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| """ | |
| Modified HuBERT model without kmeans. | |
| Original author: https://github.com/lucidrains/ | |
| Modified by: https://www.github.com/gitmylo/ | |
| License: MIT | |
| """ | |
| # Modified code from https://github.com/lucidrains/audiolm-pytorch/blob/main/audiolm_pytorch/hubert_kmeans.py | |
| from pathlib import Path | |
| import torch | |
| from torch import nn | |
| from einops import pack, unpack | |
| import fairseq | |
| from torchaudio.functional import resample | |
| from audiolm_pytorch.utils import curtail_to_multiple | |
| import logging | |
| logging.root.setLevel(logging.ERROR) | |
| def exists(val): | |
| return val is not None | |
| def default(val, d): | |
| return val if exists(val) else d | |
| class CustomHubert(nn.Module): | |
| """ | |
| checkpoint and kmeans can be downloaded at https://github.com/facebookresearch/fairseq/tree/main/examples/hubert | |
| or you can train your own | |
| """ | |
| def __init__( | |
| self, | |
| checkpoint_path, | |
| target_sample_hz=16000, | |
| seq_len_multiple_of=None, | |
| output_layer=9, | |
| device=None | |
| ): | |
| super().__init__() | |
| self.target_sample_hz = target_sample_hz | |
| self.seq_len_multiple_of = seq_len_multiple_of | |
| self.output_layer = output_layer | |
| if device is not None: | |
| self.to(device) | |
| model_path = Path(checkpoint_path) | |
| assert model_path.exists(), f'path {checkpoint_path} does not exist' | |
| print(f"Loading Hubert {checkpoint_path}") | |
| checkpoint = torch.load(checkpoint_path) | |
| load_model_input = {checkpoint_path: checkpoint} | |
| model, *_ = fairseq.checkpoint_utils.load_model_ensemble_and_task(load_model_input) | |
| if device is not None: | |
| model[0].to(device) | |
| self.model = model[0] | |
| self.model.eval() | |
| def groups(self): | |
| return 1 | |
| def forward( | |
| self, | |
| wav_input, | |
| flatten=True, | |
| input_sample_hz=None | |
| ): | |
| device = wav_input.device | |
| if exists(input_sample_hz): | |
| wav_input = resample(wav_input, input_sample_hz, self.target_sample_hz) | |
| if exists(self.seq_len_multiple_of): | |
| wav_input = curtail_to_multiple(wav_input, self.seq_len_multiple_of) | |
| embed = self.model( | |
| wav_input, | |
| features_only=True, | |
| mask=False, # thanks to @maitycyrus for noticing that mask is defaulted to True in the fairseq code | |
| output_layer=self.output_layer | |
| ) | |
| embed, packed_shape = pack([embed['x']], '* d') | |
| # codebook_indices = self.kmeans.predict(embed.cpu().detach().numpy()) | |
| codebook_indices = torch.from_numpy(embed.cpu().detach().numpy()).to(device) # .long() | |
| if flatten: | |
| return codebook_indices | |
| codebook_indices, = unpack(codebook_indices, packed_shape, '*') | |
| return codebook_indices | |