#!/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. import argparse import logging import os from pathlib import Path from typing import Optional import torch from lhotse import CutSet from lhotse.recipes.utils import read_manifests_if_cached from icefall.utils import 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( "--dataset", type=str, help="""Dataset parts to compute fbank. If None, we will use all""", ) return parser.parse_args() def process_kmeans_librispeech( dataset: Optional[str] = None, ): src_dir = Path(".") output_dir = Path(".") if dataset is None: dataset_parts = ( "dev-clean", "train-clean-100", "train-clean-360", "train-other-500", ) else: dataset_parts = dataset.split(" ", -1) prefix = "librispeech" suffix = "jsonl" 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, ) for partition, m in manifests.items(): cuts_filename = f"{prefix}_cuts_{partition}_raw.{suffix}" if (output_dir / cuts_filename).is_file(): logging.info(f"{partition} already exists - skipping.") continue logging.info(f"Processing {partition}") cut_set = CutSet.from_manifests( recordings=m["recordings"], supervisions=m["supervisions"], ) cut_set.to_file(output_dir / 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)) process_kmeans_librispeech( dataset=args.dataset, )