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Add bengaliai_speech
This commit is contained in:
Yifan Yang
parent
4ab7d61008
commit
585925d81b
124
egs/bengaliai_speech/ASR/local/preprocess_bengaliai_speech.py
Executable file
124
egs/bengaliai_speech/ASR/local/preprocess_bengaliai_speech.py
Executable file
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#!/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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import re
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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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return parser.parse_args()
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def normalize_text(utt: str) -> str:
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punc = '~`!#$%^&*()_+-=|\';":/.,?><~·！@#￥%……&*（）——+-=“：’；、。，？》《{}'
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return re.sub(r"[{0}]+".format(punc), "", utt).upper()
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def preprocess_bengaliai_speech(
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dataset: Optional[str] = None,
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):
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src_dir = Path(f"data/manifests")
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output_dir = Path(f"data/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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"train",
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"valid",
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"test",
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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"bengaliai_speech"
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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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logging.info(f"Normalizing text in {partition}")
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for sup in m["supervisions"]:
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if sup.text is None:
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continue
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text = str(sup.text)
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orig_text = text
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sup.text = normalize_text(sup.text)
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text = str(sup.text)
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if len(orig_text) != len(text):
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logging.info(
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f"\nOriginal text vs normalized text:\n{orig_text}\n{text}"
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)
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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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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_bengaliai_speech(
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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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233
egs/bengaliai_speech/ASR/prepare.sh
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233
egs/bengaliai_speech/ASR/prepare.sh
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#!/usr/bin/env bash
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set -eou pipefail
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nj=32
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stage=-1
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stop_stage=100
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# Split data/set to a number of pieces
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# This is to avoid OOM during feature extraction.
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num_per_split=4000
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# We assume dl_dir (download dir) contains the following
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# directories and files. If not, they will be downloaded
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# by this script automatically.
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#
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# - $dl_dir/bengaliai_speech
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# This directory contains the following files downloaded by
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# kaggle competitions download -c bengaliai-speech
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#
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# - train_mp3s
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# - test_mp3s
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# - examples
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# - train.csv
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# - sample_submission.csv
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#
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# - $dl_dir/musan
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# This directory contains the following directories downloaded from
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# http://www.openslr.org/17/
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#
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# - music
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# - noise
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# - speech
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dl_dir=$PWD/download
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. shared/parse_options.sh || exit 1
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# vocab size for sentence piece models.
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# It will generate data/lang_bpe_xxx,
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# data/lang_bpe_yyy if the array contains xxx, yyy
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vocab_sizes=(
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# 5000
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# 2000
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# 1000
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500
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)
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# All files generated by this script are saved in "data".
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# You can safely remove "data" and rerun this script to regenerate it.
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mkdir -p data
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log() {
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# This function is from espnet
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local fname=${BASH_SOURCE[1]##*/}
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echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
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}
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log "dl_dir: $dl_dir"
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if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
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log "Stage 0: Download data"
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# If you have pre-downloaded it to /path/to/bengaliai_speech,
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# you can create a symlink
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#
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# ln -sfv /path/to/bengaliai_speech $dl_dir/bengaliai_speech
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#
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if [ ! -d $dl_dir/bengaliai/train_mp3s ]; then
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kaggle competitions download -c bengaliai-speech
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fi
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# If you have pre-downloaded it to /path/to/musan,
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# you can create a symlink
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#
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# ln -sfv /path/to/musan $dl_dir/
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#
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if [ ! -d $dl_dir/musan ]; then
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lhotse download musan $dl_dir
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fi
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fi
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if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
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log "Stage 1: Prepare Bengali.AI Speech manifest"
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# We assume that you have downloaded the Bengali.AI Speech corpus
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# to $dl_dir/bengaliai_speech
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mkdir -p data/manifests
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if [ ! -e data/manifests/.bengaliai_speech.done ]; then
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lhotse prepare bengaliai-speech -j $nj $dl_dir/bengaliai_speech data/manifests
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touch data/manifests/.bengaliai_speech.done
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fi
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fi
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if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
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log "Stage 2: Prepare musan manifest"
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# We assume that you have downloaded the musan corpus
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# to data/musan
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mkdir -p data/manifests
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if [ ! -e data/manifests/.musan.done ]; then
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lhotse prepare musan $dl_dir/musan data/manifests
