speech_recognition/FunASR
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lyblsgo 2023-10-11 19:48:17 +08:00
commit 654af12d8d
8 changed files with 155 additions and 4 deletions
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4
.github/workflows/main.yml vendored
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@ -23,7 +23,7 @@ jobs:
pre-build-command: "pip install sphinx-markdown-tables nbsphinx jinja2 recommonmark sphinx_rtd_theme myst-parser"
- name: deploy copy
if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/dev_wjm' || github.ref == 'refs/heads/dev_lyh'
if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/dev_wjm' || github.ref == 'refs/heads/dev_lyh' || github.ref == 'refs/heads/dev_lhn'
run: |
mkdir public
touch public/.nojekyll
@ -35,7 +35,7 @@ jobs:
cp -r docs/m2met2/_build/html/* public/m2met2/
- name: deploy github.io pages
if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/dev_wjm' || github.ref == 'refs/heads/dev_lyh'
if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/dev_wjm' || github.ref == 'refs/heads/dev_lyh' || github.ref == 'refs/heads/dev_lhn'
uses: peaceiris/actions-gh-pages@v2.3.1
env:
GITHUB_TOKEN: ${{ secrets.ACCESS_TOKEN }}

1
egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/README.md Symbolic link
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@ -0,0 +1 @@
../TEMPLATE/README.md

1
egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/README_zh.md Symbolic link
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@ -0,0 +1 @@
../TEMPLATE/README_zh.md

18
egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/demo.py Normal file
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@ -0,0 +1,18 @@
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
if __name__ == '__main__':
audio_in = 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_en.wav'
output_dir = "./results"
inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
model='damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020',
model_revision='v1.0.0',
vad_model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
punc_model='damo/punc_ct-transformer_cn-en-common-vocab471067-large',
punc_model_revision='v1.0.0',
output_dir=output_dir,
)
rec_result = inference_pipeline(audio_in=audio_in, batch_size_token=5000, batch_size_token_threshold_s=40, max_single_segment_time=6000)
print(rec_result)

27
egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/infer.py Normal file
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@ -0,0 +1,27 @@
import os
import shutil
import argparse
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
def modelscope_infer(args):
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpuid)
inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
model=args.model,
output_dir=args.output_dir,
param_dict={"decoding_model": args.decoding_mode, "hotword": args.hotword_txt}
)
inference_pipeline(audio_in=args.audio_in, batch_size_token=args.batch_size_token)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, default="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
parser.add_argument('--audio_in', type=str, default="./data/test/wav.scp")
parser.add_argument('--output_dir', type=str, default="./results/")
parser.add_argument('--decoding_mode', type=str, default="normal")
parser.add_argument('--hotword_txt', type=str, default=None)
parser.add_argument('--batch_size_token', type=int, default=5000)
parser.add_argument('--gpuid', type=str, default="0")
args = parser.parse_args()
modelscope_infer(args)

