support download, data prep, and fbank

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wgb14 2021-11-12 14:43:19 -05:00
parent b7bda9eaf6
commit 75860159a2
3 changed files with 334 additions and 0 deletions

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#!/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.
"""
This file computes fbank features of the GigaSpeech dataset.
It looks for manifests in the directory data/manifests.
The generated fbank features are saved in data/fbank.
"""
import logging
import os
from pathlib import Path
import torch
from lhotse import CutSet, Fbank, FbankConfig, LilcomHdf5Writer
from lhotse.recipes.utils import read_manifests_if_cached
from icefall.utils import get_executor
# 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 compute_fbank_gigaspeech():
src_dir = Path("data/manifests")
output_dir = Path("data/fbank")
num_jobs = min(10, os.cpu_count())
num_mel_bins = 80
dataset_parts = (
"XL",
"DEV",
"TEST",
)
manifests = read_manifests_if_cached(
dataset_parts=dataset_parts,
output_dir=src_dir,
prefix="gigaspeech",
suffix="jsonl.gz",
)
assert manifests is not None
extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
with get_executor() as ex: # Initialize the executor only once.
for partition, m in manifests.items():
if (output_dir / f"cuts_{partition}.jsonl.gz").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"],
)
if "train" in partition:
cut_set = (
cut_set
+ cut_set.perturb_speed(0.9)
+ cut_set.perturb_speed(1.1)
)
cut_set = cut_set.compute_and_store_features(
extractor=extractor,
storage_path=f"{output_dir}/feats_{partition}",
# when an executor is specified, make more partitions
num_jobs=num_jobs if ex is None else 80,
executor=ex,
storage_type=LilcomHdf5Writer,
)
cut_set.to_json(output_dir / f"cuts_{partition}.jsonl.gz")
if __name__ == "__main__":
formatter = (
"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
)
logging.basicConfig(format=formatter, level=logging.INFO)
compute_fbank_gigaspeech()

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../../../librispeech/ASR/local/compute_fbank_musan.py