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touch data/manifests/.musan.done
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fi
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fi
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if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
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log "Stage 3: Preprocess Bengali.AI Speech manifest"
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mkdir -p data/fbank
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if [ ! -e data/fbank/.preprocess_complete ]; then
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./local/preprocess_bengaliai_speech.py
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touch data/fbank/.preprocess_complete
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fi
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fi
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if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
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log "Stage 4: Compute fbank for valid and test subsets of Bengali.AI Speech"
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if [ ! -e data/fbank/.bengaliai_speech_valid_test.done ]; then
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./local/compute_fbank_bengaliai_speech_valid_test.py
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touch data/fbank/.bengaliai_speech_valid_test.done
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fi
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fi
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if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
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log "Stage 5: Split train subset into pieces"
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split_dir=data/fbank/bengaliai_speech_train_split
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if [ ! -e $split_dir/.bengaliai_speech_train_split.done ]; then
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lhotse split-lazy ./data/fbank/bengaliai_speech_cuts_train_raw.jsonl.gz $split_dir $num_per_split
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touch $split_dir/.bengaliai_speech_train_split.done
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fi
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fi
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if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
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log "Stage 6: Compute features for train subset of Bengali.AI Speech"
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if [ ! -e data/fbank/.bengaliai_speech_train.done ]; then
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./local/compute_fbank_bengaliai_speech_splits.py \
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--num-workers $nj \
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--batch-duration 600 \
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--start 0 \
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--num-splits 2000
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touch data/fbank/.bengaliai_speech_train.done
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fi
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fi
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if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
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log "Stage 7: Compute fbank for musan"
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mkdir -p data/fbank
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if [ ! -e data/fbank/.musan.done ]; then
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./local/compute_fbank_musan.py
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touch data/fbank/.musan.done
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fi
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fi
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if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
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log "Stage 8: Prepare BPE based lang"
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for vocab_size in ${vocab_sizes[@]}; do
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lang_dir=data/lang_bpe_${vocab_size}
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mkdir -p $lang_dir
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if [ ! -f $lang_dir/transcript_words.txt ]; then
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log "Generate data for BPE training"
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file=$(
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find "data/fbank/bengaliai_speech_cuts_dirty_raw.jsonl.gz"
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find "data/fbank/bengaliai_speech_cuts_dirty_sa_raw.jsonl.gz"
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find "data/fbank/bengaliai_speech_cuts_clean_raw.jsonl.gz"
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find "data/fbank/bengaliai_speech_cuts_clean_sa_raw.jsonl.gz"
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)
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gunzip -c ${file} | awk -F '"' '{print $30}' > $lang_dir/transcript_words.txt
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# Ensure space only appears once
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sed -i 's/\t/ /g' $lang_dir/transcript_words.txt
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sed -i 's/ +/ /g' $lang_dir/transcript_words.txt
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fi
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if [ ! -f $lang_dir/words.txt ]; then
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cat $lang_dir/transcript_words.txt | sed 's/ /\n/g' \
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| sort -u | sed '/^$/d' > $lang_dir/words.txt
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(echo '!SIL'; echo '<SPOKEN_NOISE>'; echo '<UNK>'; ) |
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cat - $lang_dir/words.txt | sort | uniq | awk '
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BEGIN {
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print "<eps> 0";
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}
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{
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if ($1 == "<s>") {
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print "<s> is in the vocabulary!" | "cat 1>&2"
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exit 1;
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}
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if ($1 == "</s>") {
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print "</s> is in the vocabulary!" | "cat 1>&2"
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exit 1;
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}
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printf("%s %d\n", $1, NR);
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}
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END {
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printf("#0 %d\n", NR+1);
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printf("<s> %d\n", NR+2);
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printf("</s> %d\n", NR+3);
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}' > $lang_dir/words || exit 1;
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mv $lang_dir/words $lang_dir/words.txt
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fi
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if [ ! -f $lang_dir/bpe.model ]; then
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./local/train_bpe_model.py \
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--lang-dir $lang_dir \
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--vocab-size $vocab_size \
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--transcript $lang_dir/transcript_words.txt
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fi
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if [ ! -f $lang_dir/L_disambig.pt ]; then
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./local/prepare_lang_bpe.py --lang-dir $lang_dir
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log "Validating $lang_dir/lexicon.txt"
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./local/validate_bpe_lexicon.py \
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--lexicon $lang_dir/lexicon.txt \
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--bpe-model $lang_dir/bpe.model
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fi
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if [ ! -f $lang_dir/L.fst ]; then
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log "Converting L.pt to L.fst"
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./shared/convert-k2-to-openfst.py \
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--olabels aux_labels \
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$lang_dir/L.pt \
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$lang_dir/L.fst
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fi
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if [ ! -f $lang_dir/L_disambig.fst ]; then
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log "Converting L_disambig.pt to L_disambig.fst"
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./shared/convert-k2-to-openfst.py \
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--olabels aux_labels \
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$lang_dir/L_disambig.pt \
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$lang_dir/L_disambig.fst
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fi
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done
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fi
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1
egs/bengaliai_speech/ASR/shared
Symbolic link
1
egs/bengaliai_speech/ASR/shared
Symbolic link
@ -0,0 +1 @@
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../../../icefall/shared
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