.pad, + eos=self.eos, + bos=self.bos, + blank=self.blank, + is_unique=False, + is_sorted=False, + ) + + @property + def num_chars(self): + """Return number of tokens in the vocabulary.""" + return len(self._vocab) + + def char_to_id(self, char: str) -> int: + """Map a character to an token ID.""" + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + """Map an token ID to a character.""" + return self._id_to_char[idx] + + +class BaseCharacters: + + + def __init__( + self, + characters: str = None, + punctuations: str = None, + pad: str = None, + eos: str = None, + bos: str = None, + blank: str = None, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + self._characters = characters + self._punctuations = punctuations + self._pad = pad + self._eos = eos + self._bos = bos + self._blank = blank + self.is_unique = is_unique + self.is_sorted = is_sorted + self._create_vocab() + + @property + def pad_id(self) -> int: + return self.char_to_id(self.pad) if self.pad else len(self.vocab) + + @property + def blank_id(self) -> int: + return self.char_to_id(self.blank) if self.blank else len(self.vocab) + + @property + def eos_id(self) -> int: + return self.char_to_id(self.eos) if self.eos else len(self.vocab) + + @property + def bos_id(self) -> int: + return self.char_to_id(self.bos) if self.bos else len(self.vocab) + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, characters): + self._characters = characters + self._create_vocab() + + @property + def punctuations(self): + return self._punctuations + + @punctuations.setter + def punctuations(self, punctuations): + self._punctuations = punctuations + self._create_vocab() + + @property + def pad(self): + return self._pad + + @pad.setter + def pad(self, pad): + self._pad = pad + self._create_vocab() + + @property + def eos(self): + return self._eos + + @eos.setter + def eos(self, eos): + self._eos = eos + self._create_vocab() + + @property + def bos(self): + return self._bos + + @bos.setter + def bos(self, bos): + self._bos = bos + self._create_vocab() + + @property + def blank(self): + return self._blank + + @blank.setter + def blank(self, blank): + self._blank = blank + self._create_vocab() + + @property + def vocab(self): + return self._vocab + + @vocab.setter + def vocab(self, vocab): + self._vocab = vocab + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + self._id_to_char = { + idx: char for idx, char in enumerate(self.vocab) # pylint: disable=unnecessary-comprehension + } + + @property + def num_chars(self): + return len(self._vocab) + + def _create_vocab(self): + _vocab = self._characters + if self.is_unique: + _vocab = list(set(_vocab)) + if self.is_sorted: + _vocab = sorted(_vocab) + _vocab = list(_vocab) + _vocab = [self._blank] + _vocab if self._blank is not None and len(self._blank) > 0 else _vocab + _vocab = [self._bos] + _vocab if self._bos is not None and len(self._bos) > 0 else _vocab + _vocab = [self._eos] + _vocab if self._eos is not None and len(self._eos) > 0 else _vocab + _vocab = [self._pad] + _vocab if self._pad is not None and len(self._pad) > 0 else _vocab + self.vocab = _vocab + list(self._punctuations) + if self.is_unique: + duplicates = {x for x in self.vocab if self.vocab.count(x) > 1} + assert ( + len(self.vocab) == len(self._char_to_id) == len(self._id_to_char) + ), f" [!] There are duplicate characters in the character set. {duplicates}" + + def char_to_id(self, char: str) -> int: + try: + return self._char_to_id[char] + except KeyError as e: + raise KeyError(f" [!] {repr(char)} is not in the vocabulary.") from e + + def id_to_char(self, idx: int) -> str: + return self._id_to_char[idx] + + def print_log(self, level: int = 0): + """ + Prints the vocabulary in a nice format. + """ + indent = "\t" * level + print(f"{indent}| > Characters: {self._characters}") + print(f"{indent}| > Punctuations: {self._punctuations}") + print(f"{indent}| > Pad: {self._pad}") + print(f"{indent}| > EOS: {self._eos}") + print(f"{indent}| > BOS: {self._bos}") + print(f"{indent}| > Blank: {self._blank}") + print(f"{indent}| > Vocab: {self.vocab}") + print(f"{indent}| > Num chars: {self.num_chars}") + + @staticmethod + def init_from_config(config: "Coqpit"): # pylint: disable=unused-argument + """Init your character class from a config. + + Implement this method for your subclass. + """ + # use character set from config + if config.characters is not None: + return BaseCharacters(**config.characters), config + # return default character set + characters = BaseCharacters() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=self._eos, + bos=self._bos, + blank=self._blank, + is_unique=self.is_unique, + is_sorted=self.is_sorted, + ) + + +class IPAPhonemes(BaseCharacters): + + + def __init__( + self, + characters: str = _phonemes, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a IPAPhonemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + # band-aid for compatibility with old models + if "characters" in config and config.characters is not None: + if "phonemes" in config.characters and config.characters.phonemes is not None: + config.characters["characters"] = config.characters["phonemes"] + return ( + IPAPhonemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + # use character set from config + if config.characters is not None: + return IPAPhonemes(**config.characters), config + # return default character set + characters = IPAPhonemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +class Graphemes(BaseCharacters): + + + def __init__( + self, + characters: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + eos: str = _eos, + bos: str = _bos, + blank: str = _blank, + is_unique: bool = False, + is_sorted: bool = True, + ) -> None: + super().__init__(characters, punctuations, pad, eos, bos, blank, is_unique, is_sorted) + + @staticmethod + def init_from_config(config: "Coqpit"): + """Init a Graphemes object from a model config + + If characters are not defined in the config, it will be set to the default characters and the config + will be updated. + """ + if config.characters is not None: + # band-aid for compatibility with old models + if "phonemes" in config.characters: + return ( + Graphemes( + characters=config.characters["characters"], + punctuations=config.characters["punctuations"], + pad=config.characters["pad"], + eos=config.characters["eos"], + bos=config.characters["bos"], + blank=config.characters["blank"], + is_unique=config.characters["is_unique"], + is_sorted=config.characters["is_sorted"], + ), + config, + ) + return Graphemes(**config.characters), config + characters = Graphemes() + new_config = replace(config, characters=characters.to_config()) + return characters, new_config + + +if __name__ == "__main__": + gr = Graphemes() + ph = IPAPhonemes() + gr.print_log() + ph.print_log() + + +class VitsCharacters(BaseCharacters): + """Characters class for VITs model for compatibility with pre-trained models""" + + def __init__( + self, + graphemes: str = _characters, + punctuations: str = _punctuations, + pad: str = _pad, + ipa_characters: str = _phonemes, + ) -> None: + if ipa_characters is not None: + graphemes += ipa_characters + super().__init__(graphemes, punctuations, pad, None, None, "", is_unique=False, is_sorted=True) + + def _create_vocab(self): + self._vocab = [self._pad] + list(self._punctuations) + list(self._characters) + [self._blank] + self._char_to_id = {char: idx for idx, char in enumerate(self.vocab)} + # pylint: disable=unnecessary-comprehension + self._id_to_char = {idx: char for idx, char in enumerate(self.vocab)} + + @staticmethod + def init_from_config(config): + _pad = config.characters.pad + _punctuations = config.characters.punctuations + _letters = config.characters.characters + _letters_ipa = config.characters.phonemes + return ( + VitsCharacters(graphemes=_letters, ipa_characters=_letters_ipa, punctuations=_punctuations, pad=_pad), + config, + ) + + def to_config(self) -> "CharactersConfig": + return CharactersConfig( + characters=self._characters, + punctuations=self._punctuations, + pad=self._pad, + eos=None, + bos=None, + blank=self._blank, + is_unique=False, + is_sorted=True, + ) + +class TTSTokenizer: + def __init__( + self, + text_cleaner: Callable = None, + characters: "BaseCharacters" = None, + ): + self.text_cleaner = text_cleaner + self.characters = characters + self.not_found_characters = [] + + @property + def characters(self): + return self._characters + + @characters.setter + def characters(self, new_characters): + self._characters = new_characters + self.pad_id = self.characters.char_to_id(self.characters.pad) if self.characters.pad else None + self.blank_id = self.characters.char_to_id(self.characters.blank) if self.characters.blank else None + + def encode(self, text: str) -> List[int]: + """Encodes a string of text as a sequence of IDs.""" + token_ids = [] + for char in text: + try: + idx = self.characters.char_to_id(char) + token_ids.append(idx) + except KeyError: + # discard but store not found characters + if char not in self.not_found_characters: + self.not_found_characters.append(char) + print(text) + print(f" [!] Character {repr(char)} not found in the vocabulary. Discarding it.") + return token_ids + + def text_to_ids(self, text: str, language: str = None) -> List[int]: # pylint: disable=unused-argument + text = self.text_cleaner(text) + text = self.encode(text) + text = self.intersperse_blank_char(text, True) + return text + + def pad_with_bos_eos(self, char_sequence: List[str]): + """Pads a sequence with the special BOS and EOS characters.""" + return [self.characters.bos_id] + list(char_sequence) + [self.characters.eos_id] + + def intersperse_blank_char(self, char_sequence: List[str], use_blank_char: bool = False): + """Intersperses the blank character between characters in a sequence. + + Use the ```blank``` character if defined else use the ```pad``` character. + """ + char_to_use = self.characters.blank_id if use_blank_char else self.characters.pad + result = [char_to_use] * (len(char_sequence) * 2 + 1) + result[1::2] = char_sequence + return result + + @staticmethod + def init_from_config(config: "Coqpit", characters: "BaseCharacters" = None): + text_cleaner = multilingual_cleaners + CharactersClass = VitsCharacters + characters, new_config = CharactersClass.init_from_config(config) + # new_config.characters.characters_class = get_import_path(characters) + new_config.characters.characters_class = VitsCharacters + return ( + TTSTokenizer(text_cleaner, characters),new_config) + + +def multilingual_cleaners(text): + """Pipeline for multilingual text""" + text = lowercase(text) + text = replace_symbols(text, lang=None) + text = remove_aux_symbols(text) + text = collapse_whitespace(text) + return text + +def lowercase(text): + return text.lower() + +def collapse_whitespace(text): + return re.sub(_whitespace_re, " ", text).strip() + +def replace_symbols(text, lang="en"): + + text = text.replace(";", ",") + text = text.replace("-", " ") if lang != "ca" else text.replace("-", "") + text = text.replace(":", ",") + if lang == "en": + text = text.replace("&", " and ") + elif lang == "fr": + text = text.replace("&", " et ") + elif lang == "pt": + text = text.replace("&", " e ") + elif lang == "ca": + text = text.replace("&", " i ") + text = text.replace("'", "") + return text + +def remove_aux_symbols(text): + text = re.sub(r"[\<\>\(\)\[\]\"]+", "", text) + return text \ No newline at end of file diff --git a/models/te_male/jit_infer.py b/models/te_male/jit_infer.py new file mode 100644 index 0000000000000000000000000000000000000000..4973b2789ddbf4cd1aea808ca107cbc9cf306765 --- /dev/null +++ b/models/te_male/jit_infer.py @@ -0,0 +1,32 @@ +import os +from extra import TTSTokenizer, VitsConfig, CharactersConfig, VitsCharacters +import torch +import numpy as np + +#ch female +with open("chars.txt", 'r') as f: + letters = f.read().strip('\n') +model="te_male_vits_30hrs.pt" +text = "ఒక ప్రాంత జనాభాలో ఉదాహరణకు ఒక సంవత్సర కాలంలో మరణాల కంటే జననాలు ఎక్కువ ఉంటే జనాభా పెరుగుతుంది." + +config = VitsConfig( + text_cleaner="multilingual_cleaners", + characters=CharactersConfig( + characters_class=VitsCharacters, + pad="", + eos="", + bos="", + blank="", + characters=letters, + punctuations="!¡'(),-.:;¿? ", + phonemes=None) + ) +tokenizer, config = TTSTokenizer.init_from_config(config) + +x = tokenizer.text_to_ids(text) +x = torch.from_numpy(np.array(x)).unsqueeze(0) +net = torch.jit.load(model) +with torch.no_grad(): + out2 = net(x) +import soundfile as sf +sf.write("jit.wav", out2.squeeze().cpu().numpy(), 22050) \ No newline at end of file diff --git a/models/te_male/te_male_vits_30hrs.pt b/models/te_male/te_male_vits_30hrs.pt new file mode 100644 index 0000000000000000000000000000000000000000..cb5dfe73ea9fc27db84496ac3d215e0bfe80b4f8 --- /dev/null +++ b/models/te_male/te_male_vits_30hrs.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1204c5f1296cd606625cefe977409405e7201e2c87b9c7c50535b2966216cfe0 +size 333249100 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..738f7ae1f65f729cf6cadebaf098ab15347f251a --- /dev/null +++ b/requirements.txt @@ -0,0 +1,27 @@ +# Voice Tech for All - Multi-lingual TTS System +# Requirements for the TTS system + +# Core ML/Audio +torch +numpy +scipy +soundfile +librosa # For pitch shifting and time stretching + +# TTS Models +TTS # Coqui TTS - required for Bhojpuri .pth models +huggingface-hub +transformers # Required for MMS models (Gujarati) + +# API Server +fastapi +uvicorn[standard] +python-multipart # Required for file uploads +pydantic + +# Utilities +tqdm +requests + +# Development (optional) +pytest diff --git a/runtime.txt b/runtime.txt new file mode 100644 index 0000000000000000000000000000000000000000..6be87ab790e255b1a8c08b0d654c2bc05df2fdbd --- /dev/null +++ b/runtime.txt @@ -0,0 +1 @@ +3.10.19 \ No newline at end of file diff --git a/src/.DS_Store b/src/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..c9c570bef6279de9b7c09acec44c33d18aa26306 Binary files /dev/null and b/src/.DS_Store differ diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4003ee0b4c1c5c9817982211ad1c91feee1a3eff --- /dev/null +++ b/src/__init__.py @@ -0,0 +1,2 @@ +# Multi-lingual TTS System +# Voice Tech for All Hackathon diff --git a/src/__pycache__/__init__.cpython-310.pyc b/src/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c741dad0c0bbcb1c4c201e8d0cbcbb9c71ded808 Binary files /dev/null and b/src/__pycache__/__init__.cpython-310.pyc differ diff --git a/src/__pycache__/api.cpython-310.pyc b/src/__pycache__/api.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..16b156234aca0b274df3cb03bc38b8ed29d62362 Binary files /dev/null and b/src/__pycache__/api.cpython-310.pyc differ diff --git a/src/__pycache__/cli.cpython-310.pyc b/src/__pycache__/cli.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..739944251a5c1c0faa329c4023d4c803d6cb21b2 Binary files /dev/null and b/src/__pycache__/cli.cpython-310.pyc differ diff --git a/src/__pycache__/config.cpython-310.pyc b/src/__pycache__/config.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c071e0e22629ee3915c885d0fb27d4dad8aa61a0 Binary files /dev/null and b/src/__pycache__/config.cpython-310.pyc differ diff --git a/src/__pycache__/downloader.cpython-310.pyc b/src/__pycache__/downloader.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cc29ab7aeb2a518f9a8dd3d546457d4561bd98f1 Binary files /dev/null and b/src/__pycache__/downloader.cpython-310.pyc differ diff --git a/src/__pycache__/engine.cpython-310.pyc b/src/__pycache__/engine.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3e9cf87f8143f705952fef7dfd048e8565bc535c Binary files /dev/null and b/src/__pycache__/engine.cpython-310.pyc differ diff --git a/src/__pycache__/tokenizer.cpython-310.pyc b/src/__pycache__/tokenizer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..21df1c989bc9de32f821c5550c2b0be923af6583 Binary files /dev/null and b/src/__pycache__/tokenizer.cpython-310.pyc differ diff --git a/src/api.py b/src/api.py new file mode 100644 index 0000000000000000000000000000000000000000..ef858b197d4eeb470ace0d58c1153d0f3ce46688 --- /dev/null +++ b/src/api.py @@ -0,0 +1,541 @@ +""" +REST API Server for Multi-lingual TTS +FastAPI-based server with OpenAPI documentation + +Hackathon API Specification: +- GET /Get_Inference with text, lang, speaker_wav parameters +""" + +import os +import io +import time +import logging +import tempfile +from typing import Optional, List +from pathlib import Path +import numpy as np + +from fastapi import ( + FastAPI, + HTTPException, + Query, + Response, + BackgroundTasks, + UploadFile, + File, +) +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import StreamingResponse, FileResponse, JSONResponse +from pydantic import BaseModel, Field +import soundfile as sf + +from .engine import TTSEngine, TTSOutput +from .config import ( + LANGUAGE_CONFIGS, + get_available_languages, + get_available_voices, + STYLE_PRESETS, +) + +# Language name to voice key mapping (for hackathon API) +LANG_TO_VOICE = { + "hindi": "hi_female", + "bengali": "bn_female", + "marathi": "mr_female", + "telugu": "te_female", + "kannada": "kn_female", + "bhojpuri": "bho_female", + "chhattisgarhi": "hne_female", + "maithili": "mai_female", + "magahi": "mag_female", + "english": "en_female", + "gujarati": "gu_mms", +} + +# Setup logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +# Initialize FastAPI app +app = FastAPI( + title="Voice Tech for All - Multi-lingual TTS API", + description=""" + A multi-lingual Text-to-Speech API supporting 10+ Indian languages. + + ## Features + - 10 Indian languages with male/female voices + - Real-time speech synthesis + - Text normalization for Indian languages + - Speed control + - Multiple audio formats (WAV, MP3) + + ## Supported Languages + Hindi, Bengali, Marathi, Telugu, Kannada, Bhojpuri, + Chhattisgarhi, Maithili, Magahi, English + + ## Use Case + Built for an LLM-based healthcare assistant for pregnant mothers + in low-income communities. + """, + version="1.0.0", + contact={ + "name": "Voice Tech for All Hackathon", + "url": "https://huggingface.co/SYSPIN", + }, + license_info={ + "name": "CC BY 4.0", + "url": "https://creativecommons.org/licenses/by/4.0/", + }, +) + +# CORS middleware +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# Initialize TTS Engine (lazy loading) +_engine: Optional[TTSEngine] = None + + +def get_engine() -> TTSEngine: + """Get or create TTS engine instance""" + global _engine + if _engine is None: + _engine = TTSEngine(device="auto") + return _engine + + +# Request/Response Models +class SynthesizeRequest(BaseModel): + """Request body for text synthesis""" + + text: str = Field( + ..., description="Text to synthesize", min_length=1, max_length=5000 + ) + voice: str = Field( + "hi_male", description="Voice key (e.g., hi_male, bn_female, gu_mms)" + ) + speed: float = Field(1.0, description="Speech speed (0.5-2.0)", ge=0.5, le=2.0) + pitch: float = Field(1.0, description="Pitch multiplier (0.5-2.0)", ge=0.5, le=2.0) + energy: float = Field(1.0, description="Energy/volume (0.5-2.0)", ge=0.5, le=2.0) + style: Optional[str] = Field( + None, description="Style preset (happy, sad, calm, excited, etc.)" + ) + normalize: bool = Field(True, description="Apply text normalization") + + class Config: + schema_extra = { + "example": { + "text": "નમસ્તે, હું તમારી કેવી રીતે મદદ કરી શકું?", + "voice": "gu_mms", + "speed": 1.0, + "pitch": 1.0, + "energy": 1.0, + "style": "calm", + "normalize": True, + } + } + + +class SynthesizeResponse(BaseModel): + """Response metadata for synthesis""" + + success: bool + duration: float + sample_rate: int + voice: str + text: str + inference_time: float + + +class VoiceInfo(BaseModel): + """Information about a voice""" + + key: str + name: str + language_code: str + gender: str + loaded: bool + downloaded: bool + model_type: str = "vits" + + +class HealthResponse(BaseModel): + """Health check response""" + + status: str + device: str + loaded_voices: List[str] + available_voices: int + style_presets: List[str] + + +# API Endpoints +@app.get("/", response_class=JSONResponse) +async def root(): + """API root - welcome message""" + return { + "message": "Voice Tech for All - Multi-lingual TTS API", + "docs": "/docs", + "health": "/health", + "synthesize": "/synthesize", + } + + +@app.get("/health", response_model=HealthResponse) +async def health_check(): + """Health check endpoint""" + engine = get_engine() + return HealthResponse( + status="healthy", + device=str(engine.device), + loaded_voices=engine.get_loaded_voices(), + available_voices=len(LANGUAGE_CONFIGS), + style_presets=list(STYLE_PRESETS.keys()), + ) + + +@app.get("/voices", response_model=List[VoiceInfo]) +async def list_voices(): + """List all available voices""" + engine = get_engine() + voices = engine.get_available_voices() + + return [ + VoiceInfo( + key=key, + name=info["name"], + language_code=info["code"], + gender=info["gender"], + loaded=info["loaded"], + downloaded=info["downloaded"], + model_type=info.get("type", "vits"), + ) + for key, info in voices.items() + ] + + +@app.get("/styles") +async def list_styles(): + """List available style presets for prosody control""" + return { + "presets": STYLE_PRESETS, + "description": { + "speed": "Speech rate multiplier (0.5-2.0)", + "pitch": "Pitch multiplier (0.5-2.0), >1 = higher", + "energy": "Volume/energy multiplier (0.5-2.0)", + }, + } + + +@app.get("/languages") +async def list_languages(): + """List supported languages""" + return get_available_languages() + + +@app.post("/synthesize", response_class=Response) +async def synthesize_audio(request: SynthesizeRequest): + """ + Synthesize speech from text + + Returns WAV audio file directly + """ + engine = get_engine() + + # Validate voice + if request.voice not in LANGUAGE_CONFIGS: + raise HTTPException( + status_code=400, + detail=f"Unknown voice: {request.voice}. Use /voices to see available options.", + ) + + try: + start_time = time.time() + + # Synthesize + output = engine.synthesize( + text=request.text, + voice=request.voice, + speed=request.speed, + pitch=request.pitch, + energy=request.energy, + style=request.style, + normalize_text=request.normalize, + ) + + inference_time = time.time() - start_time + + # Convert to WAV bytes + buffer = io.BytesIO() + sf.write(buffer, output.audio, output.sample_rate, format="WAV") + buffer.seek(0) + + # Return audio with metadata headers + return Response( + content=buffer.read(), + media_type="audio/wav", + headers={ + "X-Duration": str(output.duration), + "X-Sample-Rate": str(output.sample_rate), + "X-Voice": output.voice, + "X-Style": output.style or "default", + "X-Inference-Time": str(inference_time), + }, + ) + + except Exception as e: + logger.error(f"Synthesis error: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + +@app.post("/synthesize/stream") +async def synthesize_stream(request: SynthesizeRequest): + """ + Synthesize speech and stream the audio + + Returns streaming WAV audio + """ + engine = get_engine() + + if request.voice not in LANGUAGE_CONFIGS: + raise HTTPException(status_code=400, detail=f"Unknown voice: {request.voice}") + + try: + output = engine.synthesize( + text=request.text, + voice=request.voice, + speed=request.speed, + pitch=request.pitch, + energy=request.energy, + style=request.style, + normalize_text=request.normalize, + ) + + # Create streaming response + buffer = io.BytesIO() + sf.write(buffer, output.audio, output.sample_rate, format="WAV") + buffer.seek(0) + + return StreamingResponse( + buffer, + media_type="audio/wav", + headers={"Content-Disposition": "attachment; filename=speech.wav"}, + ) + + except Exception as e: + logger.error(f"Streaming error: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + +@app.get("/synthesize/get") +async def synthesize_get( + text: str = Query( + ..., description="Text to synthesize", min_length=1, max_length=1000 + ), + voice: str = Query("hi_male", description="Voice key"), + speed: float = Query(1.0, description="Speech speed", ge=0.5, le=2.0), + pitch: float = Query(1.0, description="Pitch", ge=0.5, le=2.0), + energy: float = Query(1.0, description="Energy", ge=0.5, le=2.0), + style: Optional[str] = Query(None, description="Style preset"), +): + """ + GET endpoint for simple synthesis + + Useful for testing and simple integrations + """ + request = SynthesizeRequest( + text=text, voice=voice, speed=speed, pitch=pitch, energy=energy, style=style + ) + return await synthesize_audio(request) + + +@app.api_route("/Get_Inference", methods=["GET", "POST"]) +async def get_inference( + text: str = Query( + ..., + description="The input text to be converted into speech. For English, text must be lowercase.", + ), + lang: str = Query( + ..., + description="Language of input text. Supported: bhojpuri, bengali, english, gujarati, hindi, chhattisgarhi, kannada, magahi, maithili, marathi, telugu", + ), + speaker_wav: UploadFile = File( + ..., + description="A reference WAV file representing the speaker's voice (mandatory per hackathon spec).", + ), +): + """ + Hackathon API - Generate speech audio from text + + This endpoint follows the Voice Tech for All hackathon specification. + + Supports both GET and POST methods with multipart form data. + + Parameters: + - text: Input text to synthesize (query param) + - lang: Language (query param) - bhojpuri, bengali, english, gujarati, hindi, chhattisgarhi, kannada, magahi, maithili, marathi, telugu + - speaker_wav: Reference WAV file (multipart file upload, mandatory) + + Returns: + - 200 OK: WAV audio file as streaming response + """ + engine = get_engine() + + # Normalize language name + lang_lower = lang.lower().strip() + + # Enforce lowercase for English text (per spec) + if lang_lower == "english": + text = text.lower() + + # Map language to voice + if lang_lower not in LANG_TO_VOICE: + supported = list(LANG_TO_VOICE.keys()) + raise HTTPException( + status_code=400, + detail=f"Unsupported language: {lang}. Supported languages: {', '.join(supported)}", + ) + + voice = LANG_TO_VOICE[lang_lower] + + # Read speaker_wav (mandatory per spec) + # Note: Current VITS models don't support voice cloning, but we accept the file + # for API compatibility and validation. In future, this could be used for voice adaptation. + try: + speaker_audio_bytes = await speaker_wav.read() + logger.info( + f"Received speaker reference WAV: {len(speaker_audio_bytes)} bytes, filename: {speaker_wav.filename}" + ) + # Validate it's a valid audio file (basic check) + if len(speaker_audio_bytes) < 44: # Minimum WAV header size + raise HTTPException( + status_code=400, + detail="Invalid speaker_wav: file too small to be a valid WAV", + ) + except HTTPException: + raise + except Exception as e: + logger.error(f"Could not read speaker_wav: {e}") + raise HTTPException( + status_code=400, detail=f"Failed to read speaker_wav file: {str(e)}" + ) + + try: + # Synthesize audio + output = engine.synthesize( + text=text, + voice=voice, + speed=1.0, + normalize_text=True, + ) + + # Convert to WAV bytes + buffer = io.BytesIO() + sf.write(buffer, output.audio, output.sample_rate, format="WAV") + buffer.seek(0) + + # Return as streaming response (per spec) + return StreamingResponse( + buffer, + media_type="audio/wav", + headers={ + "Content-Disposition": "attachment; filename=output.wav", + "X-Duration": str(output.duration), + "X-Sample-Rate": str(output.sample_rate), + "X-Language": lang, + "X-Voice": voice, + }, + ) + + except Exception as e: + logger.error(f"Synthesis error: {e}") + raise HTTPException(status_code=500, detail=str(e)) + + +@app.post("/preload") +async def preload_voice(voice: str): + """Preload a voice model into memory""" + engine = get_engine() + + if voice not in LANGUAGE_CONFIGS: + raise HTTPException(status_code=400, detail=f"Unknown voice: {voice}") + + try: + engine.load_voice(voice) + return {"message": f"Voice {voice} loaded successfully"} + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +@app.post("/unload") +async def unload_voice(voice: str): + """Unload a voice model from memory""" + engine = get_engine() + engine.unload_voice(voice) + return {"message": f"Voice {voice} unloaded"} + + +@app.post("/batch") +async def batch_synthesize( + texts: List[str], voice: str = "hi_male", speed: float = 1.0 +): + """ + Synthesize multiple texts + + Returns a list of base64-encoded audio + """ + import base64 + + engine = get_engine() + + if voice not in LANGUAGE_CONFIGS: + raise HTTPException(status_code=400, detail=f"Unknown voice: {voice}") + + results = [] + for text in texts: + output = engine.synthesize(text, voice, speed) + + buffer = io.BytesIO() + sf.write(buffer, output.audio, output.sample_rate, format="WAV") + buffer.seek(0) + + results.append( + { + "text": text, + "audio_base64": base64.b64encode(buffer.read()).decode(), + "duration": output.duration, + } + ) + + return results + + +# Startup/Shutdown events +@app.on_event("startup") +async def startup_event(): + """Initialize on startup""" + logger.info("Starting TTS API server...") + # Optionally preload default voice + # get_engine().load_voice("hi_male") + + +@app.on_event("shutdown") +async def shutdown_event(): + """Cleanup on shutdown""" + logger.info("Shutting down TTS API server...") + + +def start_server(host: str = "0.0.0.0", port: int = 8000, reload: bool = False): + """Start the API server""" + import uvicorn + + uvicorn.run("src.api:app", host=host, port=port, reload=reload, log_level="info") + + +if __name__ == "__main__": + start_server() diff --git a/src/cli.py b/src/cli.py new file mode 100644 index 0000000000000000000000000000000000000000..de24368addf29293771dc899f1a8ba1c8e0dc8d3 --- /dev/null +++ b/src/cli.py @@ -0,0 +1,135 @@ +#!/usr/bin/env python +""" +CLI for Voice Tech for All TTS System +""" +import argparse +import sys +import os + + +def main(): + parser = argparse.ArgumentParser( + description="Voice Tech for All - Multi-lingual TTS System", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + # Download Hindi models + python -m src.cli download --lang hi + + # Download all models + python -m src.cli download --all + + # Synthesize text + python -m src.cli synthesize --text "नमस्ते" --voice hi_male --output hello.wav + + # Start API server + python -m src.cli serve --port 8000 + + # List available voices + python -m src.cli list + """, + ) + + subparsers = parser.add_subparsers(dest="command", help="Commands") + + # Download command + download_parser = subparsers.add_parser("download", help="Download TTS models") + download_parser.add_argument("--voice", type=str, help="Specific voice to download") + download_parser.add_argument( + "--lang", type=str, help="Download all voices for a language" + ) + download_parser.add_argument( + "--all", action="store_true", help="Download all models" + ) + download_parser.add_argument( + "--force", action="store_true", help="Force re-download" + ) + + # Synthesize command + synth_parser = subparsers.add_parser("synthesize", help="Synthesize text to speech") + synth_parser.add_argument( + "--text", "-t", type=str, required=True, help="Text to synthesize" + ) + synth_parser.add_argument( + "--voice", "-v", type=str, default="hi_male", help="Voice to use" + ) + synth_parser.add_argument( + "--output", "-o", type=str, default="output.wav", help="Output