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import logging
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import os
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import sys
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import fairseq
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import soundfile as sf
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import torch
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import torch.nn.functional as F
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from feature_utils import get_path_iterator, dump_feature
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logging.basicConfig(
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format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
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datefmt="%Y-%m-%d %H:%M:%S",
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level=os.environ.get("LOGLEVEL", "INFO").upper(),
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stream=sys.stdout,
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)
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logger = logging.getLogger("dump_w2v2_feature")
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class Wav2Vec2FeatureReader(object):
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def __init__(self, ckpt_path, layer, max_chunk=1600000):
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(
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model,
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cfg,
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task,
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) = fairseq.checkpoint_utils.load_model_ensemble_and_task([ckpt_path])
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self.model = model[0].eval().cuda()
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self.task = task
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self.layer = layer
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self.max_chunk = max_chunk
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logger.info(f"TASK CONFIG:\n{self.task.cfg}")
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logger.info(f" max_chunk = {self.max_chunk}")
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logger.info(f" model:\n{self.model}")
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def read_audio(self, path, ref_len=None):
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wav, sr = sf.read(path)
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assert sr == self.task.cfg.sample_rate, sr
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if wav.ndim == 2:
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wav = wav.mean(-1)
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assert wav.ndim == 1, wav.ndim
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if ref_len is not None and abs(ref_len - len(wav)) > 160:
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logging.warning(f"ref {ref_len} != read {len(wav)} ({path})")
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return wav
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def get_feats(self, path, ref_len=None):
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x = self.read_audio(path, ref_len)
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with torch.no_grad():
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x = torch.from_numpy(x).float().cuda()
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if self.task.cfg.normalize:
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x = F.layer_norm(x, x.shape)
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x = x.view(1, -1)
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feat = []
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for start in range(0, x.size(1), self.max_chunk):
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x_chunk = x[:, start: start + self.max_chunk]
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res = self.model.extract_features(
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source=x_chunk,
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padding_mask=None,
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mask=False,
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)
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feat_chunk = res["x"]
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feat.append(feat_chunk)
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return torch.cat(feat, 1).squeeze(0)
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def main(tsv_dir, split, ckpt_path, layer, nshard, rank, feat_dir, max_chunk):
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reader = Wav2Vec2FeatureReader(ckpt_path, layer, max_chunk)
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generator, num = get_path_iterator(f"{tsv_dir}/{split}.tsv", nshard, rank)
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dump_feature(reader, generator, num, split, nshard, rank, feat_dir)
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("tsv_dir")
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parser.add_argument("split")
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parser.add_argument("ckpt_path")
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parser.add_argument("layer", type=int)
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parser.add_argument("nshard", type=int)
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parser.add_argument("rank", type=int)
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parser.add_argument("feat_dir")
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parser.add_argument("--max_chunk", type=int, default=1600000)
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args = parser.parse_args()
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logger.info(args)
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main(**vars(args))
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