mirror of
https://github.com/k2-fsa/icefall.git
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138 lines
4.1 KiB
Python
138 lines
4.1 KiB
Python
#!/usr/bin/env python3
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# Copyright 2024 Xiaomi Corp. (authors: Xiaoyu Yang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This file computes fbank features of the LibriSpeech dataset.
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It looks for manifests in the directory data/manifests.
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The generated fbank features are saved in data/fbank.
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"""
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import argparse
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import logging
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import os
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from pathlib import Path
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from typing import Optional
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import sentencepiece as spm
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import torch
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from filter_cuts import filter_cuts
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from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter
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from lhotse.recipes.utils import read_manifests_if_cached
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from icefall.utils import get_executor, str2bool
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# Torch's multithreaded behavior needs to be disabled or
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# it wastes a lot of CPU and slow things down.
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# Do this outside of main() in case it needs to take effect
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# even when we are not invoking the main (e.g. when spawning subprocesses).
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torch.set_num_threads(1)
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torch.set_num_interop_threads(1)
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--manifest-dir",
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type=str,
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default="data/manifests",
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)
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parser.add_argument(
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"--fbank-dir",
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type=str,
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default="data/fbank_mls",
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)
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parser.add_argument(
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"--part",
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type=str,
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help="Which language to prepare, if all, prepare all languages",
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choices=["english", "dutch", "german", "spanish", "french", "italian", "polish", "portuguese", "all"]
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)
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return parser.parse_args()
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def compute_fbank_mls(
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manifest_dir=str,
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fbank_dir=str,
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part=str,
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):
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src_dir = Path("data/manifests")
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output_dir = Path(fbank_dir)
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num_jobs = min(15, os.cpu_count())
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num_mel_bins = 80
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if part == "all":
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dataset_parts = [
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"english",
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"dutch",
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"german",
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"spanish"
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]
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else:
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dataset_parts = [part]
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splits = ["train", "test", "dev"]
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num_jobs = 15
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num_mel_bins = 80
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extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
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for language in dataset_parts:
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for split in splits:
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recording_file = src_dir / f"mls-{language}_recordings_{split}.jsonl.gz"
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supervision_file = src_dir / f"mls-{language}_supervisions_{split}.jsonl.gz"
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recordings = CutSet.from_file(recording_file)
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supervisions = CutSet.from_file(supervision_file)
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cut_set = CutSet.from_manifests(
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recordings=recordings,
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supervisions=supervisions,
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)
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prefix = f"mls-{language}"
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with get_executor() as ex:
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cut_set = cut_set.compute_and_store_features(
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extractor=extractor,
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storage_path=f"{output_dir}/{prefix}_feats_{split}",
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# when an executor is specified, make more partitions
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num_jobs=num_jobs if ex is None else 80,
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executor=ex,
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storage_type=LilcomChunkyWriter,
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)
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cuts_filename = output_dir / f"mls-{language}_{split}.jsonl.gz"
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logging.info(f"Saving to {cuts_filename}")
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cut_set.to_file(cuts_filename)
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if __name__ == "__main__":
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formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
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logging.basicConfig(format=formatter, level=logging.INFO)
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args = get_args()
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logging.info(vars(args))
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compute_fbank_mls(
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manifest_dir=args.manifest_dir,
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fbank_dir=args.fbank_dir,
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part=args.part,
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)
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