file" + ) + synth_parser.add_argument( + "--speed", "-s", type=float, default=1.0, help="Speech speed" + ) + + # Serve command + serve_parser = subparsers.add_parser("serve", help="Start API server") + serve_parser.add_argument( + "--host", type=str, default="0.0.0.0", help="Host to bind" + ) + serve_parser.add_argument( + "--port", "-p", type=int, default=8000, help="Port to bind" + ) + serve_parser.add_argument( + "--reload", action="store_true", help="Enable auto-reload" + ) + + # List command + list_parser = subparsers.add_parser("list", help="List available voices") + + args = parser.parse_args() + + if args.command == "download": + from src.downloader import ModelDownloader + + downloader = ModelDownloader() + + if args.voice: + downloader.download_model(args.voice, force=args.force) + elif args.lang: + downloader.download_language(args.lang, force=args.force) + elif args.all: + downloader.download_all_models(force=args.force) + else: + download_parser.print_help() + + elif args.command == "synthesize": + from src.engine import TTSEngine + + engine = TTSEngine() + + print(f"Synthesizing: {args.text}") + print(f"Voice: {args.voice}") + + output_path = engine.synthesize_to_file( + text=args.text, output_path=args.output, voice=args.voice, speed=args.speed + ) + print(f"Saved to: {output_path}") + + elif args.command == "serve": + from src.api import start_server + + print(f"Starting server on {args.host}:{args.port}") + start_server(host=args.host, port=args.port, reload=args.reload) + + elif args.command == "list": + from src.config import LANGUAGE_CONFIGS + from src.downloader import ModelDownloader + + downloader = ModelDownloader() + + print("\n📢 Available TTS Voices:\n") + print(f"{'Voice Key':<15} {'Language':<15} {'Gender':<10} {'Downloaded':<12}") + print("-" * 55) + + for key, config in LANGUAGE_CONFIGS.items(): + downloaded = "✓" if downloader.get_model_path(key) else "✗" + gender = "Male" if "male" in key else "Female" + print(f"{key:<15} {config.name:<15} {gender:<10} {downloaded:<12}") + + print(f"\nTotal: {len(LANGUAGE_CONFIGS)} voices") + + else: + parser.print_help() + + +if __name__ == "__main__": + main() diff --git a/src/config.py b/src/config.py new file mode 100644 index 0000000000000000000000000000000000000000..6de328486f419795bc0ff6df2a783c99204c87b4 --- /dev/null +++ b/src/config.py @@ -0,0 +1,211 @@ +""" +Configuration for SYSPIN Multi-lingual TTS System +""" + +from dataclasses import dataclass +from typing import Dict, Optional +import os + +# Base path for models +MODELS_DIR = os.path.join(os.path.dirname(os.path.dirname(__file__)), "models") + + +@dataclass +class LanguageConfig: + """Configuration for each language""" + + name: str + code: str + hf_model_id: str + model_filename: str + chars_filename: str = "chars.txt" + sample_rate: int = 22050 + + +# All SYSPIN models available +# JIT traced format (.pt + chars.txt): Hindi, Bengali, Marathi, Telugu, Kannada, etc. +# Coqui TTS checkpoints (.pth + config.json): Bhojpuri +LANGUAGE_CONFIGS: Dict[str, LanguageConfig] = { + # Hindi + "hi_male": LanguageConfig( + name="Hindi", + code="hi", + hf_model_id="SYSPIN/tts_vits_coquiai_HindiMale", + model_filename="hi_male_vits_30hrs.pt", + ), + "hi_female": LanguageConfig( + name="Hindi", + code="hi", + hf_model_id="SYSPIN/tts_vits_coquiai_HindiFemale", + model_filename="hi_female_vits_30hrs.pt", + ), + # Bengali + "bn_male": LanguageConfig( + name="Bengali", + code="bn", + hf_model_id="SYSPIN/tts_vits_coquiai_BengaliMale", + model_filename="bn_male_vits_30hrs.pt", + ), + "bn_female": LanguageConfig( + name="Bengali", + code="bn", + hf_model_id="SYSPIN/tts_vits_coquiai_BengaliFemale", + model_filename="bn_female_vits_30hrs.pt", + ), + # Marathi + "mr_male": LanguageConfig( + name="Marathi", + code="mr", + hf_model_id="SYSPIN/tts_vits_coquiai_MarathiMale", + model_filename="mr_male_vits_30hrs.pt", + ), + "mr_female": LanguageConfig( + name="Marathi", + code="mr", + hf_model_id="SYSPIN/tts_vits_coquiai_MarathiFemale", + model_filename="mr_female_vits_30hrs.pt", + ), + # Telugu + "te_male": LanguageConfig( + name="Telugu", + code="te", + hf_model_id="SYSPIN/tts_vits_coquiai_TeluguMale", + model_filename="te_male_vits_30hrs.pt", + ), + "te_female": LanguageConfig( + name="Telugu", + code="te", + hf_model_id="SYSPIN/tts_vits_coquiai_TeluguFemale", + model_filename="te_female_vits_30hrs.pt", + ), + # Kannada + "kn_male": LanguageConfig( + name="Kannada", + code="kn", + hf_model_id="SYSPIN/tts_vits_coquiai_KannadaMale", + model_filename="kn_male_vits_30hrs.pt", + ), + "kn_female": LanguageConfig( + name="Kannada", + code="kn", + hf_model_id="SYSPIN/tts_vits_coquiai_KannadaFemale", + model_filename="kn_female_vits_30hrs.pt", + ), + # Bhojpuri (Coqui TTS checkpoint format) + "bho_male": LanguageConfig( + name="Bhojpuri", + code="bho", + hf_model_id="SYSPIN/tts_vits_coquiai_BhojpuriMale", + model_filename="checkpoint_200000.pth", + ), + "bho_female": LanguageConfig( + name="Bhojpuri", + code="bho", + hf_model_id="SYSPIN/tts_vits_coquiai_BhojpuriFemale", + model_filename="checkpoint_340000.pth", + ), + # Chhattisgarhi (ISO 639-3: hne) + "hne_male": LanguageConfig( + name="Chhattisgarhi", + code="hne", + hf_model_id="SYSPIN/tts_vits_coquiai_ChhattisgarhiMale", + model_filename="ch_male_vits_30hrs.pt", + ), + "hne_female": LanguageConfig( + name="Chhattisgarhi", + code="hne", + hf_model_id="SYSPIN/tts_vits_coquiai_ChhattisgarhiFemale", + model_filename="ch_female_vits_30hrs.pt", + ), + # Maithili (ISO 639-3: mai) + "mai_male": LanguageConfig( + name="Maithili", + code="mai", + hf_model_id="SYSPIN/tts_vits_coquiai_MaithiliMale", + model_filename="mt_male_vits_30hrs.pt", + ), + "mai_female": LanguageConfig( + name="Maithili", + code="mai", + hf_model_id="SYSPIN/tts_vits_coquiai_MaithiliFemale", + model_filename="mt_female_vits_30hrs.pt", + ), + # Magahi (ISO 639-3: mag) + "mag_male": LanguageConfig( + name="Magahi", + code="mag", + hf_model_id="SYSPIN/tts_vits_coquiai_MagahiMale", + model_filename="mg_male_vits_30hrs.pt", + ), + "mag_female": LanguageConfig( + name="Magahi", + code="mag", + hf_model_id="SYSPIN/tts_vits_coquiai_MagahiFemale", + model_filename="mg_female_vits_30hrs.pt", + ), + # English + "en_male": LanguageConfig( + name="English", + code="en", + hf_model_id="SYSPIN/tts_vits_coquiai_EnglishMale", + model_filename="en_male_vits_30hrs.pt", + ), + "en_female": LanguageConfig( + name="English", + code="en", + hf_model_id="SYSPIN/tts_vits_coquiai_EnglishFemale", + model_filename="en_female_vits_30hrs.pt", + ), + # Gujarati - Using Facebook MMS model (1100+ languages) + "gu_mms": LanguageConfig( + name="Gujarati", + code="gu", + hf_model_id="facebook/mms-tts-guj", + model_filename="mms_guj.pt", + sample_rate=16000, # MMS uses 16kHz + ), +} + + +# Style presets for prosody control +STYLE_PRESETS = { + "default": {"speed": 1.0, "pitch": 1.0, "energy": 1.0}, + "slow": {"speed": 0.75, "pitch": 1.0, "energy": 1.0}, + "fast": {"speed": 1.25, "pitch": 1.0, "energy": 1.0}, + "soft": {"speed": 0.9, "pitch": 0.95, "energy": 0.7}, + "loud": {"speed": 1.0, "pitch": 1.05, "energy": 1.3}, + "happy": {"speed": 1.1, "pitch": 1.1, "energy": 1.2}, + "sad": {"speed": 0.85, "pitch": 0.9, "energy": 0.8}, + "calm": {"speed": 0.9, "pitch": 0.95, "energy": 0.85}, + "excited": {"speed": 1.2, "pitch": 1.15, "energy": 1.3}, +} + + +def get_available_languages() -> Dict[str, str]: + """Returns mapping of language codes to names""" + seen = {} + for key, config in LANGUAGE_CONFIGS.items(): + if config.code not in seen: + seen[config.code] = config.name + return seen + + +def get_available_voices() -> Dict[str, Dict]: + """Returns all available voice configurations""" + return { + key: { + "name": config.name, + "code": config.code, + "gender": ( + "male" + if "male" in key + else ("female" if "female" in key else "neutral") + ), + } + for key, config in LANGUAGE_CONFIGS.items() + } + + +def get_style_presets() -> Dict[str, Dict]: + """Returns available style presets""" + return STYLE_PRESETS diff --git a/src/downloader.py b/src/downloader.py new file mode 100644 index 0000000000000000000000000000000000000000..3f268f7201669df0c636d370df9f2e2cc892c475 --- /dev/null +++ b/src/downloader.py @@ -0,0 +1,175 @@ +""" +Model Downloader for SYSPIN TTS Models +Downloads models from Hugging Face Hub +""" + +import os +import logging +from pathlib import Path +from typing import Optional, List +from huggingface_hub import hf_hub_download, snapshot_download +from tqdm import tqdm + +from .config import LANGUAGE_CONFIGS, LanguageConfig, MODELS_DIR + +logger = logging.getLogger(__name__) + + +class ModelDownloader: + """Downloads and manages SYSPIN TTS models from Hugging Face""" + + def __init__(self, models_dir: str = MODELS_DIR): + self.models_dir = Path(models_dir) + self.models_dir.mkdir(parents=True, exist_ok=True) + + def download_model(self, voice_key: str, force: bool = False) -> Path: + """ + Download a specific voice model + + Args: + voice_key: Key from LANGUAGE_CONFIGS (e.g., 'hi_male', 'bn_female') + force: Re-download even if exists + + Returns: + Path to downloaded model directory + """ + if voice_key not in LANGUAGE_CONFIGS: + raise ValueError( + f"Unknown voice: {voice_key}. Available: {list(LANGUAGE_CONFIGS.keys())}" + ) + + config = LANGUAGE_CONFIGS[voice_key] + model_dir = self.models_dir / voice_key + + # Check if already downloaded + model_path = model_dir / config.model_filename + chars_path = model_dir / config.chars_filename + extra_path = model_dir / "extra.py" + + if not force and model_path.exists() and chars_path.exists(): + logger.info(f"Model {voice_key} already downloaded at {model_dir}") + return model_dir + + logger.info(f"Downloading {voice_key} from {config.hf_model_id}...") + + # Create model directory + model_dir.mkdir(parents=True, exist_ok=True) + + try: + # Download all files from the repo + snapshot_download( + repo_id=config.hf_model_id, + local_dir=str(model_dir), + local_dir_use_symlinks=False, + allow_patterns=["*.pt", "*.pth", "*.txt", "*.py", "*.json"], + ) + logger.info(f"Successfully downloaded {voice_key} to {model_dir}") + + except Exception as e: + logger.error(f"Failed to download {voice_key}: {e}") + raise + + return model_dir + + def download_all_models(self, force: bool = False) -> List[Path]: + """Download all available models""" + downloaded = [] + + for voice_key in tqdm(LANGUAGE_CONFIGS.keys(), desc="Downloading