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https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
add paraformer online infer and finetune recipe
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haoneng.lhn
parent
e7334e0ec3
commit
f6809397d3
@ -4,7 +4,7 @@ from modelscope.utils.constant import Tasks
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inference_pipeline = pipeline(
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task=Tasks.auto_speech_recognition,
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model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online',
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model_revision='v1.0.5',
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model_revision='v1.0.6',
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mode="paraformer_fake_streaming"
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)
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audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav'
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@ -14,7 +14,7 @@ os.environ["MODELSCOPE_CACHE"] = "./"
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inference_pipeline = pipeline(
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task=Tasks.auto_speech_recognition,
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model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online',
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model_revision='v1.0.5',
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model_revision='v1.0.6',
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mode="paraformer_streaming"
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)
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@ -0,0 +1,36 @@
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import os
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from modelscope.metainfo import Trainers
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from modelscope.trainers import build_trainer
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from funasr.datasets.ms_dataset import MsDataset
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from funasr.utils.modelscope_param import modelscope_args
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def modelscope_finetune(params):
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if not os.path.exists(params.output_dir):
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os.makedirs(params.output_dir, exist_ok=True)
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# dataset split ["train", "validation"]
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ds_dict = MsDataset.load(params.data_path)
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kwargs = dict(
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model=params.model,
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data_dir=ds_dict,
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dataset_type=params.dataset_type,
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work_dir=params.output_dir,
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batch_bins=params.batch_bins,
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max_epoch=params.max_epoch,
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lr=params.lr)
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trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
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trainer.train()
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if __name__ == '__main__':
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params = modelscope_args(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", data_path="./data")
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params.output_dir = "./checkpoint" # m模型保存路径
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params.data_path = "./example_data/" # 数据路径
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params.dataset_type = "small" # 小数据量设置small，若数据量大于1000小时，请使用large
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params.batch_bins = 1000 # 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，
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params.max_epoch = 20 # 最大训练轮数
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params.lr = 0.00005 # 设置学习率
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modelscope_finetune(params)
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@ -0,0 +1,32 @@
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import os
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import shutil
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import argparse
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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def modelscope_infer(args):
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os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpuid)
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inference_pipeline = pipeline(
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task=Tasks.auto_speech_recognition,
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model=args.model,
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output_dir=args.output_dir,
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batch_size=args.batch_size,
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model_revision='v1.0.6',
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mode="paraformer_fake_streaming",
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param_dict={"decoding_model": args.decoding_mode, "hotword": args.hotword_txt}
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)
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inference_pipeline(audio_in=args.audio_in)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--model', type=str, default="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
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parser.add_argument('--audio_in', type=str, default="./data/test/wav.scp")
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parser.add_argument('--output_dir', type=str, default="./results/")
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parser.add_argument('--decoding_mode', type=str, default="normal")
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parser.add_argument('--model_revision', type=str, default=None)
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parser.add_argument('--mode', type=str, default=None)
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parser.add_argument('--hotword_txt', type=str, default=None)
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parser.add_argument('--batch_size', type=int, default=64)
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parser.add_argument('--gpuid', type=str, default="0")
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args = parser.parse_args()
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modelscope_infer(args)
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@ -0,0 +1,104 @@
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#!/usr/bin/env bash
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set -e
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set -u
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set -o pipefail
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stage=1
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stop_stage=2
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model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online"
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data_dir="./data/test"
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output_dir="./results"
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batch_size=64
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gpu_inference=true # whether to perform gpu decoding
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gpuid_list="0,1" # set gpus, e.g., gpuid_list="0,1"
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njob=64 # the number of jobs for CPU decoding, if gpu_inference=false, use CPU decoding, please set njob
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checkpoint_dir=
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checkpoint_name="valid.cer_ctc.ave.pb"
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. utils/parse_options.sh || exit 1;
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if ${gpu_inference} == "true"; then
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nj=$(echo $gpuid_list | awk -F "," '{print NF}')
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else
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nj=$njob
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batch_size=1
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gpuid_list=""
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for JOB in $(seq ${nj}); do
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gpuid_list=$gpuid_list"-1,"
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done
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fi
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mkdir -p $output_dir/split
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split_scps=""
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for JOB in $(seq ${nj}); do
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split_scps="$split_scps $output_dir/split/wav.$JOB.scp"
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done
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perl utils/split_scp.pl ${data_dir}/wav.scp ${split_scps}
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if [ -n "${checkpoint_dir}" ]; then
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python utils/prepare_checkpoint.py ${model} ${checkpoint_dir} ${checkpoint_name}
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model=${checkpoint_dir}/${model}
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fi
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if [ $stage -le 1 ] && [ $stop_stage -ge 1 ];then
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echo "Decoding ..."
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gpuid_list_array=(${gpuid_list//,/ })
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for JOB in $(seq ${nj}); do
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{
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id=$((JOB-1))
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gpuid=${gpuid_list_array[$id]}
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mkdir -p ${output_dir}/output.$JOB
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python infer.py \
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--model ${model} \
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--audio_in ${output_dir}/split/wav.$JOB.scp \
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--output_dir ${output_dir}/output.$JOB \
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--batch_size ${batch_size} \
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--gpuid ${gpuid}
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--mode "paraformer_fake_streaming"
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}&
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done
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wait
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mkdir -p ${output_dir}/1best_recog
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for f in token score text; do
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if [ -f "${output_dir}/output.1/1best_recog/${f}" ]; then
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for i in $(seq "${nj}"); do
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cat "${output_dir}/output.${i}/1best_recog/${f}"
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done | sort -k1 >"${output_dir}/1best_recog/${f}"
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fi
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done
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fi
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if [ $stage -le 2 ] && [ $stop_stage -ge 2 ];then
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echo "Computing WER ..."
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cp ${output_dir}/1best_recog/text ${output_dir}/1best_recog/text.proc
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cp ${data_dir}/text ${output_dir}/1best_recog/text.ref
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python utils/compute_wer.py ${output_dir}/1best_recog/text.ref ${output_dir}/1best_recog/text.proc ${output_dir}/1best_recog/text.cer
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tail -n 3 ${output_dir}/1best_recog/text.cer
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fi
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if [ $stage -le 3 ] && [ $stop_stage -ge 3 ];then
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echo "SpeechIO TIOBE textnorm"
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echo "$0 --> Normalizing REF text ..."
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./utils/textnorm_zh.py \
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--has_key --to_upper \
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${data_dir}/text \
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${output_dir}/1best_recog/ref.txt
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echo "$0 --> Normalizing HYP text ..."
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./utils/textnorm_zh.py \
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--has_key --to_upper \
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${output_dir}/1best_recog/text.proc \
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${output_dir}/1best_recog/rec.txt
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grep -v $'\t$' ${output_dir}/1best_recog/rec.txt > ${output_dir}/1best_recog/rec_non_empty.txt
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echo "$0 --> computing WER/CER and alignment ..."
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./utils/error_rate_zh \
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--tokenizer char \
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--ref ${output_dir}/1best_recog/ref.txt \
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--hyp ${output_dir}/1best_recog/rec_non_empty.txt \
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${output_dir}/1best_recog/DETAILS.txt | tee ${output_dir}/1best_recog/RESULTS.txt
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rm -rf ${output_dir}/1best_recog/rec.txt ${output_dir}/1best_recog/rec_non_empty.txt
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fi
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