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README.md

@@ -1,3 +1,255 @@
----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+language:
+- as
+- bn
+- brx
+- doi
+- kn
+- mai
+- ml
+- mr
+- ne
+- pa
+- sa
+- ta
+- te
+library_name: transformers
+pipeline_tag: text-to-speech
+tags:
+- text-to-speech
+---
+# VITS TTS for Indian Languages
+
+This repository contains a VITS-based Text-to-Speech (TTS) model fine-tuned for Indian languages. The model supports multiple Indian languages and a wide range of speaking styles and emotions, making it suitable for diverse use cases such as conversational AI, audiobooks, and more.
+
+---
+
+## Model Overview
+
+The model `ai4bharat/vits_rasa_13` is based on the VITS architecture and supports the following features:
+- **Languages**: Multiple Indian languages.
+- **Styles**: Various speaking styles and emotions.
+- **Speaker IDs**: Predefined speaker profiles for male and female voices.
+
+---
+
+## Installation
+
+```bash
+pip install transformers torch
+```
+
+---
+
+## Usage
+
+Here's a quick example to get started:
+
+```python
+import soundfile as sf
+from transformers import AutoModel, AutoTokenizer
+
+model = AutoModel.from_pretrained("ai4bharat/vits_rasa_13", trust_remote_code=True).to("cuda")
+tokenizer = AutoTokenizer.from_pretrained("ai4bharat/vits_rasa_13", trust_remote_code=True)
+
+text = "ਕੀ ਮੈਂ ਇਸ ਹਫਤੇ ਦੇ ਅੰਤ ਵਿੱਚ ਰੁੱਝਿਆ ਹੋਇਆ ਹਾਂ?"  # Example text in Punjabi
+speaker_id = 16  # PAN_M
+style_id = 0  # ALEXA
+
+inputs = tokenizer(text=text, return_tensors="pt").to("cuda")
+outputs = model(inputs['input_ids'], speaker_id=speaker_id, emotion_id=style_id)
+sf.write("audio.wav", outputs.waveform.squeeze(), model.config.sampling_rate)
+print(outputs.waveform.shape)
+```
+
+---
+
+## Supported Languages
+
+- `Assamese` 
+- `Bengali`   
+- `Bodo`     
+- `Dogri`      
+- `Kannada`    
+- `Maithili`   
+- `Malayalam`  
+- `Marathi`    
+- `Nepali`     
+- `Punjabi`    
+- `Sanskrit`   
+- `Tamil`      
+- `Telugu`
+
+---
+
+## Speaker-Style Identifier Overview
+
+<div style="display: flex; align-items: flex-start; gap: 20px; margin: 0; padding: 0;">
+
+<table style="margin: 0; padding: 0; border-spacing: 0;">
+  <tr>
+    <th>Speaker Name</th>
+    <th>Speaker ID</th>
+  </tr>
+  <tr>
+    <td>ASM_F</td>
+    <td>0</td>
+  </tr>
+  <tr>
+    <td>ASM_M</td>
+    <td>1</td>
+  </tr>
+  <tr>
+    <td>BEN_F</td>
+    <td>2</td>
+  </tr>
+  <tr>
+    <td>BEN_M</td>
+    <td>3</td>
+  </tr>
+  <tr>
+    <td>BRX_F</td>
+    <td>4</td>
+  </tr>
+  <tr>
+    <td>BRX_M</td>
+    <td>5</td>
+  </tr>
+  <tr>
+    <td>DOI_F</td>
+    <td>6</td>
+  </tr>
+  <tr>
+    <td>DOI_M</td>
+    <td>7</td>
+  </tr>
+  <tr>
+    <td>KAN_F</td>
+    <td>8</td>
+  </tr>
+  <tr>
+    <td>KAN_M</td>
+    <td>9</td>
+  </tr>
+  <tr>
+    <td>MAI_M</td>
+    <td>10</td>
+  </tr>
+  <tr>
+    <td>MAL_F</td>
+    <td>11</td>
+  </tr>
+  <tr>
+    <td>MAR_F</td>
+    <td>12</td>
+  </tr>
+  <tr>
+    <td>MAR_M</td>
+    <td>13</td>
+  </tr>
+  <tr>
+    <td>NEP_F</td>
+    <td>14</td>
+  </tr>
+  <tr>
+    <td>PAN_F</td>
+    <td>15</td>
+  </tr>
+  <tr>
+    <td>PAN_M</td>
+    <td>16</td>
+  </tr>
+  <tr>
+    <td>SAN_M</td>
+    <td>17</td>
+  </tr>
+  <tr>
+    <td>TAM_F</td>
+    <td>18</td>
+  </tr>
+  <tr>
+    <td>TEL_F</td>
+    <td>19</td>
+  </tr>
+</table>
+
+<table>
+  <tr>
+    <th>Style Name</th>
+    <th>Style ID</th>
+  </tr>
+  <tr>
+    <td>ALEXA</td>
+    <td>0</td>
+  </tr>
+  <tr>
+    <td>ANGER</td>
+    <td>1</td>
+  </tr>
+  <tr>
+    <td>BB</td>
+    <td>2</td>
+  </tr>
+  <tr>
+    <td>BOOK</td>
+    <td>3</td>
+  </tr>
+  <tr>
+    <td>CONV</td>
+    <td>4</td>
+  </tr>
+  <tr>
+    <td>DIGI</td>
+    <td>5</td>
+  </tr>
+  <tr>
+    <td>DISGUST</td>
+    <td>6</td>
+  </tr>
+  <tr>
+    <td>FEAR</td>
+    <td>7</td>
+  </tr>
+  <tr>
+    <td>HAPPY</td>
+    <td>8</td>
+  </tr>
+  <tr>
+    <td>NEWS</td>
+    <td>10</td>
+  </tr>
+  <tr>
+    <td>SAD</td>
+    <td>12</td>
+  </tr>
+  <tr>
+    <td>SURPRISE</td>
+    <td>14</td>
+  </tr>
+  <tr>
+    <td>UMANG</td>
+    <td>15</td>
+  </tr>
+  <tr>
+    <td>WIKI</td>
+    <td>16</td>
+  </tr>
+</table>
+
+</div>
+
+---
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@article{ai4bharat_vits_rasa_13,
+  title={VITS TTS for Indian Languages},
+  author={Ashwin Sankar},
+  year={2024},
+  publisher={Hugging Face}
+}
+```

