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| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| # This code is modified from | |
| # https://github.com/lifeiteng/vall-e/blob/9c69096d603ce13174fb5cb025f185e2e9b36ac7/valle/data/tokenizer.py | |
| import re | |
| from typing import Any, Dict, List, Optional, Pattern, Union | |
| import torch | |
| import torchaudio | |
| from encodec import EncodecModel | |
| from encodec.utils import convert_audio | |
| class AudioTokenizer: | |
| """EnCodec audio tokenizer for encoding and decoding audio. | |
| Attributes: | |
| device: The device on which the codec model is loaded. | |
| codec: The pretrained EnCodec model. | |
| sample_rate: Sample rate of the model. | |
| channels: Number of audio channels in the model. | |
| """ | |
| def __init__(self, device: Any = None) -> None: | |
| model = EncodecModel.encodec_model_24khz() | |
| model.set_target_bandwidth(6.0) | |
| remove_encodec_weight_norm(model) | |
| if not device: | |
| device = torch.device("cpu") | |
| if torch.cuda.is_available(): | |
| device = torch.device("cuda:0") | |
| self._device = device | |
| self.codec = model.to(device) | |
| self.sample_rate = model.sample_rate | |
| self.channels = model.channels | |
| def device(self): | |
| return self._device | |
| def encode(self, wav: torch.Tensor) -> torch.Tensor: | |
| """Encode the audio waveform. | |
| Args: | |
| wav: A tensor representing the audio waveform. | |
| Returns: | |
| A tensor representing the encoded audio. | |
| """ | |
| return self.codec.encode(wav.to(self.device)) | |
| def decode(self, frames: torch.Tensor) -> torch.Tensor: | |
| """Decode the encoded audio frames. | |
| Args: | |
| frames: A tensor representing the encoded audio frames. | |
| Returns: | |
| A tensor representing the decoded audio waveform. | |
| """ | |
| return self.codec.decode(frames) | |
| def tokenize_audio(tokenizer: AudioTokenizer, audio_path: str): | |
| """ | |
| Tokenize the audio waveform using the given AudioTokenizer. | |
| Args: | |
| tokenizer: An instance of AudioTokenizer. | |
| audio_path: Path to the audio file. | |
| Returns: | |
| A tensor of encoded frames from the audio. | |
| Raises: | |
| FileNotFoundError: If the audio file is not found. | |
| RuntimeError: If there's an error processing the audio data. | |
| """ | |
| # try: | |
| # Load and preprocess the audio waveform | |
| wav, sr = torchaudio.load(audio_path) | |
| wav = convert_audio(wav, sr, tokenizer.sample_rate, tokenizer.channels) | |
| wav = wav.unsqueeze(0) | |
| # Extract discrete codes from EnCodec | |
| with torch.no_grad(): | |
| encoded_frames = tokenizer.encode(wav) | |
| return encoded_frames | |
| # except FileNotFoundError: | |
| # raise FileNotFoundError(f"Audio file not found at {audio_path}") | |
| # except Exception as e: | |
| # raise RuntimeError(f"Error processing audio data: {e}") | |
| def remove_encodec_weight_norm(model): | |
| from encodec.modules import SConv1d | |
| from encodec.modules.seanet import SConvTranspose1d, SEANetResnetBlock | |
| from torch.nn.utils import remove_weight_norm | |
| encoder = model.encoder.model | |
| for key in encoder._modules: | |
| if isinstance(encoder._modules[key], SEANetResnetBlock): | |
| remove_weight_norm(encoder._modules[key].shortcut.conv.conv) | |
| block_modules = encoder._modules[key].block._modules | |
| for skey in block_modules: | |
| if isinstance(block_modules[skey], SConv1d): | |
| remove_weight_norm(block_modules[skey].conv.conv) | |
| elif isinstance(encoder._modules[key], SConv1d): | |
| remove_weight_norm(encoder._modules[key].conv.conv) | |
| decoder = model.decoder.model | |
| for key in decoder._modules: | |
| if isinstance(decoder._modules[key], SEANetResnetBlock): | |
| remove_weight_norm(decoder._modules[key].shortcut.conv.conv) | |
| block_modules = decoder._modules[key].block._modules | |
| for skey in block_modules: | |
| if isinstance(block_modules[skey], SConv1d): | |
| remove_weight_norm(block_modules[skey].conv.conv) | |
| elif isinstance(decoder._modules[key], SConvTranspose1d): | |
| remove_weight_norm(decoder._modules[key].convtr.convtr) | |
| elif isinstance(decoder._modules[key], SConv1d): | |
| remove_weight_norm(decoder._modules[key].conv.conv) | |
| def extract_encodec_token(wav_path): | |
| model = EncodecModel.encodec_model_24khz() | |
| model.set_target_bandwidth(6.0) | |
| wav, sr = torchaudio.load(wav_path) | |
| wav = convert_audio(wav, sr, model.sample_rate, model.channels) | |
| wav = wav.unsqueeze(0) | |
| if torch.cuda.is_available(): | |
| model = model.cuda() | |
| wav = wav.cuda() | |
| with torch.no_grad(): | |
| encoded_frames = model.encode(wav) | |
| codes_ = torch.cat( | |
| [encoded[0] for encoded in encoded_frames], dim=-1 | |
| ) # [B, n_q, T] | |
| codes = codes_.cpu().numpy()[0, :, :].T # [T, 8] | |
| return codes | |