Add TTS Tokenizer, Technical Report, and Basic Tests

- Implemented `TTSTokenizer` for multi-lingual TTS models, including character and punctuation handling.
- Created a comprehensive technical report detailing the multi-lingual TTS system, architecture, API specifications, and healthcare use case.
- Added a basic test script to verify the functionality of the TTS system, including module imports, configuration checks, tokenizer functionality, text normalization, model downloading, and engine initialization.

.gitignore

@@ -0,0 +1,4 @@
+# Ignore generated audio + specs
+outputs/
+*.wav
+Voicetech API Specification.pdf

Procfile

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+web: python download_models.py && uvicorn src.api:app --host 0.0.0.0 --port ${PORT:-8000}

README.md

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+# 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**

download_models.py

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+#!/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())

models/bho_female/.gitattributes

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models/bho_female/README.md

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+---
+license: cc-by-4.0
+---

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+    "test_sentences_file": "",
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+    "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"
+}

models/bho_male/.gitattributes

@@ -0,0 +1,35 @@
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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
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+*.msgpack filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
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+*.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
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+*tfevents* filter=lfs diff=lfs merge=lfs -text

models/bho_male/README.md

@@ -0,0 +1,3 @@
+---
+license: cc-by-4.0
+---

models/bho_male/checkpoint_200000.pth

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+version https://git-lfs.github.com/spec/v1
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+size 997766981

models/bho_male/config.json

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+{
+    "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": "<PAD>",
+        "eos": "<EOS>",
+        "bos": "<BOS>",
+        "blank": "<BLNK>",
+        "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,
+        "dilation_rate_posterior_encoder": 1,
+        "num_layers_posterior_encoder": 16,
+        "kernel_size_flow": 5,
+        "dilation_rate_flow": 1,
+        "num_layers_flow": 4,
+        "resblock_type_decoder": "1",
+        "resblock_kernel_sizes_decoder": [
+            3,
+            7,
+            11
+        ],
+        "resblock_dilation_sizes_decoder": [
+            [
+                1,
+                3,
+                5
+            ],
+            [
+                1,
+                3,
+                5
+            ],
+            [
+                1,
+                3,
+                5
+            ]
+        ],
+        "upsample_rates_decoder": [
+            8,
+            8,
+            2,
+            2
+        ],
+        "upsample_initial_channel_decoder": 512,
+        "upsample_kernel_sizes_decoder": [
+            16,
+            16,
+            4,
+            4
+        ],
+        "periods_multi_period_discriminator": [
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+            3,
+            5,
+            7,
+            11
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+        "use_sdp": true,
+        "noise_scale": 1.0,
+        "inference_noise_scale": 0.667,
+        "length_scale": 1,
+        "noise_scale_dp": 1.0,
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+        "max_inference_len": null,
+        "init_discriminator": true,
+        "use_spectral_norm_disriminator": false,
+        "use_speaker_embedding": false,
+        "num_speakers": 0,
+        "speakers_file": null,
+        "d_vector_file": null,
+        "speaker_embedding_channels": 256,
+        "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"
+}

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

models/bn_female/chars.txt

@@ -0,0 +1 @@
+ূঞংঘঔদলৌআডখরথটোৗঙঐানষঝবছঅঢ়ঁপউধঢশগয়।?িক,যঈস্ত়ফঋৈজ'ীঠৰণওৎঃমচঊড়ইুভে এ"ৃহ

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="<PAD>",
+        eos="<EOS>",
+        bos="<BOS>",
+        blank="<BLNK>",
+        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)

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

models/bn_male/chars.txt

@@ -0,0 +1 @@
+ূঞংঘঔদলৌআডখরঃটোৗঙঐনাঝষবঅছঢ়ঁপউধঢশগয়।?িক,যঈসত্ৈফ়ঊজ'ীঠৎণওঋৰমচড়ভুইে থএ"ৃহ

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 = "<PAD>"
+_eos = "<EOS>"
+_bos = "<BOS>"
+_blank = "<BLNK>"  # 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, "<BLNK>", 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

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="<PAD>",
+        eos="<EOS>",
+        bos="<BOS>",
+        blank="<BLNK>",
+        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)

models/en_female/.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
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models/en_female/README.md

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+---
+license: cc-by-4.0
+---

models/en_female/chars.txt

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+pqw'"sgufmxre?d!lcab,zk.iytoh jvn

models/en_female/en_female_vits_30hrs.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9dfa80f08da6ca7222a16cb6d919251fb733d3f03042848a20201fa6ae0d0b9c
+size 333229574

models/en_female/extra.py

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+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 = "<PAD>"
+_eos = "<EOS>"
+_bos = "<BOS>"
+_blank = "<BLNK>"  # 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, "<BLNK>", 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

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="<PAD>",
+        eos="<EOS>",
+        bos="<BOS>",
+        blank="<BLNK>",
+        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)

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

models/en_male/README.md

@@ -0,0 +1,3 @@
+---
+license: cc-by-4.0
+---

models/en_male/chars.txt

@@ -0,0 +1 @@
+pqw'"sgufmxre?d!lcab,zk.iytoh jvn

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

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 = "<PAD>"
+_eos = "<EOS>"
+_bos = "<BOS>"
+_blank = "<BLNK>"  # 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, "<BLNK>", 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

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="<PAD>",
+        eos="<EOS>",
+        bos="<BOS>",
+        blank="<BLNK>",
+        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)

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
+}

models/gu_mms/special_tokens_map.json

@@ -0,0 +1,4 @@
+{
+  "pad_token": "|",
+  "unk_token": "<unk>"
+}

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": "<unk>"
+}

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
+}

models/hi_female/chars.txt

@@ -0,0 +1 @@
+शदऊतसओषमऱढै?ख़ौक़ड़ःिअनठय़ज़फ़्खँे।ंऋउ'हछङझ" ुणऔघयञृएईॆीपचॉॠवगडटइ,बॅूऐफकजलग़आधोथाभढ़ऑ

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 = "<PAD>"
+_eos = "<EOS>"
+_bos = "<BOS>"
+_blank = "<BLNK>"  # 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, "<BLNK>", 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

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

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="<PAD>",
+        eos="<EOS>",
+        bos="<BOS>",
+        blank="<BLNK>",
+        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)

models/hi_male/chars.txt

@@ -0,0 +1 @@
+शदऊतओसषमऱढै?ख़ौक़ड़ःिअनठय़ज़फ़्खँे।ंऋउ'हछङझ" ुणऔयघञृएईॆीपचॉॠवगडटइ,बॅूऐफजकलग़आधोथाभढ़ऑ

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 = "<PAD>"
+_eos = "<EOS>"
+_bos = "<BOS>"
+_blank = "<BLNK>"  # 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, "<BLNK>", 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

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

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="<PAD>",
+        eos="<EOS>",
+        bos="<BOS>",
+        blank="<BLNK>",
+        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)

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

models/hne_female/README.md

@@ -0,0 +1,3 @@
+---
+license: cc-by-4.0
+---

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

models/hne_female/chars.txt

@@ -0,0 +1 @@
+खछगचऊुलशौढ़इणज़झैठढजफ़औ्ड़फूेानटॅयव़ऋदप.थअँऑआघहतषरसभउञडएईऐक़ िओ?धी,ॉंख़कोबमृ