config.json

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+{
+  "_name_or_path": "rasa_boosted",
+  "activation_dropout": 0.1,
+  "architectures": [
+    "IndicVitsModel"
+  ],
+  "attention_dropout": 0.1,
+  "auto_map": {
+    "AutoConfig": "configuration_vits.IndicVitsConfig",
+    "AutoModel": "modeling_vits.IndicVitsModel"
+  },
+  "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,
+  "emotion_embedding_size": 256,
+  "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": "indic_vits_model",
+  "noise_scale": 0.667,
+  "noise_scale_duration": 0.8,
+  "num_attention_heads": 2,
+  "num_emotions": 32,
+  "num_hidden_layers": 6,
+  "num_speakers": 1024,
+  "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": 24000,
+  "speaker_embedding_size": 256,
+  "speaking_rate": 1.0,
+  "spectrogram_bins": 513,
+  "tokenizer_class": "IndicVitsTokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1",
+  "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": 1260,
+  "wavenet_dilation_rate": 1,
+  "wavenet_dropout": 0.0,
+  "wavenet_kernel_size": 5,
+  "window_size": 4
+}

reexport_vits_with_controls.py

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+import argparse
+import shutil
+import subprocess
+from pathlib import Path
+
+import onnx
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+
+class VitsExportWrapper(torch.nn.Module):
+    def __init__(self, model: torch.nn.Module):
+        super().__init__()
+        self.model = model.eval()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor,
+        speaker_id: torch.Tensor,
+        emotion_id: torch.Tensor,
+    ) -> torch.Tensor:
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            speaker_id=speaker_id.to(torch.long),
+            emotion_id=emotion_id.to(torch.long),
+            return_dict=True,
+        )
+        return outputs.waveform
+
+
+def inspect_onnx(onnx_path: Path) -> None:
+    model = onnx.load(str(onnx_path))
+    print("onnx inputs:")
+    for value in model.graph.input:
+        tensor_type = value.type.tensor_type
+        dims = []
+        for dim in tensor_type.shape.dim:
+            if dim.dim_value:
+                dims.append(dim.dim_value)
+            elif dim.dim_param:
+                dims.append(dim.dim_param)
+            else:
+                dims.append("?")
+        print(f"  {value.name}: shape={dims}, elem_type={tensor_type.elem_type}")
+
+
+def inspect_mnn(mnn_path: Path) -> None:
+    import MNN.expr as expr
+
+    graph_vars = expr.load_as_dict(str(mnn_path))
+    for name in ("input_ids", "attention_mask", "speaker_id", "emotion_id"):
+        if name not in graph_vars:
+            print(f"mnn input missing: {name}")
+            continue
+        var = graph_vars[name]
+        print(f"mnn input {name}: shape={var.shape}, dtype={var.dtype}, format={var.data_format}")
+
+
+def export_onnx(args: argparse.Namespace) -> None:
+    model_dir = Path(args.model_dir)
+    tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True, local_files_only=True)
+    model = AutoModel.from_pretrained(model_dir, trust_remote_code=True, local_files_only=True)
+    wrapper = VitsExportWrapper(model)
+
+    tokenized = tokenizer(text=args.text, return_tensors="pt")
+    input_ids = tokenized["input_ids"].to(torch.long)
+    attention_mask = tokenized.get("attention_mask", torch.ones_like(input_ids)).to(torch.long)
+    speaker_id = torch.tensor([args.speaker_id], dtype=torch.long)
+    emotion_id = torch.tensor([args.style_id], dtype=torch.long)
+
+    torch.onnx.export(
+        wrapper,
+        (input_ids, attention_mask, speaker_id, emotion_id),
+        str(args.onnx_output),
+        input_names=["input_ids", "attention_mask", "speaker_id", "emotion_id"],
+        output_names=["waveform"],
+        dynamic_axes={
+            "input_ids": {1: "text_length"},
+            "attention_mask": {1: "text_length"},
+            "waveform": {1: "audio_length"},
+        },
+        opset_version=args.opset,
+        do_constant_folding=True,
+        dynamo=False,
+    )
+    print(f"wrote {args.onnx_output}")
+    inspect_onnx(Path(args.onnx_output))
+
+
+def convert_to_mnn(args: argparse.Namespace) -> None:
+    if not args.mnn_output:
+        return
+
+    converter = shutil.which("MNNConvert")
+    if converter is None:
+        raise FileNotFoundError("MNNConvert not found in PATH")
+
+    command = [
+        converter,
+        "-f",
+        "ONNX",
+        "--modelFile",
+        str(args.onnx_output),
+        "--MNNModel",
+        str(args.mnn_output),
+        "--bizCode",
+        "MNN",
+    ]
+    subprocess.run(command, check=True)
+    print(f"wrote {args.mnn_output}")
+    inspect_mnn(Path(args.mnn_output))
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Re-export local VITS weights with speaker/style control inputs.")
+    parser.add_argument("--model-dir", default=".", help="Directory containing config, tokenizer, custom code, and weights.")
+    parser.add_argument("--text", default="வணக்கம்", help="Sample text used to trace the export graph.")
+    parser.add_argument("--speaker-id", type=int, default=18, help="Sample speaker ID used during tracing.")
+    parser.add_argument("--style-id", type=int, default=0, help="Sample style or emotion ID used during tracing.")
+    parser.add_argument("--onnx-output", default="vits_tamil_with_controls.onnx", help="Path for exported ONNX.")
+    parser.add_argument("--mnn-output", default="vits_tamil_with_controls.mnn", help="Optional output path for converted MNN.")
+    parser.add_argument("--opset", type=int, default=17, help="ONNX opset version.")
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    export_onnx(args)
+    convert_to_mnn(args)
+
+
+if __name__ == "__main__":
+    main()