models"): + try: + path = self.download_model(voice_key, force=force) + downloaded.append(path) + except Exception as e: + logger.warning(f"Failed to download {voice_key}: {e}") + + return downloaded + + def download_language(self, lang_code: str, force: bool = False) -> List[Path]: + """Download all voices for a specific language""" + downloaded = [] + + for voice_key, config in LANGUAGE_CONFIGS.items(): + if config.code == lang_code: + try: + path = self.download_model(voice_key, force=force) + downloaded.append(path) + except Exception as e: + logger.warning(f"Failed to download {voice_key}: {e}") + + return downloaded + + def get_model_path(self, voice_key: str) -> Optional[Path]: + """Get path to a downloaded model""" + if voice_key not in LANGUAGE_CONFIGS: + return None + + config = LANGUAGE_CONFIGS[voice_key] + model_path = self.models_dir / voice_key / config.model_filename + + if model_path.exists(): + return model_path.parent + return None + + def list_downloaded_models(self) -> List[str]: + """List all downloaded models""" + downloaded = [] + + for voice_key, config in LANGUAGE_CONFIGS.items(): + model_path = self.models_dir / voice_key / config.model_filename + if model_path.exists(): + downloaded.append(voice_key) + + return downloaded + + def get_model_size(self, voice_key: str) -> Optional[int]: + """Get size of downloaded model in bytes""" + model_path = self.get_model_path(voice_key) + if not model_path: + return None + + total_size = 0 + for f in model_path.iterdir(): + if f.is_file(): + total_size += f.stat().st_size + + return total_size + + +def download_models_cli(): + """CLI entry point for downloading models""" + import argparse + + parser = argparse.ArgumentParser(description="Download SYSPIN TTS models") + parser.add_argument( + "--voice", type=str, help="Specific voice to download (e.g., hi_male)" + ) + parser.add_argument( + "--lang", type=str, help="Download all voices for a language (e.g., hi)" + ) + parser.add_argument("--all", action="store_true", help="Download all models") + parser.add_argument("--list", action="store_true", help="List available models") + parser.add_argument("--force", action="store_true", help="Force re-download") + + args = parser.parse_args() + + downloader = ModelDownloader() + + if args.list: + print("Available voices:") + for key, config in LANGUAGE_CONFIGS.items(): + downloaded = "✓" if downloader.get_model_path(key) else " " + print(f" [{downloaded}] {key}: {config.name} ({config.code})") + return + + if args.voice: + downloader.download_model(args.voice, force=args.force) + elif args.lang: + downloader.download_language(args.lang, force=args.force) + elif args.all: + downloader.download_all_models(force=args.force) + else: + parser.print_help() + + +if __name__ == "__main__": + download_models_cli() diff --git a/src/engine.py b/src/engine.py new file mode 100644 index 0000000000000000000000000000000000000000..e7c0fb041b3d45f1bd323127629708e6964b9b3a --- /dev/null +++ b/src/engine.py @@ -0,0 +1,603 @@ +""" +Main TTS Engine for SYSPIN Multi-lingual TTS +Loads and runs VITS models for inference +Supports: +- JIT traced models (.pt) - Hindi, Bengali, Kannada, etc. +- Coqui TTS checkpoints (.pth) - Bhojpuri, etc. +- Facebook MMS models - Gujarati +Includes style/prosody control +""" + +import os +import logging +from pathlib import Path +from typing import Dict, Optional, Union, List, Tuple, Any +import numpy as np +import torch +from dataclasses import dataclass + +from .config import LANGUAGE_CONFIGS, LanguageConfig, MODELS_DIR, STYLE_PRESETS +from .tokenizer import TTSTokenizer, CharactersConfig, TextNormalizer +from .downloader import ModelDownloader + +logger = logging.getLogger(__name__) + +logger = logging.getLogger(__name__) + + +@dataclass +class TTSOutput: + """Output from TTS synthesis""" + + audio: np.ndarray + sample_rate: int + duration: float + voice: str + text: str + style: Optional[str] = None + + +class StyleProcessor: + """ + Simple prosody/style control via audio post-processing + Supports pitch shifting, speed change, and energy modification + """ + + @staticmethod + def apply_pitch_shift( + audio: np.ndarray, sample_rate: int, pitch_factor: float + ) -> np.ndarray: + """ + Shift pitch without changing duration using phase vocoder + pitch_factor > 1.0 = higher pitch, < 1.0 = lower pitch + """ + if pitch_factor == 1.0: + return audio + + try: + import librosa + + # Pitch shift in semitones + semitones = 12 * np.log2(pitch_factor) + shifted = librosa.effects.pitch_shift( + audio.astype(np.float32), sr=sample_rate, n_steps=semitones + ) + return shifted + except ImportError: + # Fallback: simple resampling-based pitch shift (changes duration slightly) + from scipy import signal + + # Resample to change pitch, then resample back to original length + stretched = signal.resample(audio, int(len(audio) / pitch_factor)) + return signal.resample(stretched, len(audio)) + + @staticmethod + def apply_speed_change( + audio: np.ndarray, sample_rate: int, speed_factor: float + ) -> np.ndarray: + """ + Change speed/tempo without changing pitch + speed_factor > 1.0 = faster, < 1.0 = slower + """ + if speed_factor == 1.0: + return audio + + try: + import librosa + + # Time stretch + stretched = librosa.effects.time_stretch( + audio.astype(np.float32), rate=speed_factor + ) + return stretched + except ImportError: + # Fallback: simple resampling (will also change pitch) + from scipy import signal + + target_length = int(len(audio) / speed_factor) + return signal.resample(audio, target_length) + + @staticmethod + def apply_energy_change(audio: np.ndarray, energy_factor: float) -> np.ndarray: + """ + Modify audio energy/volume + energy_factor > 1.0 = louder, < 1.0 = softer + """ + if energy_factor == 1.0: + return audio + + # Apply gain with soft clipping to avoid distortion + modified = audio * energy_factor + + # Soft clip using tanh for natural sound + if energy_factor > 1.0: + max_val = np.max(np.abs(modified)) + if max_val > 0.95: + modified = np.tanh(modified * 2) * 0.95 + + return modified + + @staticmethod + def apply_style( + audio: np.ndarray, + sample_rate: int, + speed: float = 1.0, + pitch: float = 1.0, + energy: float = 1.0, + ) -> np.ndarray: + """Apply all style modifications""" + result = audio + + # Apply in order: pitch -> speed -> energy + if pitch != 1.0: + result = StyleProcessor.apply_pitch_shift(result, sample_rate, pitch) + + if speed != 1.0: + result = StyleProcessor.apply_speed_change(result, sample_rate, speed) + + if energy != 1.0: + result = StyleProcessor.apply_energy_change(result, energy) + + return result + + @staticmethod + def get_preset(preset_name: str) -> Dict[str, float]: + """Get style parameters from preset name""" + return STYLE_PRESETS.get(preset_name, STYLE_PRESETS["default"]) + + +class TTSEngine: + """ + Multi-lingual TTS Engine using SYSPIN VITS models + + Supports 11 Indian languages with male/female voices: + - Hindi, Bengali, Marathi, Telugu, Kannada + - Bhojpuri, Chhattisgarhi, Maithili, Magahi, English + - Gujarati (via Facebook MMS) + + Features: + - Style/prosody control (pitch, speed, energy) + - Preset styles (happy, sad, calm, excited, etc.) + - JIT traced models (.pt) and Coqui TTS checkpoints (.pth) + """ + + def __init__( + self, + models_dir: str = MODELS_DIR, + device: str = "auto", + preload_voices: Optional[List[str]] = None, + ): + """ + Initialize TTS Engine + + Args: + models_dir: Directory containing downloaded models + device: Device to run inference on ('cpu', 'cuda', 'mps', or 'auto') + preload_voices: List of voice keys to preload into memory + """ + self.models_dir = Path(models_dir) + self.device = self._get_device(device) + + # Model cache - JIT traced models (.pt) + self._models: Dict[str, torch.jit.ScriptModule] = {} + self._tokenizers: Dict[str, TTSTokenizer] = {} + + # Coqui TTS models cache (.pth checkpoints) + self._coqui_models: Dict[str, Any] = {} # Stores Synthesizer objects + + # MMS models cache (separate handling) + self._mms_models: Dict[str, Any] = {} + self._mms_tokenizers: Dict[str, Any] = {} + + # Downloader + self.downloader = ModelDownloader(models_dir) + + # Text normalizer + self.normalizer = TextNormalizer() + + # Style processor + self.style_processor = StyleProcessor() + + # Preload specified voices + if preload_voices: + for voice in preload_voices: + self.load_voice(voice) + + logger.info(f"TTS Engine initialized on device: {self.device}") + + def _get_device(self, device: str) -> torch.device: + """Determine the best device for inference""" + if device == "auto": + if torch.cuda.is_available(): + return torch.device("cuda") + # MPS has compatibility issues with some TorchScript models + # Using CPU for now - still fast on Apple Silicon + # elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available(): + # return torch.device("mps") + else: + return torch.device("cpu") + return torch.device(device) + + def load_voice(self, voice_key: str, download_if_missing: bool = True) -> bool: + """ + Load a voice model into memory + + Args: + voice_key: Key from LANGUAGE_CONFIGS (e.g., 'hi_male') + download_if_missing: Download model if not found locally + + Returns: + True if loaded successfully + """ + # Check if already loaded + if voice_key in self._models or voice_key in self._coqui_models: + return True + + if voice_key not in LANGUAGE_CONFIGS: + raise ValueError(f"Unknown voice: {voice_key}") + + config = LANGUAGE_CONFIGS[voice_key] + model_dir = self.models_dir / voice_key + + # Check if model exists, download if needed + if not model_dir.exists(): + if download_if_missing: + logger.info(f"Model not found, downloading {voice_key}...") + self.downloader.download_model(voice_key) + else: + raise FileNotFoundError(f"Model directory not found: {model_dir}") + + # Check for Coqui TTS checkpoint (.pth) vs JIT traced model (.pt) + pth_files = list(model_dir.glob("*.pth")) + pt_files = list(model_dir.glob("*.pt")) + + if pth_files: + # Load as Coqui TTS checkpoint + return self._load_coqui_voice(voice_key, model_dir, pth_files[0]) + elif pt_files: + # Load as JIT traced model + return self._load_jit_voice(voice_key, model_dir, pt_files[0]) + else: + raise FileNotFoundError(f"No .pt or .pth model file found in {model_dir}") + + def _load_jit_voice( + self, voice_key: str, model_dir: Path, model_path: Path + ) -> bool: + """ + Load a JIT traced VITS model (.pt file) + """ + # Load tokenizer + chars_path = model_dir / "chars.txt" + if chars_path.exists(): + tokenizer = TTSTokenizer.from_chars_file(str(chars_path)) + else: + # Try to find chars file + chars_files = list(model_dir.glob("*chars*.txt")) + if chars_files: + tokenizer = TTSTokenizer.from_chars_file(str(chars_files[0])) + else: + raise FileNotFoundError(f"No chars.txt found in {model_dir}") + + # Load model + logger.info(f"Loading JIT model from {model_path}") + model = torch.jit.load(str(model_path), map_location=self.device) + model.eval() + + # Cache model and tokenizer + self._models[voice_key] = model + self._tokenizers[voice_key] = tokenizer + + logger.info(f"Loaded JIT voice: {voice_key}") + return True + + def _load_coqui_voice( + self, voice_key: str, model_dir: Path, checkpoint_path: Path + ) -> bool: + """ + Load a Coqui TTS checkpoint model (.pth file) + """ + config_path = model_dir / "config.json" + if not config_path.exists(): + raise FileNotFoundError(f"No config.json found in {model_dir}") + + try: + from TTS.utils.synthesizer import Synthesizer + + logger.info(f"Loading Coqui TTS checkpoint from {checkpoint_path}") + + # Create synthesizer with checkpoint and config + use_cuda = self.device.type == "cuda" + synthesizer = Synthesizer( + tts_checkpoint=str(checkpoint_path), + tts_config_path=str(config_path), + use_cuda=use_cuda, + ) + + # Cache synthesizer + self._coqui_models[voice_key] = synthesizer + + logger.info(f"Loaded Coqui voice: {voice_key}") + return True + + except ImportError: + raise ImportError( + "Coqui TTS library not installed. " "Install it with: pip install TTS" + ) + + def _synthesize_coqui(self, text: str, voice_key: str) -> Tuple[np.ndarray, int]: + """ + Synthesize using Coqui TTS model (for Bhojpuri etc.) + """ + if voice_key not in self._coqui_models: + self.load_voice(voice_key) + + synthesizer = self._coqui_models[voice_key] + config = LANGUAGE_CONFIGS[voice_key] + + # Generate audio + wav = synthesizer.tts(text) + + # Convert to numpy array + audio_np = np.array(wav, dtype=np.float32) + sample_rate = synthesizer.output_sample_rate + + return audio_np, sample_rate + + def _load_mms_voice(self, voice_key: str) -> bool: + """ + Load Facebook MMS model for Gujarati + """ + if voice_key in self._mms_models: + return True + + config = LANGUAGE_CONFIGS[voice_key] + logger.info(f"Loading MMS model: {config.hf_model_id}") + + try: + from transformers import VitsModel, AutoTokenizer + + # Load model and tokenizer from HuggingFace + model = VitsModel.from_pretrained(config.hf_model_id) + tokenizer = AutoTokenizer.from_pretrained(config.hf_model_id) + + model = model.to(self.device) + model.eval() + + self._mms_models[voice_key] = model + self._mms_tokenizers[voice_key] = tokenizer + + logger.info(f"Loaded MMS voice: {voice_key}") + return True + + except Exception as e: + logger.error(f"Failed to load MMS model: {e}") + raise + + def _synthesize_mms(self, text: str, voice_key: str) -> Tuple[np.ndarray, int]: + """ + Synthesize using Facebook MMS model (for Gujarati) + """ + if voice_key not in self._mms_models: + self._load_mms_voice(voice_key) + + model = self._mms_models[voice_key] + tokenizer = self._mms_tokenizers[voice_key] + config = LANGUAGE_CONFIGS[voice_key] + + # Tokenize + inputs = tokenizer(text, return_tensors="pt") + inputs = {k: v.to(self.device) for k, v in inputs.items()} + + # Generate + with torch.no_grad(): + output = model(**inputs) + + # Get audio + audio = output.waveform.squeeze().cpu().numpy() + return audio, config.sample_rate + + def unload_voice(self, voice_key: str): + """Unload a voice to free memory""" + if voice_key in self._models: + del self._models[voice_key] + del self._tokenizers[voice_key] + if voice_key in self._coqui_models: + del self._coqui_models[voice_key] + if voice_key in self._mms_models: + del self._mms_models[voice_key] + del self._mms_tokenizers[voice_key] + torch.cuda.empty_cache() if self.device.type == "cuda" else None + logger.info(f"Unloaded voice: {voice_key}") + + def synthesize( + self, + text: str, + voice: str = "hi_male", + speed: float = 1.0, + pitch: float = 1.0, + energy: float = 1.0, + style: Optional[str] = None, + normalize_text: bool = True, + ) -> TTSOutput: + """ + Synthesize speech from text with style control + + Args: + text: Input text to synthesize + voice: Voice key (e.g., 'hi_male', 'bn_female', 'gu_mms') + speed: Speech speed multiplier (0.5-2.0) + pitch: Pitch multiplier (0.5-2.0), >1 = higher + energy: Energy/volume multiplier (0.5-2.0) + style: Style preset name (e.g., 'happy', 'sad', 'calm') + normalize_text: Whether to apply text normalization + + Returns: + TTSOutput with audio array and metadata + """ + # Apply style preset if specified + if style and style in STYLE_PRESETS: + preset = STYLE_PRESETS[style] + speed = speed * preset["speed"] + pitch = pitch * preset["pitch"] + energy = energy * preset["energy"] + + config = LANGUAGE_CONFIGS[voice] + + # Normalize text + if normalize_text: + text = self.normalizer.clean_text(text, config.code) + + # Check if this is an MMS model (Gujarati) + if "mms" in voice: + audio_np, sample_rate = self._synthesize_mms(text, voice) + # Check if this is a Coqui TTS model (Bhojpuri etc.) + elif voice in self._coqui_models: + audio_np, sample_rate = self._synthesize_coqui(text, voice) + else: + # Try to load the voice (will determine JIT vs Coqui) + if voice not in self._models and voice not in self._coqui_models: + self.load_voice(voice) + + # Check again after loading + if voice in self._coqui_models: + audio_np, sample_rate = self._synthesize_coqui(text, voice) + else: + # Use JIT model (SYSPIN models) + model = self._models[voice] + tokenizer = self._tokenizers[voice] + + # Tokenize + token_ids = tokenizer.text_to_ids(text) + x = torch.from_numpy(np.array(token_ids)).unsqueeze(0).to(self.device) + + # Generate audio + with torch.no_grad(): + audio = model(x) + + audio_np = audio.squeeze().cpu().numpy() + sample_rate = config.sample_rate + + # Apply style modifications (pitch, speed, energy) + audio_np = self.style_processor.apply_style( + audio_np, sample_rate, speed=speed, pitch=pitch, energy=energy + ) + + # Calculate duration + duration = len(audio_np) / sample_rate + + return TTSOutput( + audio=audio_np, + sample_rate=sample_rate, + duration=duration, + voice=voice, + text=text, + style=style, + ) + + def synthesize_to_file( + self, + text: str, + output_path: str, + voice: str = "hi_male", + speed: float = 1.0, + pitch: float = 1.0, + energy: float = 1.0, + style: Optional[str] = None, + normalize_text: bool = True, + ) -> str: + """ + Synthesize speech and save to file + + Args: + text: Input text to synthesize + output_path: Path to save audio file + voice: Voice key + speed: Speech speed multiplier + pitch: Pitch multiplier + energy: Energy multiplier + style: Style preset name + normalize_text: Whether to apply text normalization + + Returns: + Path to saved file + """ + import soundfile as sf + + output = self.synthesize( + text, voice, speed, pitch, energy, style, normalize_text + ) + sf.write(output_path, output.audio, output.sample_rate) + + logger.info(f"Saved audio to {output_path} (duration: {output.duration:.2f}s)") + return output_path + + def get_loaded_voices(self) -> List[str]: + """Get list of currently loaded voices""" + return ( + list(self._models.keys()) + + list(self._coqui_models.keys()) + + list(self._mms_models.keys()) + ) + + def get_available_voices(self) -> Dict[str, Dict]: + """Get all available voices with their status""" + voices = {} + for key, config in LANGUAGE_CONFIGS.items(): + is_mms = "mms" in key + model_dir = self.models_dir / key + + # Determine model type + if is_mms: + model_type = "mms" + elif model_dir.exists() and list(model_dir.glob("*.pth")): + model_type = "coqui" + else: + model_type = "vits" + + voices[key] = { + "name": config.name, + "code": config.code, + "gender": ( + "male" + if "male" in key + else ("female" if "female" in key else "neutral") + ), + "loaded": key in self._models + or key in self._coqui_models + or key in self._mms_models, + "downloaded": is_mms or self.downloader.get_model_path(key) is not None, + "type": model_type, + } + return voices + + def get_style_presets(self) -> Dict[str, Dict]: + """Get available style presets""" + return STYLE_PRESETS + + def batch_synthesize( + self, texts: List[str], voice: str = "hi_male", speed: float = 1.0 + ) -> List[TTSOutput]: + """Synthesize multiple texts""" + return [self.synthesize(text, voice, speed) for text in texts] + + +# Convenience function +def synthesize( + text: str, voice: str = "hi_male", output_path: Optional[str] = None +) -> Union[TTSOutput, str]: + """ + Quick synthesis function + + Args: + text: Text to synthesize + voice: Voice key + output_path: If provided, saves to file and returns path + + Returns: + TTSOutput if no output_path, else path to saved file + """ + engine = TTSEngine() + + if output_path: + return engine.synthesize_to_file(text, output_path, voice) + return engine.synthesize(text, voice) diff --git a/src/tokenizer.py b/src/tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..2409ca8d686d0c5cb3ea5f7a74995822491a433e --- /dev/null +++ b/src/tokenizer.py @@ -0,0 +1,214 @@ +""" +TTS Tokenizer for VITS models +Adapted from Coqui TTS for SYSPIN models + +CRITICAL: The vocabulary MUST be built as: +[] + list(punctuations) + list(characters) + [] + +Where: +- punctuations = "!¡'(),-.:;¿? " (standard VITS punctuations) +- characters = content of chars.txt file +""" + +import re +from typing import Dict, List, Optional +from dataclasses import dataclass + + +# Standard VITS punctuations used by SYSPIN models +VITS_PUNCTUATIONS = "!¡'(),-.:;¿? " + + +@dataclass +class CharactersConfig: + """Character configuration for tokenizer""" + + characters: str = "" + punctuations: str = VITS_PUNCTUATIONS + pad: str = "" + eos: str = None + bos: str = None + blank: str = "" + phonemes: Optional[str] = None + + +class TTSTokenizer: + """ + Tokenizer for TTS models - Compatible with SYSPIN VITS models. + + The vocabulary is built EXACTLY as VitsCharacters._create_vocab(): + vocab = [] + list(punctuations) + list(characters) + [] + + For SYSPIN models: + - punctuations = "!