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Running on Zero
| # Copyright 2024 Yiwei Guo | |
| # Licensed under Apache 2.0 | |
| # This file is a direct copy from vec2wav2.0 (https://github.com/cantabile-kwok/vec2wav2.0) | |
| """Extract VQ indexes using vq-wav2vec model (from fairseq)""" | |
| import torch | |
| import logging | |
| from kaldiio import WriteHelper | |
| import os | |
| import fairseq | |
| import argparse | |
| import numpy as np | |
| from pathlib import Path | |
| import soundfile as sf | |
| from tqdm import tqdm | |
| from vec2wav2.utils.utils import read_wav_16k | |
| logging.basicConfig(level=logging.INFO, format='%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s') | |
| class Extractor: | |
| def __init__(self, checkpoint="pretrained/vq-wav2vec_kmeans.pt", device="cuda"): | |
| self.device = device | |
| self.model, self.cfg, self.task = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint]) | |
| self.model = self.model[0].to(device) | |
| self.model.eval() | |
| for p in self.model.parameters(): | |
| p.requires_grad_(False) | |
| def extract(self, wav: np.ndarray) -> torch.Tensor: | |
| with torch.no_grad(): | |
| audio = torch.from_numpy(wav).float().unsqueeze(0).to(self.device) | |
| z = self.model.feature_extractor(audio) | |
| _, idxs = self.model.vector_quantizer.forward_idx(z) | |
| return idxs[0].cpu() # [L, Groups] | |
| def get_codebook(self) -> np.ndarray: | |
| quantizer = self.model.vector_quantizer | |
| if self.cfg.model.vq_type == "kmeans": | |
| codebook = quantizer.expand_embedding.data.transpose(0,1).contiguous() | |
| elif self.cfg.model.vq_type == "gumbel": | |
| codebook = quantizer.vars.data | |
| if quantizer.combine_groups: | |
| codebook = codebook.repeat(1, quantizer.groups, 1) | |
| codebook = codebook.view(quantizer.groups, quantizer.num_vars, -1) | |
| codebook = codebook.cpu().numpy() | |
| return codebook | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--wav-scp', type=str) | |
| parser.add_argument("--out-dir", type=str) | |
| parser.add_argument('--model', default="pretrained/vq-wav2vec_kmeans.pt", type=str) | |
| args = parser.parse_args() | |
| extractor = Extractor(checkpoint=args.model, device="cuda" if torch.cuda.is_available() else "cpu") | |
| out_dir=Path(args.out_dir).absolute() | |
| with open(args.wav_scp, 'r') as f, torch.no_grad(), WriteHelper(f"ark,scp:{out_dir}/feats.ark,{out_dir}/feats.scp") as writer: | |
| for line in tqdm(f.readlines()): | |
| uttid, wav_path = line.strip().split(maxsplit=1) | |
| logging.info("Extracting " + uttid) | |
| audio = read_wav_16k(wav_path) | |
| idxs = extractor.extract(audio).cpu().numpy() | |
| idxs = idxs.astype(float) | |
| writer(uttid, idxs) | |