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