run_mnn_inference.py

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+import argparse
+import json
+import wave
+from pathlib import Path
+
+import numpy as np
+import MNN.expr as expr
+import MNN.nn as nn
+
+
+def load_vocab(vocab_path: Path) -> dict[str, int]:
+    with vocab_path.open(encoding="utf-8") as handle:
+        return json.load(handle)
+
+
+def normalize_text(text: str, vocab: dict[str, int]) -> str:
+    lowered = "".join(char.lower() for char in text)
+    return "".join(char for char in lowered if char in vocab).strip()
+
+
+def tokenize(text: str, vocab: dict[str, int], add_blank: bool = True) -> np.ndarray:
+    filtered = normalize_text(text, vocab)
+    if not filtered:
+        raise ValueError("Text becomes empty after tokenizer normalization.")
+
+    token_ids = [vocab[char] for char in filtered]
+    if add_blank:
+        interspersed = [0] * (len(token_ids) * 2 + 1)
+        interspersed[1::2] = token_ids
+        token_ids = interspersed
+
+    return np.asarray([token_ids], dtype=np.int32)
+
+
+def write_wav(path: Path, waveform: np.ndarray, sample_rate: int) -> None:
+    clipped = np.clip(waveform, -1.0, 1.0)
+    pcm = (clipped * 32767.0).astype(np.int16)
+
+    with wave.open(str(path), "wb") as wav_file:
+        wav_file.setnchannels(1)
+        wav_file.setsampwidth(2)
+        wav_file.setframerate(sample_rate)
+        wav_file.writeframes(pcm.tobytes())
+
+
+def build_placeholder(value: np.ndarray, reference_var) -> object:
+    placeholder = expr.placeholder(list(value.shape), reference_var.data_format, reference_var.dtype)
+    placeholder.write(value)
+    return placeholder
+
+
+def maybe_add_control_input(
+    model_inputs: list[object],
+    model_input_names: list[str],
+    graph_vars: dict[str, object],
+    input_name: str,
+    input_value: int | None,
+    flag_name: str,
+) -> None:
+    if input_name not in graph_vars:
+        if input_value is not None:
+            raise ValueError(
+                f"Model does not expose {input_name}. Re-export the model with {input_name} as a graph input."
+            )
+        return
+
+    if input_value is None:
+        raise ValueError(f"Model expects {input_name}. Pass {flag_name}.")
+
+    value = np.asarray([input_value], dtype=np.int32)
+    model_inputs.append(build_placeholder(value, graph_vars[input_name]))
+    model_input_names.append(input_name)
+
+
+def synthesize(
+    model_path: Path,
+    vocab_path: Path,
+    text: str,
+    speaker_id: int | None = None,
+    style_id: int | None = None,
+) -> np.ndarray:
+    vocab = load_vocab(vocab_path)
+    input_ids = tokenize(text, vocab)
+    attention_mask = np.ones_like(input_ids, dtype=np.int32)
+    graph_vars = expr.load_as_dict(str(model_path))
+
+    model_inputs = [
+        build_placeholder(input_ids, graph_vars["input_ids"]),
+        build_placeholder(attention_mask, graph_vars["attention_mask"]),
+    ]
+    model_input_names = ["input_ids", "attention_mask"]
+
+    maybe_add_control_input(model_inputs, model_input_names, graph_vars, "speaker_id", speaker_id, "--speaker-id")
+    maybe_add_control_input(model_inputs, model_input_names, graph_vars, "emotion_id", style_id, "--style-id")
