| .github/workflows/main.yml | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/README.md | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/README_zh.md | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/demo.py | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/infer.py | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/infer.sh | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/utils | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| funasr/datasets/large_datasets/dataset.py | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 |
.github/workflows/main.yml
@@ -23,7 +23,7 @@ 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 @@ 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 }} egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/README.md
New file @@ -0,0 +1 @@ ../TEMPLATE/README.md egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/README_zh.md
New file @@ -0,0 +1 @@ ../TEMPLATE/README_zh.md egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/demo.py
New file @@ -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)
New file @@ -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) egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020/infer.sh
New file @@ -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
New file @@ -0,0 +1 @@ ../../asr/TEMPLATE/utils funasr/datasets/large_datasets/dataset.py
@@ -108,7 +108,7 @@ 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 @@ # 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:
