如何使用ModelScope魔搭训练工业级语音识别模型

from modelscope.pipelines import pipelinefrom modelscope.utils.constant import Tasks
inference_16k_pipline = pipeline(    task=Tasks.auto_speech_recognition,    model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')
rec_result = inference_16k_pipline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')print(rec_result)

rec_result = inference_16k_pipline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.pcm', audio_fs=16000)

# Clone the repo:git clone https://github.com/alibaba/FunASR.git
# Install Conda:wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.shsh Miniconda3-latest-Linux-x86_64.shconda create -n funasr python=3.7conda activate funasr
# Install Pytorch (version >= 1.7.0):conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=9.2 -c pytorch  # For more versions, please see https://pytorch.org/get-started/locally/
# Install other packages:pip install --editable ./

pip install "modelscope[audio]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

tree ./example_data/./example_data/├── dev│   ├── text│   └── wav.scp├── test│   ├── text│   └── wav.scp└── train    ├── text    └── wav.scp
3 directories, 6 files

cat textBAC009S0002W0122 而 对 楼市 成交 抑制 作用 最 大 的 限 购BAC009S0002W0123 也 成为 地方 政府 的 眼中 钉
cat wav.scpBAC009S0002W0122 /mnt/data/wav/train/S0002/BAC009S0002W0122.wavBAC009S0002W0123 /mnt/data/wav/train/S0002/BAC009S0002W0123.wav

cd egs_modelscope/common
# compute fbank featuresutils/compute_fbank.sh --cmd "utils/run.pl" --nj 32 --speed_perturb "1.0" \        example_data/train ${exp_dir}/exp/make_fbank/train ${fbankdir}/trainutils/fix_data_feat.sh ${fbankdir}/train
# apply low_frame_rate and cmvnutils/apply_lfr_and_cmvn.sh --cmd "utils/run.pl" --nj 32 --lfr True --lfr-m 7 --lfr-n 6 \        ${fbankdir}/train am.mvn ${exp_dir}/exp/make_fbank/train ${feat_train_dir}
# Text Tokenize# 我爱reading->我 爱 read@@ ingutils/text_tokenize.sh --cmd "utils/run.pl" --nj 32 ${fbankdir}/train seg_dict ${feat_train_dir}/log ${feat_train_dir}
# Dictionary Preparationawk -v v=,vocab_size '{print $0v}' ${feat_train_dir}/text_shape > ${feat_train_dir}/text_shape.charcp ${feat_train_dir}/speech_shape ${feat_train_dir}/text_shape ${feat_train_dir}/text_shape.char asr_stats_fbank_zh_char/train
# dev集参照如上train集对音频进行特征提取

modelname="speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"python modelscope_utils/download_model.py --model_name $modelname

python asr_train_paraformer.py \  --train_data_path_and_name_and_type ${feat_train_dir}/feats.scp,speech,kaldi_ark \  --train_data_path_and_name_and_type ${feat_train_dir}/text,text,text \  --valid_data_path_and_name_and_type ${feat_dev_dir}/feats.sc,speech,kaldi_ark \  --valid_data_path_and_name_and_type ${feat_dev_dir}/text,text,text \  --output_dir exp/model_dir \  --init_param base_model.pth \  --config finetune_config.yaml

cp finetuned_model.pth exp_dir/finetune_model_name.modelscopecp -r ${HOME}/.cache/modelscope/hub/damo/pretrained_model_name/* exp_dir/
python -m funasr.bin.modelscope_infer \  --local_model_path exp_dir \  --wav_list example_data/dev/wav.scp \  --output_file logdir/text

# 配置modelscope_common_finetune.sh中参数# dataset：  数据路径，结构如example_data所示，dev/test可不配置，若无dev数据处理过程中可自动抽取训练集中1000句音频作为dev集# tag：  结果保存路径后缀
# 配置修改完成后，执行命令启动模型微调训练及推理: sh modelscope_common_finetune.sh

cd egs_modelscope/speechio/paraformer
# 配置paraformer_large_infer.sh中参数# ori_data:       # 测试数据路径# data_dir:        # 数据处理路径# exp_dir:        # 结果保存路径# test_sets:       # 测试集名称# use_lm=true      # 是否使用LM# beam_size=10    # 设置beam_size# lm_weight=0.15  # 设置lm_weight
# 测试集目录结构树，必须有trans.txt和wav.scptree SPEECHIO_ASR_ZH00001SPEECHIO_ASR_ZH00001├── metadata.tsv├── trans.txt├── wav│   ├── e5oipIfM49I__20190201_CCTV_10.wav│   └── e5oipIfM49I__20190201_CCTV_1.wav│    └── ...└── wav.scp
# 配置修改完成后，执行命令启动模型推理: sh paraformer_large_infer.sh