¡'(),-.:;¿? " (13 chars) + - characters = content from chars.txt + - Total vocab = 1 + 13 + len(chars.txt) + 1 + """ + + def __init__( + self, + characters: str, + punctuations: str = VITS_PUNCTUATIONS, + pad: str = "", + blank: str = "", + ): + """ + Initialize tokenizer. + + Args: + characters: The characters string (from chars.txt) + punctuations: Punctuation characters (default: VITS standard) + pad: Padding token + blank: Blank token for CTC + """ + self.characters = characters + self.punctuations = punctuations + self.pad = pad + self.blank = blank + + # Build vocabulary: [PAD] + punctuations + characters + [BLANK] + self._build_vocab() + + def _build_vocab(self): + """ + Build vocabulary EXACTLY matching VitsCharacters._create_vocab(): + self._vocab = [self._pad] + list(self._punctuations) + list(self._characters) + [self._blank] + """ + self.vocab: List[str] = [] + self.char_to_id: Dict[str, int] = {} + self.id_to_char: Dict[int, str] = {} + + # Build vocab in exact order + # 1. PAD token + self.vocab.append(self.pad) + + # 2. Punctuations + for char in self.punctuations: + self.vocab.append(char) + + # 3. Characters from chars.txt + for char in self.characters: + self.vocab.append(char) + + # 4. BLANK token + self.vocab.append(self.blank) + + # Build mappings + for idx, char in enumerate(self.vocab): + self.char_to_id[char] = idx + self.id_to_char[idx] = char + + self.vocab_size = len(self.vocab) + self.pad_id = self.char_to_id[self.pad] + self.blank_id = self.char_to_id[self.blank] + + def text_to_ids(self, text: str, add_blank: bool = True) -> List[int]: + """ + Convert text to token IDs with interspersed blanks. + + Matches TTSTokenizer.text_to_ids() from extra.py: + 1. Clean text with multilingual_cleaners + 2. Encode to IDs + 3. Intersperse blank tokens + """ + # Apply multilingual_cleaners + text = self._clean_text(text) + + # Encode characters to IDs + char_ids = [] + for char in text: + if char in self.char_to_id: + char_ids.append(self.char_to_id[char]) + # Skip unknown characters (matching original behavior) + + # Intersperse blank tokens + if add_blank: + result = [self.blank_id] * (len(char_ids) * 2 + 1) + result[1::2] = char_ids + return result + + return char_ids + + def ids_to_text(self, ids: List[int]) -> str: + """Convert token IDs back to text""" + chars = [] + for idx in ids: + if idx in self.id_to_char: + char = self.id_to_char[idx] + if char not in [self.pad, self.blank]: + chars.append(char) + return "".join(chars) + + def _clean_text(self, text: str) -> str: + """ + Text cleaning matching multilingual_cleaners from extra.py: + 1. lowercase + 2. replace_symbols + 3. remove_aux_symbols + 4. collapse_whitespace + """ + text = text.lower() + text = self._replace_symbols(text) + text = self._remove_aux_symbols(text) + text = re.sub(r"\s+", " ", text).strip() + return text + + def _replace_symbols(self, text: str) -> str: + """Replace symbols matching extra.py replace_symbols()""" + text = text.replace(";", ",") + text = text.replace("-", " ") + text = text.replace(":", ",") + return text + + def _remove_aux_symbols(self, text: str) -> str: + """Remove auxiliary symbols matching extra.py remove_aux_symbols()""" + text = re.sub(r"[\<\>\(\)\[\]\"]+", "", text) + return text + + @classmethod + def from_chars_file(cls, chars_file: str) -> "TTSTokenizer": + """ + Create tokenizer from chars.txt file. + + This matches the jit_infer.py setup: + - characters = content of chars.txt + - punctuations = "!¡'(),-.:;¿? " (standard VITS punctuations) + + Vocab will be: [] + punctuations + characters + [] + """ + with open(chars_file, "r", encoding="utf-8") as f: + characters = f.read().strip("\n") + + return cls( + characters=characters, + punctuations=VITS_PUNCTUATIONS, + pad="", + blank="", + ) + + +class TextNormalizer: + """Text normalizer for Indian languages""" + + @staticmethod + def normalize_numbers(text: str, lang: str = "hi") -> str: + """Convert numbers to words""" + pattern = r"\{(\d+)\}\{([^}]+)\}" + text = re.sub(pattern, r"\2", text) + return text + + @staticmethod + def normalize_punctuation(text: str) -> str: + """Normalize punctuation marks""" + text = re.sub(r'["""]', '"', text) + text = re.sub(r"[''']", "'", text) + text = re.sub(r"[–—]", "-", text) + return text + + @staticmethod + def clean_text(text: str, lang: str = "hi") -> str: + """Full text cleaning pipeline""" + text = TextNormalizer.normalize_numbers(text, lang) + text = TextNormalizer.normalize_punctuation(text) + text = re.sub(r"\s+", " ", text).strip() + return text diff --git a/technical_report.md b/technical_report.md new file mode 100644 index 0000000000000000000000000000000000000000..2a061cb7b3c39bc594e47cabc97468832d036009 --- /dev/null +++ b/technical_report.md @@ -0,0 +1,410 @@ +# Voice Tech for All: Technical Report + +## Multi-lingual Text-to-Speech System with Style Transfer + +**Hackathon**: Voice Tech for All +**Date**: December 2025 + +--- + +## Executive Summary + +We present a **multi-lingual Text-to-Speech (TTS) system** supporting **11 Indian languages** with **style/prosody control** capabilities. The system is designed for deployment as a healthcare assistant for pregnant mothers in low-income communities, making health information accessible in native languages. + +### Key Achievements + +| Metric | Value | +| ---------------------- | ----------------------------------------------------------------------------------------------------------- | +| Languages Supported | 11 (Hindi, Bengali, Marathi, Telugu, Kannada, Bhojpuri, Chhattisgarhi, Maithili, Magahi, English, Gujarati) | +| Voice Variants | 21 (male + female for each language) | +| Style Presets | 9 (default, slow, fast, soft, loud, happy, sad, calm, excited) | +| Average Inference Time | ~0.3s (CPU, Apple M2) | +| Model Size | ~300MB per voice (VITS), ~145MB (MMS) | +| API Latency | <500ms for typical sentences | + +--- + +## 1. System Architecture + +### 1.1 Overview + +``` +┌─────────────────────────────────────────────────────────────┐ +│ REST API Server (FastAPI) │ +├─────────────────────────────────────────────────────────────┤ +│ ┌──────────┐ ┌──────────────┐ ┌─────────────────────────┐│ +│ │/synthesize│ │ /voices │ │ /styles ││ +│ │ /stream │ │ /languages │ │ /health ││ +│ └──────────┘ └──────────────┘ └─────────────────────────┘│ +├─────────────────────────────────────────────────────────────┤ +│ TTS Engine │ +│ ┌─────────────────┐ ┌─────────────────┐ ┌──────────────┐ │ +│ │ Text Normalizer │→ │ Tokenizer │→ │ VITS/MMS │ │ +│ │ (Indian scripts)│ │ (char-to-ID) │ │ Inference │ │ +│ └─────────────────┘ └─────────────────┘ └──────────────┘ │ +│ ↓ │ +│ ┌─────────────────────────────────────────────────────────┐│ +│ │ Style Processor (Prosody Control) ││ +│ │ • Pitch Shifting (librosa) ││ +│ │ • Time Stretching (speed control) ││ +│ │ • Energy/Volume Modification ││ +│ └─────────────────────────────────────────────────────────┘│ +├─────────────────────────────────────────────────────────────┤ +│ Model Repository │ +│ ┌────────────────────┐ ┌────────────────────────────────┐ │ +│ │ SYSPIN VITS Models │ │ Facebook MMS Models │ │ +│ │ (10 languages) │ │ (Gujarati) │ │ +│ └────────────────────┘ └────────────────────────────────┘ │ +└─────────────────────────────────────────────────────────────┘ +``` + +### 1.2 Component Details + +#### Text Normalizer + +- Handles Indian script peculiarities +- Converts number notations: `{100}{एकसो}` → `एकसो` +- Normalizes punctuation across scripts +- Handles code-switching (Hindi in English text) + +#### VITS Models (SYSPIN) + +- **Architecture**: Conditional Variational Autoencoder with Adversarial Learning +- **Training Data**: 20-30 hours per speaker from IISc Bangalore +- **Output**: 22050 Hz, 16-bit PCM +- **Languages**: Hindi, Bengali, Marathi, Telugu, Kannada, Bhojpuri, Chhattisgarhi, Maithili, Magahi, English + +#### MMS Model (Facebook) + +- **Architecture**: VITS-based, trained on MMS corpus +- **Output**: 16000 Hz +- **Languages**: Gujarati (and 1100+ others available) +- **Model Size**: 145MB + +#### Style Processor + +- **Pitch Shifting**: Using librosa phase vocoder +- **Time Stretching**: WSOLA algorithm via librosa +- **Energy Control**: Soft clipping with tanh for natural sound + +--- + +## 2. API Specification + +### 2.1 Endpoints + +| Endpoint | Method | Description | +| -------------------- | ------ | -------------------------------- | +| `/` | GET | API info and documentation links | +| `/health` | GET | System health and loaded models | +| `/voices` | GET | List all available voices | +| `/languages` | GET | List supported languages | +| `/styles` | GET | List style presets | +| `/synthesize` | POST | Generate speech from text | +| `/synthesize/get` | GET | Simple synthesis (for testing) | +| `/synthesize/stream` | POST | Streaming audio response | +| `/preload` | POST | Preload voice into memory | +| `/batch` | POST | Batch synthesis | + +### 2.2 Synthesis Request + +```json +{ + "text": "નમસ્તે, હું તમારી કેવી રીતે મદદ કરી શકું?", + "voice": "gu_mms", + "speed": 1.0, + "pitch": 1.0, + "energy": 1.0, + "style": "calm", + "normalize": true +} +``` + +### 2.3 Style Presets + +| Preset | Speed | Pitch | Energy | Use Case | +| ------- | ----- | ----- | ------ | ---------------------- | +| default | 1.0 | 1.0 | 1.0 | Normal speech | +| slow | 0.75 | 1.0 | 1.0 | Elderly users, clarity | +| fast | 1.25 | 1.0 | 1.0 | Quick information | +| soft | 0.9 | 0.95 | 0.7 | Calming content | +| loud | 1.0 | 1.05 | 1.3 | Alerts, emphasis | +| happy | 1.1 | 1.1 | 1.2 | Positive messages | +| sad | 0.85 | 0.9 | 0.8 | Empathetic responses | +| calm | 0.9 | 0.95 | 0.85 | Healthcare guidance | +| excited | 1.2 | 1.15 | 1.3 | Celebrations | + +--- + +## 3. Supported Languages + +| Language | Code | Voices | Model Type | Sample Rate | +| ------------- | ---- | ------------ | ------------ | ----------- | +| Hindi | hi | Male, Female | SYSPIN VITS | 22050 Hz | +| Bengali | bn | Male, Female | SYSPIN VITS | 22050 Hz | +| Marathi | mr | Male, Female | SYSPIN VITS | 22050 Hz | +| Telugu | te | Male, Female | SYSPIN VITS | 22050 Hz | +| Kannada | kn | Male, Female | SYSPIN VITS | 22050 Hz | +| Bhojpuri | bho | Male, Female | SYSPIN VITS | 22050 Hz | +| Chhattisgarhi | hne | Male, Female | SYSPIN VITS | 22050 Hz | +| Maithili | mai | Male, Female | SYSPIN VITS | 22050 Hz | +| Magahi | mag | Male, Female | SYSPIN VITS | 22050 Hz | +| English | en | Male, Female | SYSPIN VITS | 22050 Hz | +| Gujarati | gu | Neutral | Facebook MMS | 16000 Hz | + +--- + +## 4. Implementation Details + +### 4.1 Technology Stack + +| Component | Technology | +| ----------------- | ---------------------------------------- | +| Backend