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#!/usr/bin/env bash
set -eou pipefail
nj=15
stage=0
stop_stage=100
# We assume dl_dir (download dir) contains the following
# directories and files. If not, they will be downloaded
# by this script automatically.
#
# - $dl_dir/GigaSpeech
# You can find audio, dict, GigaSpeech.json inside it.
# You can apply for the download credentials by following
# https://github.com/SpeechColab/GigaSpeech#dataset-download
#
# - $dl_dir/musan
# This directory contains the following directories downloaded from
# http://www.openslr.org/17/
#
# - music
# - noise
# - speech
dl_dir=$PWD/download
. shared/parse_options.sh || exit 1
# vocab size for sentence piece models.
# It will generate data/lang_bpe_xxx,
# data/lang_bpe_yyy if the array contains xxx, yyy
vocab_sizes=(
5000
2000
1000
500
)
# All files generated by this script are saved in "data".
# You can safely remove "data" and rerun this script to regenerate it.
mkdir -p data
log() {
# This function is from espnet
local fname=${BASH_SOURCE[1]##*/}
echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
}
log "dl_dir: $dl_dir"
if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
log "Stage 0: Download data"
[ ! -e $dl_dir/GigaSpeech ] && mkdir -p $dl_dir/GigaSpeech
# If you have pre-downloaded it to /path/to/GigaSpeech,
# you can create a symlink
#
# ln -sfv /path/to/GigaSpeech $dl_dir/GigaSpeech
#
if [ ! -d $dl_dir/GigaSpeech/audio ] && [ ! -f $dl_dir/GigaSpeech.json ]; then
# Check credentials.
if [ ! -f $dl_dir/password ]; then
echo -n "$0: Please apply for the download credentials by following"
echo -n "https://github.com/SpeechColab/GigaSpeech#dataset-download"
echo " and save it to $dl_dir/password."
exit 1;
fi
PASSWORD=`cat $dl_dir/password 2>/dev/null`
if [ -z "$PASSWORD" ]; then
echo "$0: Error, $dl_dir/password is empty."
exit 1;
fi
PASSWORD_MD5=`echo $PASSWORD | md5sum | cut -d ' ' -f 1`
if [[ $PASSWORD_MD5 != "dfbf0cde1a3ce23749d8d81e492741b8" ]]; then
echo "$0: Error, invalid $dl_dir/password."
exit 1;
fi
lhotse download gigaspeech --subset auto --host tsinghua \
$dl_dir/password $dl_dir/GigaSpeech
fi
# If you have pre-downloaded it to /path/to/musan,
# you can create a symlink
#
# ln -sfv /path/to/musan $dl_dir/
#
if [ ! -d $dl_dir/musan ]; then
lhotse download musan $dl_dir
fi
fi
if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
log "Stage 1: Prepare GigaSpeech manifest"
# We assume that you have downloaded the GigaSpeech corpus
# to $dl_dir/GigaSpeech
mkdir -p data/manifests
lhotse prepare gigaspeech --subset auto -j $nj \
$dl_dir/GigaSpeech data/manifests
fi
if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
log "Stage 2: Prepare musan manifest"
# We assume that you have downloaded the musan corpus
# to $dl_dir/musan
mkdir -p data/manifests
lhotse prepare musan $dl_dir/musan data/manifests
fi
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
log "Stage 3: Compute fbank for GigaSpeech"
mkdir -p data/fbank
./local/compute_fbank_gigaspeech.py
fi
if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
log "Stage 4: Compute fbank for musan"
mkdir -p data/fbank
./local/compute_fbank_musan.py
fi
if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
log "Stage 5: Prepare phone based lang"
lang_dir=data/lang_phone
mkdir -p $lang_dir
(echo '!SIL SIL'; echo '<SPOKEN_NOISE> SPN'; echo '<UNK> SPN'; ) |
cat - $dl_dir/lm/librispeech-lexicon.txt |
sort | uniq > $lang_dir/lexicon.txt
if [ ! -f $lang_dir/L_disambig.pt ]; then
./local/prepare_lang.py --lang-dir $lang_dir
fi
fi
if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
log "Stage 6: Prepare BPE based lang"
for vocab_size in ${vocab_sizes[@]}; do
lang_dir=data/lang_bpe_${vocab_size}
mkdir -p $lang_dir
# We reuse words.txt from phone based lexicon
# so that the two can share G.pt later.
cp data/lang_phone/words.txt $lang_dir
if [ ! -f $lang_dir/transcript_words.txt ]; then
log "Generate data for BPE training"
files=$(
find "$dl_dir/LibriSpeech/train-clean-100" -name "*.trans.txt"
find "$dl_dir/LibriSpeech/train-clean-360" -name "*.trans.txt"
find "$dl_dir/LibriSpeech/train-other-500" -name "*.trans.txt"
)
for f in ${files[@]}; do
cat $f | cut -d " " -f 2-
done > $lang_dir/transcript_words.txt
fi
./local/train_bpe_model.py \
--lang-dir $lang_dir \
--vocab-size $vocab_size \
--transcript $lang_dir/transcript_words.txt
if [ ! -f $lang_dir/L_disambig.pt ]; then
./local/prepare_lang_bpe.py --lang-dir $lang_dir
fi
done
fi
if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
log "Stage 7: Prepare bigram P"
for vocab_size in ${vocab_sizes[@]}; do
lang_dir=data/lang_bpe_${vocab_size}
if [ ! -f $lang_dir/transcript_tokens.txt ]; then
./local/convert_transcript_words_to_tokens.py \
--lexicon $lang_dir/lexicon.txt \
--transcript $lang_dir/transcript_words.txt \
--oov "<UNK>" \
> $lang_dir/transcript_tokens.txt
fi
if [ ! -f $lang_dir/P.arpa ]; then
./shared/make_kn_lm.py \
-ngram-order 2 \
-text $lang_dir/transcript_tokens.txt \
-lm $lang_dir/P.arpa
fi
if [ ! -f $lang_dir/P.fst.txt ]; then
python3 -m kaldilm \
--read-symbol-table="$lang_dir/tokens.txt" \
--disambig-symbol='#0' \
--max-order=2 \
$lang_dir/P.arpa > $lang_dir/P.fst.txt
fi
done
fi
if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
log "Stage 8: Prepare G"
# We assume you have install kaldilm, if not, please install
# it using: pip install kaldilm
mkdir -p data/lm
if [ ! -f data/lm/G_3_gram.fst.txt ]; then
# It is used in building HLG
python3 -m kaldilm \
--read-symbol-table="data/lang_phone/words.txt" \
--disambig-symbol='#0' \
--max-order=3 \
$dl_dir/lm/3-gram.pruned.1e-7.arpa > data/lm/G_3_gram.fst.txt
fi
if [ ! -f data/lm/G_4_gram.fst.txt ]; then
# It is used for LM rescoring
python3 -m kaldilm \
--read-symbol-table="data/lang_phone/words.txt" \
--disambig-symbol='#0' \
--max-order=4 \
$dl_dir/lm/4-gram.arpa > data/lm/G_4_gram.fst.txt
fi
fi
if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
log "Stage 9: Compile HLG"
./local/compile_hlg.py --lang-dir data/lang_phone
for vocab_size in ${vocab_sizes[@]}; do
lang_dir=data/lang_bpe_${vocab_size}
./local/compile_hlg.py --lang-dir $lang_dir
done
fi