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import logging |
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import math |
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import os |
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import sys |
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import soundfile as sf |
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import torch |
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import torchaudio |
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import tqdm |
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from npy_append_array import NpyAppendArray |
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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_mfcc_feature") |
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class MfccFeatureReader(object): |
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def __init__(self, sample_rate): |
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self.sample_rate = sample_rate |
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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.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() |
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x = x.view(1, -1) |
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mfccs = torchaudio.compliance.kaldi.mfcc( |
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waveform=x, |
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sample_frequency=self.sample_rate, |
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use_energy=False, |
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) |
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mfccs = mfccs.transpose(0, 1) |
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deltas = torchaudio.functional.compute_deltas(mfccs) |
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ddeltas = torchaudio.functional.compute_deltas(deltas) |
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concat = torch.cat([mfccs, deltas, ddeltas], dim=0) |
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concat = concat.transpose(0, 1).contiguous() |
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return concat |
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def get_path_iterator(tsv, nshard, rank): |
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with open(tsv, "r") as f: |
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root = f.readline().rstrip() |
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lines = [line.rstrip() for line in f] |
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tot = len(lines) |
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shard_size = math.ceil(tot / nshard) |
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start, end = rank * shard_size, min((rank + 1) * shard_size, tot) |
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assert start < end, "start={start}, end={end}" |
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logger.info( |
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f"rank {rank} of {nshard}, process {end-start} " |
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f"({start}-{end}) out of {tot}" |
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) |
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lines = lines[start:end] |
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def iterate(): |
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for line in lines: |
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subpath, nsample = line.split("\t") |
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yield f"{root}/{subpath}", int(nsample) |
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return iterate, len(lines) |
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def dump_feature(tsv_dir, split, sample_rate, nshard, rank, feat_dir): |
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reader = MfccFeatureReader(sample_rate) |
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generator, num = get_path_iterator(f"{tsv_dir}/{split}.tsv", nshard, rank) |
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iterator = generator() |
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feat_path = f"{feat_dir}/{split}_{rank}_{nshard}.npy" |
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leng_path = f"{feat_dir}/{split}_{rank}_{nshard}.len" |
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os.makedirs(feat_dir, exist_ok=True) |
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if os.path.exists(feat_path): |
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os.remove(feat_path) |
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feat_f = NpyAppendArray(feat_path) |
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with open(leng_path, "w") as leng_f: |
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for path, nsample in tqdm.tqdm(iterator, total=num): |
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feat = reader.get_feats(path, nsample) |
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feat_f.append(feat.cpu().numpy()) |
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leng_f.write(f"{len(feat)}\n") |
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logger.info("finished successfully") |
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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("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("--sample_rate", type=int, default=16000) |
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args = parser.parse_args() |
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logger.info(args) |
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dump_feature(**vars(args)) |
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