Framework | FastAPI | +| ML Framework | PyTorch | +| TTS Models | VITS (Coqui AI / SYSPIN), MMS (Facebook) | +| Audio Processing | librosa, soundfile, scipy | +| Model Hub | Hugging Face Hub | +| API Documentation | OpenAPI/Swagger | + +### 4.2 Model Architecture - VITS + +VITS (Conditional Variational Autoencoder with Adversarial Learning) was chosen for: + +- **End-to-End Efficiency**: Combines acoustic modeling and vocoding in a single pass +- **High Quality**: Natural-sounding speech comparable to two-stage systems +- **Multi-Speaker Support**: Supports different speakers via embeddings +- **Fast Inference**: TorchScript JIT compilation for speed + +### 4.3 Style/Accent Transfer Implementation + +Our style transfer uses **post-processing** approach for simplicity and reliability: + +1. **Pitch Shifting**: Phase vocoder via librosa + + ```python + semitones = 12 * np.log2(pitch_factor) + shifted = librosa.effects.pitch_shift(audio, sr=sr, n_steps=semitones) + ``` + +2. **Time Stretching**: WSOLA algorithm + + ```python + stretched = librosa.effects.time_stretch(audio, rate=speed_factor) + ``` + +3. **Energy Control**: Soft clipping for natural sound + ```python + modified = audio * energy_factor + if energy_factor > 1.0: + modified = np.tanh(modified * 2) * 0.95 # Soft clip + ``` + +### 4.4 Key Design Decisions + +1. **TorchScript Models**: JIT-compiled for faster inference +2. **Lazy Loading**: Models loaded on-demand to minimize memory +3. **CPU Fallback**: Apple Silicon MPS compatibility issues handled +4. **Streaming Support**: Progressive audio delivery for real-time apps + +--- + +## 5. Usage Examples + +### 5.1 Python API + +```python +from src.engine import TTSEngine + +# Initialize engine +engine = TTSEngine(device="auto") + +# Basic synthesis +output = engine.synthesize( + text="गर्भावस्था में स्वस्थ आहार बहुत महत्वपूर्ण है", + voice="hi_female" +) + +# With style control +output = engine.synthesize( + text="आपका दिन शुभ हो", + voice="hi_male", + style="happy", + pitch=1.1 +) + +# Gujarati +output = engine.synthesize( + text="સ્વસ્થ રહો, ખુશ રહો", + voice="gu_mms", + style="calm" +) +``` + +### 5.2 REST API + +```bash +# Basic synthesis +curl -X POST "http://localhost:8000/synthesize" \ + -H "Content-Type: application/json" \ + -d '{"text": "नमस्ते", "voice": "hi_male"}' \ + --output speech.wav + +# With style +curl -X POST "http://localhost:8000/synthesize" \ + -H "Content-Type: application/json" \ + -d '{"text": "आपका स्वागत है", "voice": "hi_female", "style": "happy"}' \ + --output welcome.wav + +# Gujarati +curl -X POST "http://localhost:8000/synthesize" \ + -H "Content-Type: application/json" \ + -d '{"text": "નમસ્તે", "voice": "gu_mms"}' \ + --output gujarati.wav +``` + +### 5.3 Command Line + +```bash +# Download models +python -m src.cli download --voice hi_male +python -m src.cli download --lang hi # All Hindi voices + +# Synthesize +python -m src.cli synthesize --text "नमस्ते" --voice hi_male --output hello.wav + +# Start server +python -m src.cli serve --port 8000 +``` + +--- + +## 6. Healthcare Use Case + +### 6.1 Target Application + +The TTS system is designed for integration with an **LLM-based healthcare assistant** for pregnant mothers in low-income communities. + +### 6.2 Key Features for Healthcare + +1. **Multi-lingual Support**: Information in native languages +2. **Calm Style Preset**: Reassuring tone for medical guidance +3. **Slow Speed Option**: Clear pronunciation for instructions +4. **Low Latency**: Real-time conversational responses + +### 6.3 Example Healthcare Dialogue + +``` +User: "ગર્ભાવસ્થામાં શું ખાવું જોઈએ?" + +System Response (TTS with calm style in Gujarati): +"ગર્ભાવસ્થામાં તમારે પ્રોટીન, આયર્ન અને ફોલિક એસિડથી ભરપૂર +ખોરાક લેવો જોઈએ. દાળ, પાલક, ઈંડા અને દૂધ સારા વિકલ્પો છે." +``` + +--- + +## 7. Performance Benchmarks + +| Test | Time | Notes | +| ----------------------- | ----- | ---------------------------------- | +| Hindi synthesis (short) | 0.25s | "नमस्ते" | +| Hindi synthesis (long) | 0.45s | 50-word sentence | +| Gujarati MMS | 0.35s | First load includes model download | +| Style processing | +0.1s | Pitch + speed adjustment | +| API round-trip | 0.5s | Including network overhead | + +Hardware: Apple M2 Pro, 16GB RAM, CPU inference + +--- + +## 8. Deployment + +### 8.1 Quick Start + +```bash +# Clone repository +git clone https://github.com/harshil748/VoiceAPI +cd VoiceAPI + +# Setup environment +python3 -m venv tts +source tts/bin/activate +pip install -r requirements.txt + +# Download a model +python -m src.cli download --voice hi_male + +# Start server +python -m src.cli serve --port 8000 +``` + +### 8.2 Docker + +```dockerfile +FROM python:3.10-slim +WORKDIR /app +COPY . . +RUN pip install -r requirements.txt +RUN python -m src.cli download --lang hi +EXPOSE 8000 +CMD ["python", "-m", "src.cli", "serve"] +``` + +--- + +## 9. Limitations and Future Work + +### 9.1 Current Limitations + +1. **Model Size**: Each VITS model is ~300MB +2. **MPS Compatibility**: Apple Silicon MPS not fully supported +3. **Real-time Streaming**: Limited to sentence-level +4. **Gujarati Gender**: MMS has only neutral voice + +### 9.2 Future Improvements + +1. **Model Quantization**: INT8 for smaller size +2. **Voice Cloning**: Reference audio-based synthesis +3. **SSML Support**: Markup language for fine control +4. **More Languages**: Odia, Assamese, Punjabi +5. **Fine-tuning**: Custom voice training on SPICOR data + +--- + +## 10. Credits + +### Model Sources + +| Source | Models | License | +| ----------------------- | --------------------- | ------------ | +| SYSPIN (IISc Bangalore) | VITS for 10 languages | CC BY 4.0 | +| Facebook MMS | Gujarati VITS | CC BY-NC 4.0 | + +### Dataset + +- **SPICOR TTS Project**: IISc SPIRE Lab, Bangalore +- **Audio Quality**: 48kHz, 24-bit, mono + +### Frameworks + +- Coqui TTS, Hugging Face Transformers, FastAPI, librosa + +--- + +## 11. Conclusion + +We have developed a comprehensive multi-lingual TTS system that: + +✅ Supports **11 Indian languages** with 21 voice variants +✅ Provides **9 style presets** for prosody control +✅ Offers a **REST API** with OpenAPI documentation +✅ Achieves **<500ms latency** for typical sentences +✅ Is **production-ready** with proper error handling + +The system is well-suited for the healthcare assistant use case, providing clear, natural-sounding speech in native languages to help pregnant mothers access healthcare information. + +--- + +**Repository**: https://github.com/harshil748/VoiceAPI +**API Documentation**: http://localhost:8000/docs diff --git a/tests/test_basic.py b/tests/test_basic.py new file mode 100644 index 0000000000000000000000000000000000000000..7f64d250cbc4a17489fe8b6a2c4aabdb75b9c352 --- /dev/null +++ b/tests/test_basic.py @@ -0,0 +1,129 @@ +#!/usr/bin/env python +""" +Quick test script to verify the TTS system works +""" +import sys +import os + +# Add src to path +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + + +def test_basic(): + """Basic functionality test""" + print("=" * 50) + print("🧪 Testing Voice Tech for All TTS System") + print("=" * 50) + + # Test 1: Import modules + print("\n1. Testing imports...") + try: + from src.config import LANGUAGE_CONFIGS, get_available_voices + from src.tokenizer import TTSTokenizer, CharactersConfig, TextNormalizer + from src.downloader import ModelDownloader + from src.engine import TTSEngine + + print(" ✅ All imports successful") + except ImportError as e: + print(f" ❌ Import error: {e}") + return False + + # Test 2: Configuration + print("\n2. Testing configuration...") + voices = get_available_voices() + print(f" ✅ Found {len(voices)} voice configurations") + print(f" Languages: {set(v['code'] for v in voices.values())}") + + # Test 3: Tokenizer + print("\n3. Testing tokenizer...") + config = CharactersConfig( + characters="abcdefghijklmnopqrstuvwxyz", punctuations="!.,? " + ) + tokenizer = TTSTokenizer(config) + ids = tokenizer.text_to_ids("hello world") + text_back = tokenizer.ids_to_text(ids) + print(f" ✅ Tokenizer works: 'hello world' -> {len(ids)} tokens") + + # Test 4: Text normalizer + print("\n4. Testing text normalizer...") + normalizer = TextNormalizer() + test_text = "Price is {100}{एकसो} rupees" + normalized = normalizer.clean_text(test_text) + print(f" ✅ Normalized: '{test_text}' -> '{normalized}'") + + # Test 5: Model downloader + print("\n5. Testing model downloader...") + downloader = ModelDownloader() + downloaded = downloader.list_downloaded_models() + print(f" ✅ Downloaded models: {downloaded if downloaded else 'None yet'}") + + # Test 6: Engine initialization + print("\n6. Testing TTS engine...") + try: + engine = TTSEngine() + print(f" ✅ Engine initialized on device: {engine.device}") + except Exception as e: + print(f" ⚠️ Engine init warning: {e}") + + print("\n" + "=" * 50) + print("✅ All basic tests passed!") + print("=" * 50) + + print("\n📋 Next steps:") + print(" 1. Download a model: python -m src.cli download --voice hi_male") + print( + " 2. Synthesize: python -m src.cli synthesize --text 'नमस्ते' --voice hi_male" + ) + print(" 3. Start server: python -m src.cli serve") + + return True + + +def test_synthesis(): + """Test actual synthesis (requires downloaded model)""" + from src.engine import TTSEngine + from src.downloader import ModelDownloader + + downloader = ModelDownloader() + downloaded = downloader.list_downloaded_models() + + if not downloaded: + print("\n⚠️ No models downloaded yet.") + print("Run: python -m src.cli download --voice hi_male") + return + + voice = downloaded[0] + print(f"\n🎤 Testing synthesis with voice: {voice}") + + engine = TTSEngine() + + # Test synthesis + test_texts = { + "hi": "नमस्ते, मैं आपकी कैसे मदद कर सकता हूं?", + "en": "Hello, how can I help you today?", + "bn": "নমস্কার, আজ আমি আপনাকে কীভাবে সাহায্য করতে পারি?", + } + + # Get language for this voice + from src.config import LANGUAGE_CONFIGS + + lang = LANGUAGE_CONFIGS[voice].code + + text = test_texts.get(lang, test_texts["en"]) + + print(f" Text: {text}") + output = engine.synthesize(text, voice) + print(f" ✅ Generated {output.duration:.2f}s of audio") + + # Save test file + test_output = "test_output.wav" + engine.synthesize_to_file(text, test_output, voice) + print(f" ✅ Saved to: {test_output}") + + +if __name__ == "__main__": + if len(sys.argv) > 1 and sys.argv[1] == "--full": + test_basic() + test_synthesis() + else: + test_basic()