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some small changes for aidatatang_200zh #542

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merged 8 commits into from
Aug 23, 2022
Merged

some small changes for aidatatang_200zh #542

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7217610
Update prepare.sh
rxhmdia Aug 23, 2022
e3b3a7f
Update compute_fbank_aidatatang_200zh.py
rxhmdia Aug 23, 2022
f1b428e
Update prepare.sh
rxhmdia Aug 23, 2022
d04f7c8
Update prepare.sh
rxhmdia Aug 23, 2022
6e994b5
Update decode.py
rxhmdia Aug 23, 2022
ddf904c
Update prepare.sh
rxhmdia Aug 23, 2022
7c0c2a0
Update decode.py
rxhmdia Aug 23, 2022
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Update decode.py
rxhmdia Aug 23, 2022
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2 changes: 1 addition & 1 deletion egs/aidatatang_200zh/ASR/local/compute_fbank_aidatatang_200zh.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,7 +43,7 @@


def compute_fbank_aidatatang_200zh(num_mel_bins: int = 80):
src_dir = Path("data/manifests")
src_dir = Path("data/manifests/aidatatang_200zh")
output_dir = Path("data/fbank")
num_jobs = min(15, os.cpu_count())

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58 changes: 28 additions & 30 deletions egs/aidatatang_200zh/ASR/prepare.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -50,69 +50,67 @@ if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
fi

if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
log "Stage 2: Process aidatatang_200zh"
if [ ! -f data/fbank/aidatatang_200zh/.fbank.done ]; then
mkdir -p data/fbank/aidatatang_200zh
lhotse prepare aidatatang-200zh $dl_dir data/manifests/aidatatang_200zh
touch data/fbank/aidatatang_200zh/.fbank.done
fi
fi

if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
log "Stage 3: Prepare musan manifest"
log "Stage 2: Prepare musan manifest"
# We assume that you have downloaded the musan corpus
# to data/musan
if [ ! -f data/manifests/.musan_manifests.done ]; then
if [ ! -f data/manifests/.manifests.done ]; then
log "It may take 6 minutes"
mkdir -p data/manifests
lhotse prepare musan $dl_dir/musan data/manifests
touch data/manifests/.musan_manifests.done
mkdir -p data/manifests/
lhotse prepare musan $dl_dir/musan data/manifests/
touch data/manifests/.manifests.done
fi
fi

if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
log "Stage 4: Compute fbank for musan"
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
log "Stage 3: Compute fbank for musan"
if [ ! -f data/fbank/.msuan.done ]; then
mkdir -p data/fbank
./local/compute_fbank_musan.py
touch data/fbank/.msuan.done
fi
fi

if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
log "Stage 5: Compute fbank for aidatatang_200zh"
if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
log "Stage 4: Compute fbank for aidatatang_200zh"
if [ ! -f data/fbank/.aidatatang_200zh.done ]; then
mkdir -p data/fbank
./local/compute_fbank_aidatatang_200zh.py
touch data/fbank/.aidatatang_200zh.done
fi
fi

if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
log "Stage 6: Prepare char based lang"
if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
log "Stage 5: Prepare char based lang"
lang_char_dir=data/lang_char
mkdir -p $lang_char_dir

# Prepare text.
grep "\"text\":" data/manifests/aidatatang_200zh/supervisions_train.json \
| sed -e 's/["text:\t ]*//g' | sed 's/,//g' \
| ./local/text2token.py -t "char" > $lang_char_dir/text

# Note: in Linux, you can install jq with the following command:
# 1. wget -O jq https://github.com/stedolan/jq/releases/download/jq-1.6/jq-linux64
# 2. chmod +x ./jq
# 3. cp jq /usr/bin
if [ ! -f $lang_char_dir/text ]; then
gunzip -c data/manifests/aidatatang_200zh/aidatatang_supervisions_train.jsonl.gz \
|jq '.text' |sed -e 's/["text:\t ]*//g' | sed 's/"//g' \
| ./local/text2token.py -t "char" > $lang_char_dir/text
fi
# Prepare words.txt
grep "\"text\":" data/manifests/aidatatang_200zh/supervisions_train.json \
| sed -e 's/["text:\t]*//g' | sed 's/,//g' \
| ./local/text2token.py -t "char" > $lang_char_dir/text_words
if [ ! -f $lang_char_dir/text_words ]; then
gunzip -c data/manifests/aidatatang_200zh/aidatatang_supervisions_train.jsonl.gz \
| jq '.text' | sed -e 's/["text:\t]*//g' | sed 's/"//g' \
| ./local/text2token.py -t "char" > $lang_char_dir/text_words
fi

cat $lang_char_dir/text_words | sed 's/ /\n/g' | sort -u | sed '/^$/d' \
| uniq > $lang_char_dir/words_no_ids.txt

if [ ! -f $lang_char_dir/words.txt ]; then
./local/prepare_words.py \
--input-file $lang_char_dir/words_no_ids.txt
--output-file $lang_char_dir/words.txt
--input-file $lang_char_dir/words_no_ids.txt \
--output-file $lang_char_dir/words.txt
fi

if [ ! -f $lang_char_dir/L_disambig.pt ]; then
./local/prepare_char.py
fi
fi

57 changes: 4 additions & 53 deletions egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/decode.py
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Original file line number Diff line number Diff line change
Expand Up @@ -522,63 +522,14 @@ def main():
num_param = sum([p.numel() for p in model.parameters()])
logging.info(f"Number of model parameters: {num_param}")

# Note: Please use "pip install webdataset==0.1.103"
# for installing the webdataset.
import glob
import os

from lhotse import CutSet
from lhotse.dataset.webdataset import export_to_webdataset

# we need cut ids to display recognition results.
args.return_cuts = True
aidatatang_200zh = Aidatatang_200zhAsrDataModule(args)

dev = "dev"
test = "test"

if not os.path.exists(f"{dev}/shared-0.tar"):
os.makedirs(dev)
dev_cuts = aidatatang_200zh.valid_cuts()
export_to_webdataset(
dev_cuts,
output_path=f"{dev}/shared-%d.tar",
shard_size=300,
)

if not os.path.exists(f"{test}/shared-0.tar"):
os.makedirs(test)
test_cuts = aidatatang_200zh.test_cuts()
export_to_webdataset(
test_cuts,
output_path=f"{test}/shared-%d.tar",
shard_size=300,
)

dev_shards = [
str(path)
for path in sorted(glob.glob(os.path.join(dev, "shared-*.tar")))
]
cuts_dev_webdataset = CutSet.from_webdataset(
dev_shards,
split_by_worker=True,
split_by_node=True,
shuffle_shards=True,
)

test_shards = [
str(path)
for path in sorted(glob.glob(os.path.join(test, "shared-*.tar")))
]
cuts_test_webdataset = CutSet.from_webdataset(
test_shards,
split_by_worker=True,
split_by_node=True,
shuffle_shards=True,
)

dev_dl = aidatatang_200zh.valid_dataloaders(cuts_dev_webdataset)
test_dl = aidatatang_200zh.test_dataloaders(cuts_test_webdataset)
dev_cuts = aidatatang_200zh.valid_cuts()
test_cuts = aidatatang_200zh.test_cuts()
dev_dl = aidatatang_200zh.valid_dataloaders(dev_cuts)
test_dl = aidatatang_200zh.test_dataloaders(test_cuts)

test_sets = ["dev", "test"]
test_dl = [dev_dl, test_dl]
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