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import csv |
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import io |
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import logging |
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import math |
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import os |
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import os.path as op |
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import sys |
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import tqdm |
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from dump_hubert_feature import HubertFeatureReader |
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from fairseq.data.audio.audio_utils import get_waveform |
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from fairseq.data.audio.speech_to_text_dataset import ( |
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read_from_uncompressed_zip, |
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) |
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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_hubert_feature_s2t") |
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class HubertFeatureReaderS2T(HubertFeatureReader): |
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def read_audio(self, path, ref_len=None): |
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path, *extra = path.split(":") |
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assert len(extra) == 2 |
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assert path.endswith(".zip") |
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data = read_from_uncompressed_zip(path, int(extra[0]), int(extra[1])) |
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f = io.BytesIO(data) |
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wav, sr = get_waveform(f) |
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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_path_iterator(root, tsv, nshard, rank): |
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with open(tsv) as f: |
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reader = csv.DictReader( |
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f, |
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delimiter="\t", |
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quotechar=None, |
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doublequote=False, |
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lineterminator="\n", |
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quoting=csv.QUOTE_NONE, |
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) |
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subpaths = [op.join(root, e["audio"]) for e in reader] |
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tot = len(subpaths) |
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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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subpaths = subpaths[start:end] |
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def iterate(): |
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for subpath in subpaths: |
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yield op.join(root, subpath) |
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return iterate, len(subpaths) |
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def dump_feature( |
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root, |
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tsv_path, |
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ckpt_path, |
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layer, |
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nshard, |
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rank, |
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feat_dir, |
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feat_name, |
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max_chunk, |
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): |
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reader = HubertFeatureReaderS2T(ckpt_path, layer, max_chunk) |
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generator, num = get_path_iterator(root, tsv_path, nshard, rank) |
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iterator = generator() |
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feat_path = f"{feat_dir}/{feat_name}_{rank}_{nshard}.npy" |
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leng_path = f"{feat_dir}/{feat_name}_{rank}_{nshard}.len" |
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os.makedirs(feat_dir, exist_ok=True) |
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if op.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 in tqdm.tqdm(iterator, total=num): |
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feat = reader.get_feats(path) |
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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("root") |
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parser.add_argument("tsv_path") |
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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("feat_name") |
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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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dump_feature(**vars(args)) |
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