mirror of
https://github.com/k2-fsa/icefall.git
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1c30847947
* add decode seamlessm4t * add requirements * add decoding with avg model * add token files * add custom tokenizer * support deepspeed to finetune large model * support large-v3 * add model saving * using monkey patch to replace models * add manifest dir option
153 lines
4.3 KiB
Python
Executable File
153 lines
4.3 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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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 musan 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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import torch
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from lhotse import (
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CutSet,
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Fbank,
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FbankConfig,
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LilcomChunkyWriter,
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MonoCut,
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WhisperFbank,
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WhisperFbankConfig,
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combine,
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)
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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 is_cut_long(c: MonoCut) -> bool:
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return c.duration > 5
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def compute_fbank_musan(
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num_mel_bins: int = 80, whisper_fbank: bool = False, output_dir: str = "data/fbank"
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):
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src_dir = Path("data/manifests")
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output_dir = Path(output_dir)
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num_jobs = min(15, os.cpu_count())
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dataset_parts = (
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"music",
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"speech",
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"noise",
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)
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prefix = "musan"
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suffix = "jsonl.gz"
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manifests = read_manifests_if_cached(
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dataset_parts=dataset_parts,
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output_dir=src_dir,
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prefix=prefix,
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suffix=suffix,
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)
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assert manifests is not None
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assert len(manifests) == len(dataset_parts), (
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len(manifests),
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len(dataset_parts),
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list(manifests.keys()),
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dataset_parts,
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)
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musan_cuts_path = output_dir / "musan_cuts.jsonl.gz"
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if musan_cuts_path.is_file():
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logging.info(f"{musan_cuts_path} already exists - skipping")
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return
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logging.info("Extracting features for Musan")
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if whisper_fbank:
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extractor = WhisperFbank(
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WhisperFbankConfig(num_filters=num_mel_bins, device="cuda")
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)
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else:
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extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
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with get_executor() as ex: # Initialize the executor only once.
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# create chunks of Musan with duration 5 - 10 seconds
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musan_cuts = (
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CutSet.from_manifests(
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recordings=combine(part["recordings"] for part in manifests.values())
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)
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.cut_into_windows(10.0)
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.filter(is_cut_long)
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.compute_and_store_features(
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extractor=extractor,
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storage_path=f"{output_dir}/musan_feats",
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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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)
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musan_cuts.to_file(musan_cuts_path)
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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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"--num-mel-bins",
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type=int,
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default=80,
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help="""The number of mel bins for Fbank""",
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)
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parser.add_argument(
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"--whisper-fbank",
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type=str2bool,
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default=False,
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help="Use WhisperFbank instead of Fbank. Default: False.",
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)
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parser.add_argument(
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"--output-dir",
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type=str,
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default="data/fbank",
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help="Output directory. Default: data/fbank.",
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)
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return parser.parse_args()
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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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compute_fbank_musan(
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num_mel_bins=args.num_mel_bins,
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whisper_fbank=args.whisper_fbank,
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output_dir=args.output_dir,
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)
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