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| #!/usr/bin/env python3 | |
| # Copyright 2023 (authors: Feiteng Li) | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import re | |
| from dataclasses import asdict, dataclass | |
| from typing import Any, Dict, List, Optional, Pattern, Union | |
| import numpy as np | |
| import torch | |
| import torchaudio | |
| from encodec import EncodecModel | |
| from encodec.utils import convert_audio | |
| 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) | |
| class AudioTokenizer: | |
| """EnCodec audio.""" | |
| def __init__( | |
| self, | |
| device: Any = None, | |
| ) -> None: | |
| # Instantiate a pretrained EnCodec model | |
| 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: | |
| return self.codec.encode(wav.to(self.device)) | |
| def decode(self, frames: torch.Tensor) -> torch.Tensor: | |
| return self.codec.decode(frames) | |
| def tokenize_audio(tokenizer: AudioTokenizer, audio): | |
| # Load and pre-process the audio waveform | |
| if isinstance(audio, str): | |
| wav, sr = torchaudio.load(audio) | |
| else: | |
| wav, sr = audio | |
| 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 | |
| if __name__ == "__main__": | |
| model = EncodecModel.encodec_model_24khz() | |
| model.set_target_bandwidth(6.0) | |
| samples = torch.from_numpy(np.random.random([4, 1, 1600])).type( | |
| torch.float32 | |
| ) | |
| codes_raw = model.encode(samples) | |
| remove_encodec_weight_norm(model) | |
| codes_norm = model.encode(samples) | |
| assert torch.allclose(codes_raw[0][0], codes_norm[0][0]) | |