+
+    module = nn.load_module_from_file(
+        str(model_path),
+        model_input_names,
+        ["waveform"],
+        dynamic=True,
+    )
+    outputs = module.forward(model_inputs)
+    if not outputs:
+        raise RuntimeError("PyMNN returned no outputs.")
+
+    waveform = outputs[0].read()
+    return np.asarray(waveform).squeeze(0)
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Run VITS MNN inference with PyMNN.")
+    parser.add_argument("--text", required=True, help="Input text to synthesize.")
+    parser.add_argument("--model", default="vits_tamil_static.mnn", help="Path to the MNN model.")
+    parser.add_argument("--vocab", default="vocab.json", help="Path to the tokenizer vocabulary JSON.")
+    parser.add_argument("--output", default="audio_mnn.wav", help="Output WAV file path.")
+    parser.add_argument("--sample-rate", type=int, default=24000, help="Output WAV sample rate.")
+    parser.add_argument("--speaker-id", type=int, default=None, help="Speaker ID for models that expose speaker_id.")
+    parser.add_argument(
+        "--style-id",
+        "--emotion-id",
+        dest="style_id",
+        type=int,
+        default=None,
+        help="Style or emotion ID for models that expose emotion_id.",
+    )
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    waveform = synthesize(
+        Path(args.model),
+        Path(args.vocab),
+        args.text,
+        speaker_id=args.speaker_id,
+        style_id=args.style_id,
+    )
+    write_wav(Path(args.output), waveform, args.sample_rate)
+    print(f"wrote {args.output} with {waveform.shape[-1]} samples at {args.sample_rate} Hz")
+
+
+if __name__ == "__main__":
+    main()

special_tokens_map.json

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+{
+  "pad_token": {
+    "content": "_",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,37 @@
+{
+  "add_blank": true,
+  "added_tokens_decoder": {
+    "95": {
+      "content": "_",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1218": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_vits.IndicVitsTokenizer",
+      null
+    ]
+  },
+  "clean_up_tokenization_spaces": false,
+  "extra_special_tokens": {},
+  "is_uroman": false,
+  "language": null,
+  "model_max_length": 1000000000000000019884624838656,
+  "normalize": true,
+  "pad_token": "_",
+  "phonemize": false,
+  "tokenizer_class": "IndicVitsTokenizer",
+  "unk_token": "<unk>"
+}

vocab.json

@@ -0,0 +1,1240 @@
+{
+  " ": 16,
+  "!": 69,
+  "\"": 70,
+  "#": 71,
+  "$": 72,
+  "%": 73,
+  "&": 74,
+  "'": 1247,
+  "(": 76,
+  ")": 77,
+  "*": 78,
+  "+": 79,
+  ",": 80,
+  "-": 81,
+  ".": 82,
+  "/": 83,
+  "0": 1250,
+  "1": 1251,
+  "2": 1252,
+  "3": 1253,
+  "4": 1254,
+  "5": 1255,
+  "6": 1256,
+  "7": 1257,
+  "8": 1258,
+  "9": 1259,
+  ":": 84,
+  ";": 85,
+  "<": 86,
+  "=": 87,
+  ">": 88,
+  "?": 89,
+  "@": 90,
+  "A": 17,
+  "B": 18,
+  "C": 19,
+  "D": 20,
+  "E": 21,
+  "F": 22,
+  "G": 23,
+  "H": 24,
+  "I": 25,
+  "J": 26,
+  "K": 27,
+  "L": 28,
+  "M": 29,
+  "N": 30,
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