#!/usr/bin/env python3 # Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang) # # 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 musan 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 import torch from lhotse import ( CutSet, Fbank, FbankConfig, LilcomChunkyWriter, MonoCut, WhisperFbank, WhisperFbankConfig, combine, ) 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 is_cut_long(c: MonoCut) -> bool: return c.duration > 5 def compute_fbank_musan( num_mel_bins: int = 80, whisper_fbank: bool = False, output_dir: str = "data/fbank" ): src_dir = Path("data/manifests") output_dir = Path(output_dir) num_jobs = min(15, os.cpu_count()) dataset_parts = ( "music", "speech", "noise", ) prefix = "musan" suffix = "jsonl.gz" manifests = read_manifests_if_cached( dataset_parts=dataset_parts, output_dir=src_dir, prefix=prefix, suffix=suffix, ) assert manifests is not None assert len(manifests) == len(dataset_parts), ( len(manifests), len(dataset_parts), list(manifests.keys()), dataset_parts, ) musan_cuts_path = output_dir / "musan_cuts.jsonl.gz" if musan_cuts_path.is_file(): logging.info(f"{musan_cuts_path} already exists - skipping") return logging.info("Extracting features for Musan") if whisper_fbank: extractor = WhisperFbank( WhisperFbankConfig(num_filters=num_mel_bins, device="cuda") ) else: extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins)) with get_executor() as ex: # Initialize the executor only once. # create chunks of Musan with duration 5 - 10 seconds musan_cuts = ( CutSet.from_manifests( recordings=combine(part["recordings"] for part in manifests.values()) ) .cut_into_windows(10.0) .filter(is_cut_long) .compute_and_store_features( extractor=extractor, storage_path=f"{output_dir}/musan_feats", num_jobs=num_jobs if ex is None else 80, executor=ex, storage_type=LilcomChunkyWriter, ) ) musan_cuts.to_file(musan_cuts_path) def get_args(): parser = argparse.ArgumentParser() parser.add_argument( "--num-mel-bins", type=int, default=80, help="""The number of mel bins for Fbank""", ) parser.add_argument( "--whisper-fbank", type=str2bool, default=False, help="Use WhisperFbank instead of Fbank. Default: False.", ) parser.add_argument( "--output-dir", type=str, default="data/fbank", help="Output directory. Default: data/fbank.", ) return parser.parse_args() if __name__ == "__main__": formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" logging.basicConfig(format=formatter, level=logging.INFO) args = get_args() compute_fbank_musan( num_mel_bins=args.num_mel_bins, whisper_fbank=args.whisper_fbank, output_dir=args.output_dir, )