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130 lines
4.1 KiB
Python
130 lines
4.1 KiB
Python
#!/usr/bin/env python3
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# Copyright 2021 Johns Hopkins University (Piotr Żelasko)
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# Copyright 2021 Xiaomi Corp. (Fangjun Kuang)
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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 logging
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import re
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from pathlib import Path
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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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# Similar text filtering and normalization procedure as in:
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# https://github.com/SpeechColab/GigaSpeech/blob/main/toolkits/kaldi/gigaspeech_data_prep.sh
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def normalize_text(
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utt: str,
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punct_pattern=re.compile(r"<(COMMA|PERIOD|QUESTIONMARK|EXCLAMATIONPOINT)>"),
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whitespace_pattern=re.compile(r"\s\s+"),
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) -> str:
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return whitespace_pattern.sub(" ", punct_pattern.sub("", utt))
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def has_no_oov(
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sup: SupervisionSegment,
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oov_pattern=re.compile(r"<(SIL|MUSIC|NOISE|OTHER)>"),
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) -> bool:
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return oov_pattern.search(sup.text) is None
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def preprocess_giga_speech():
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src_dir = Path("data/manifests")
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output_dir = Path("data/fbank")
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output_dir.mkdir(exist_ok=True)
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dataset_parts = (
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"DEV",
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"TEST",
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"XS",
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"S",
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"M",
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"L",
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"XL",
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)
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logging.info("Loading manifest (may take 4 minutes)")
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prefix = "gigaspeech"
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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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prefix=prefix,
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suffix=suffix,
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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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# Note this step makes the recipe different than LibriSpeech:
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# We must filter out some utterances and remove punctuation
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# to be consistent with Kaldi.
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logging.info("Filtering OOV utterances from supervisions")
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m["supervisions"] = m["supervisions"].filter(has_no_oov)
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logging.info(f"Normalizing text in {partition}")
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for sup in m["supervisions"]:
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sup.text = normalize_text(sup.text)
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sup.custom = {"origin": "giga"}
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# Create long-recording cut manifests.
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logging.info(f"Processing {partition}")
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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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)
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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 partition not in ["DEV", "TEST"]:
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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 8 minutes and saving may"
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# " take 20 minutes)"
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# )
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# cut_set = (
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# cut_set
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# + cut_set.perturb_speed(0.9)
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# + cut_set.perturb_speed(1.1)
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# )
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#
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# Note: No need to perturb the training subset as not all of the
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# data is going to be used in the training.
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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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preprocess_giga_speech()
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if __name__ == "__main__":
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main()
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