| egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/README.md | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/demo.py | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/finetune.py | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/infer.py | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/infer.sh | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 | |
| egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/utils | ●●●●● 补丁 | 查看 | 原始文档 | blame | 历史 |
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/README.md
New file @@ -0,0 +1 @@ ../../TEMPLATE/README.md egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/demo.py
New file @@ -0,0 +1,10 @@ from modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks inference_pipeline = pipeline( task=Tasks.auto_speech_recognition, model='damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch', model_revision='v1.0.0') rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_tr.wav') print(rec_result) egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/finetune.py
@@ -1,4 +1,27 @@ import os <<<<<<< HEAD from modelscope.metainfo import Trainers from modelscope.trainers import build_trainer from funasr.datasets.ms_dataset import MsDataset from funasr.utils.modelscope_param import modelscope_args def modelscope_finetune(params): if not os.path.exists(params.output_dir): os.makedirs(params.output_dir, exist_ok=True) # dataset split ["train", "validation"] ds_dict = MsDataset.load(params.data_path) kwargs = dict( model=params.model, data_dir=ds_dict, dataset_type=params.dataset_type, work_dir=params.output_dir, batch_bins=params.batch_bins, max_epoch=params.max_epoch, lr=params.lr) ======= from modelscope.metainfo import Trainers from modelscope.trainers import build_trainer from funasr.datasets.ms_dataset import MsDataset @@ -18,11 +41,22 @@ batch_bins=params["batch_bins"], max_epoch=params["max_epoch"], lr=params["lr"]) >>>>>>> main trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs) trainer.train() if __name__ == '__main__': <<<<<<< HEAD params = modelscope_args(model="damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch", data_path="./data") params.output_dir = "./checkpoint" # m模型保存路径 params.data_path = "./example_data/" # 数据路径 params.dataset_type = "small" # 小数据量设置small，若数据量大于1000小时，请使用large params.batch_bins = 2000 # batch size，如果dataset_type="small"，batch_bins单位为fbank特征帧数，如果dataset_type="large"，batch_bins单位为毫秒， params.max_epoch = 50 # 最大训练轮数 params.lr = 0.00005 # 设置学习率 ======= params = {} params["output_dir"] = "./checkpoint" params["data_dir"] = "./data" @@ -32,4 +66,5 @@ params["lr"] = 0.00005 params["model"] = "damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch" params["model_revision"] = None >>>>>>> main modelscope_finetune(params) egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/infer.py
@@ -1,3 +1,33 @@ <<<<<<< HEAD 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, batch_size=args.batch_size, param_dict={"decoding_model": args.decoding_mode, "hotword": args.hotword_txt} ) inference_pipeline(audio_in=args.audio_in) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--model', type=str, default="damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-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', type=int, default=64) parser.add_argument('--gpuid', type=str, default="0") args = parser.parse_args() modelscope_infer(args) ======= from modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks @@ -11,3 +41,4 @@ ) rec_result = inference_pipeline(audio_in=audio_in, param_dict={"decoding_model":"offline"}) print(rec_result) >>>>>>> main egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/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_UniASR_asr_2pass-tr-16k-common-vocab1582-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 @@ ../../../egs/aishell/transformer/utils
