#!/usr/bin/env python3 # Copyright 2022 Xiaomi Corp. (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 logging from pathlib import Path from lhotse import CutSet from lhotse.recipes.utils import read_manifests_if_cached def preprocess_aidatatang_200zh(): src_dir = Path("data/manifests/aidatatang_200zh") output_dir = Path("data/fbank/aidatatang_200zh") output_dir.mkdir(exist_ok=True, parents=True) dataset_parts = ( "train", "test", "dev", ) logging.info("Loading manifest") manifests = read_manifests_if_cached( dataset_parts=dataset_parts, output_dir=src_dir, ) assert len(manifests) > 0 for partition, m in manifests.items(): logging.info(f"Processing {partition}") raw_cuts_path = output_dir / f"cuts_{partition}_raw.jsonl.gz" if raw_cuts_path.is_file(): logging.info(f"{partition} already exists - skipping") continue for sup in m["supervisions"]: sup.custom = {"origin": "aidatatang_200zh"} cut_set = CutSet.from_manifests( recordings=m["recordings"], supervisions=m["supervisions"], ) logging.info(f"Saving to {raw_cuts_path}") cut_set.to_file(raw_cuts_path) def main(): formatter = ( "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s" ) logging.basicConfig(format=formatter, level=logging.INFO) preprocess_aidatatang_200zh() if __name__ == "__main__": main()