103
egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/infer.sh Normal file
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@ -0,0 +1,103 @@
#!/usr/bin/env bash
set -e
set -u
set -o pipefail
stage=1
stop_stage=2
model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
data_dir="./data/test"
output_dir="./results"
batch_size=64
gpu_inference=true # whether to perform gpu decoding
gpuid_list="0,1" # set gpus, e.g., gpuid_list="0,1"
njob=64 # the number of jobs for CPU decoding, if gpu_inference=false, use CPU decoding, please set njob
checkpoint_dir=
checkpoint_name="valid.cer_ctc.ave.pb"
. utils/parse_options.sh || exit 1;
if ${gpu_inference} == "true"; then
nj=$(echo $gpuid_list | awk -F "," '{print NF}')
else
nj=$njob
batch_size=1
gpuid_list=""
for JOB in $(seq ${nj}); do
gpuid_list=$gpuid_list"-1,"
done
fi
mkdir -p $output_dir/split
split_scps=""
for JOB in $(seq ${nj}); do
split_scps="$split_scps $output_dir/split/wav.$JOB.scp"
done
perl utils/split_scp.pl ${data_dir}/wav.scp ${split_scps}
if [ -n "${checkpoint_dir}" ]; then
python utils/prepare_checkpoint.py ${model} ${checkpoint_dir} ${checkpoint_name}
model=${checkpoint_dir}/${model}
fi
if [ $stage -le 1 ] && [ $stop_stage -ge 1 ];then
echo "Decoding ..."
gpuid_list_array=(${gpuid_list//,/ })
for JOB in $(seq ${nj}); do
{
id=$((JOB-1))
gpuid=${gpuid_list_array[$id]}
mkdir -p ${output_dir}/output.$JOB
python infer.py \
--model ${model} \
--audio_in ${output_dir}/split/wav.$JOB.scp \
--output_dir ${output_dir}/output.$JOB \
--batch_size ${batch_size} \
--gpuid ${gpuid}
}&
done
wait
mkdir -p ${output_dir}/1best_recog
for f in token score text; do
if [ -f "${output_dir}/output.1/1best_recog/${f}" ]; then
for i in $(seq "${nj}"); do
cat "${output_dir}/output.${i}/1best_recog/${f}"
done | sort -k1 >"${output_dir}/1best_recog/${f}"
fi
done
fi
if [ $stage -le 2 ] && [ $stop_stage -ge 2 ];then
echo "Computing WER ..."
cp ${output_dir}/1best_recog/text ${output_dir}/1best_recog/text.proc
cp ${data_dir}/text ${output_dir}/1best_recog/text.ref
python utils/compute_wer.py ${output_dir}/1best_recog/text.ref ${output_dir}/1best_recog/text.proc ${output_dir}/1best_recog/text.cer
tail -n 3 ${output_dir}/1best_recog/text.cer
fi
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ];then
echo "SpeechIO TIOBE textnorm"
echo "$0 --> Normalizing REF text ..."
./utils/textnorm_zh.py \
--has_key --to_upper \
${data_dir}/text \
${output_dir}/1best_recog/ref.txt
echo "$0 --> Normalizing HYP text ..."
./utils/textnorm_zh.py \
--has_key --to_upper \
${output_dir}/1best_recog/text.proc \
${output_dir}/1best_recog/rec.txt
grep -v $'\t$' ${output_dir}/1best_recog/rec.txt > ${output_dir}/1best_recog/rec_non_empty.txt
echo "$0 --> computing WER/CER and alignment ..."
./utils/error_rate_zh \
--tokenizer char \
--ref ${output_dir}/1best_recog/ref.txt \
--hyp ${output_dir}/1best_recog/rec_non_empty.txt \
${output_dir}/1best_recog/DETAILS.txt | tee ${output_dir}/1best_recog/RESULTS.txt
rm -rf ${output_dir}/1best_recog/rec.txt ${output_dir}/1best_recog/rec_non_empty.txt
fi

1
egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/utils Symbolic link
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@ -0,0 +1 @@
../../asr/TEMPLATE/utils

4
funasr/datasets/large_datasets/dataset.py
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@ -108,7 +108,7 @@ class AudioDataset(IterableDataset):
ark_reader = ReadHelper('ark:{}'.format(data_file))
reader_list.append(ark_reader)
elif data_type == "text" or data_type == "sound" or data_type == 'text_hotword':
text_reader = open(data_file, "r")
text_reader = open(data_file, "r", encoding="utf-8")
reader_list.append(text_reader)
elif data_type == "none":
continue
@ -205,7 +205,7 @@ def Dataset(data_list_file,
# pre_prob = conf.get("pre_prob", 0) # unused yet
if pre_hwfile is not None:
pre_hwlist = []
with open(pre_hwfile, 'r') as fin:
with open(pre_hwfile, 'r', encoding="utf-8") as fin:
for line in fin.readlines():
pre_hwlist.append(line.strip())
else: