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120 lines
3.4 KiB
Python
Executable File
120 lines
3.4 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2023 Xiaomi Corp. (authors: Yifan Yang)
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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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import argparse
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import logging
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from pathlib import Path
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from typing import Optional
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from lhotse import CutSet, SupervisionSegment
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from lhotse.recipes.utils import read_manifests_if_cached
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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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"--dataset",
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type=str,
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help="""Dataset parts to compute fbank. If None, we will use all""",
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)
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parser.add_argument(
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"--language",
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type=str,
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help="""Language of Common Voice""",
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)
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return parser.parse_args()
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def preprocess_commonvoice(
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language: str,
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dataset: Optional[str] = None,
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):
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src_dir = Path(f"data/{language}/manifests")
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output_dir = Path(f"data/{language}/fbank")
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output_dir.mkdir(exist_ok=True)
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if dataset is None:
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dataset_parts = (
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"dev",
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"test",
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"train",
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)
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else:
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dataset_parts = dataset.split(" ", -1)
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logging.info("Loading manifest")
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prefix = f"cv-{language}"
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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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suffix=suffix,
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prefix=prefix,
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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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for partition, m in manifests.items():
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logging.info(f"Processing {partition}")
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raw_cuts_path = output_dir / f"{prefix}_cuts_{partition}_raw.{suffix}"
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if raw_cuts_path.is_file():
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logging.info(f"{partition} already exists - skipping")
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continue
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# Create long-recording cut manifests.
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cut_set = CutSet.from_manifests(
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recordings=m["recordings"],
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supervisions=m["supervisions"],
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).resample(16000)
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# Run data augmentation that needs to be done in the
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# time domain.
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if "train" in partition:
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logging.info(
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f"Speed perturb for {partition} with factors 0.9 and 1.1 "
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"(Perturbing may take 2 minutes and saving may take 7 minutes)"
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)
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cut_set = cut_set + cut_set.perturb_speed(0.9) + cut_set.perturb_speed(1.1)
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logging.info(f"Saving to {raw_cuts_path}")
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cut_set.to_file(raw_cuts_path)
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def 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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logging.info(vars(args))
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preprocess_commonvoice(
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language=args.language,
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dataset=args.dataset,
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)
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logging.info("Done")
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if __name__ == "__main__":
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main()
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