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https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
hnluo
GitHub
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c2dee5e3c2
@ -41,8 +41,7 @@ The decoding results can be found in `$output_dir/1best_recog/text.cer`, which i
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- Modify inference related parameters in `infer_after_finetune.py`
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- <strong>output_dir:</strong> # result dir
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- <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
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- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave
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.pb`
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- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pb`
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- Then you can run the pipeline to finetune with:
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```python
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@ -0,0 +1,12 @@
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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decoding_mode="normal" #fast, normal, offline
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inference_pipeline = pipeline(
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task=Tasks.auto_speech_recognition,
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model='damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online',
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param_dict={"decoding_model": decoding_mode}
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)
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rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
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print(rec_result)
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@ -1,88 +0,0 @@
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import os
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import shutil
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from multiprocessing import Pool
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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from funasr.utils.compute_wer import compute_wer
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def modelscope_infer_core(output_dir, split_dir, njob, idx):
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output_dir_job = os.path.join(output_dir, "output.{}".format(idx))
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gpu_id = (int(idx) - 1) // njob
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if "CUDA_VISIBLE_DEVICES" in os.environ.keys():
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gpu_list = os.environ['CUDA_VISIBLE_DEVICES'].split(",")
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os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_list[gpu_id])
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else:
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os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
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inference_pipeline = pipeline(
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task=Tasks.auto_speech_recognition,
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model="damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online",
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output_dir=output_dir_job,
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batch_size=1
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)
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audio_in = os.path.join(split_dir, "wav.{}.scp".format(idx))
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inference_pipeline(audio_in=audio_in, param_dict={"decoding_model": "normal"})
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def modelscope_infer(params):
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# prepare for multi-GPU decoding
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ngpu = params["ngpu"]
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njob = params["njob"]
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output_dir = params["output_dir"]
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if os.path.exists(output_dir):
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shutil.rmtree(output_dir)
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os.mkdir(output_dir)
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split_dir = os.path.join(output_dir, "split")
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os.mkdir(split_dir)
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nj = ngpu * njob
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wav_scp_file = os.path.join(params["data_dir"], "wav.scp")
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with open(wav_scp_file) as f:
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lines = f.readlines()
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num_lines = len(lines)
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num_job_lines = num_lines // nj
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start = 0
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for i in range(nj):
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end = start + num_job_lines
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file = os.path.join(split_dir, "wav.{}.scp".format(str(i + 1)))
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with open(file, "w") as f:
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if i == nj - 1:
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f.writelines(lines[start:])
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else:
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f.writelines(lines[start:end])
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start = end
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p = Pool(nj)
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for i in range(nj):
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p.apply_async(modelscope_infer_core,
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args=(output_dir, split_dir, njob, str(i + 1)))
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p.close()
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p.join()
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# combine decoding results
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best_recog_path = os.path.join(output_dir, "1best_recog")
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os.mkdir(best_recog_path)
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files = ["text", "token", "score"]
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for file in files:
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with open(os.path.join(best_recog_path, file), "w") as f:
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for i in range(nj):
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job_file = os.path.join(output_dir, "output.{}/1best_recog".format(str(i + 1)), file)
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with open(job_file) as f_job:
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lines = f_job.readlines()
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f.writelines(lines)
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# If text exists, compute CER
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text_in = os.path.join(params["data_dir"], "text")
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if os.path.exists(text_in):
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text_proc_file = os.path.join(best_recog_path, "text")
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compute_wer(text_in, text_proc_file, os.path.join(best_recog_path, "text.cer"))
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if __name__ == "__main__":
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params = {}
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params["data_dir"] = "./data/test"
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params["output_dir"] = "./results"
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params["ngpu"] = 1
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params["njob"] = 1
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modelscope_infer(params)
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@ -0,0 +1 @@
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../../TEMPLATE/infer.py
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@ -0,0 +1,105 @@
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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_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online"
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data_dir="./data/test"
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output_dir="./results"
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batch_size=1
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gpu_inference=false # whether to perform gpu decoding
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gpuid_list="-1" # set gpus, e.g., gpuid_list="0,1"
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njob=32 # 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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decoding_mode="normal"
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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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--decoding_mode ${decoding_mode}
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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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@ -1,53 +0,0 @@
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import json
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import os
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import shutil
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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from funasr.utils.compute_wer import compute_wer
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def modelscope_infer_after_finetune(params):
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# prepare for decoding
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pretrained_model_path = os.path.join(os.environ["HOME"], ".cache/modelscope/hub", params["modelscope_model_name"])
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for file_name in params["required_files"]:
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if file_name == "configuration.json":
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with open(os.path.join(pretrained_model_path, file_name)) as f:
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config_dict = json.load(f)
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config_dict["model"]["am_model_name"] = params["decoding_model_name"]
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with open(os.path.join(params["output_dir"], "configuration.json"), "w") as f:
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json.dump(config_dict, f, indent=4, separators=(',', ': '))
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else:
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shutil.copy(os.path.join(pretrained_model_path, file_name),
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os.path.join(params["output_dir"], file_name))
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decoding_path = os.path.join(params["output_dir"], "decode_results")
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if os.path.exists(decoding_path):
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shutil.rmtree(decoding_path)
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os.mkdir(decoding_path)
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# decoding
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inference_pipeline = pipeline(
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task=Tasks.auto_speech_recognition,
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model=params["output_dir"],
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output_dir=decoding_path,
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batch_size=1
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)
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audio_in = os.path.join(params["data_dir"], "wav.scp")
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inference_pipeline(audio_in=audio_in, param_dict={"decoding_model": "normal"})
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# computer CER if GT text is set
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text_in = os.path.join(params["data_dir"], "text")
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if os.path.exists(text_in):
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text_proc_file = os.path.join(decoding_path, "1best_recog/text")
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compute_wer(text_in, text_proc_file, os.path.join(decoding_path, "text.cer"))
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if __name__ == '__main__':
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params = {}
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params["modelscope_model_name"] = "damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online"
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params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
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params["output_dir"] = "./checkpoint"
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params["data_dir"] = "./data/test"
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params["decoding_model_name"] = "20epoch.pb"
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modelscope_infer_after_finetune(params)
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@ -0,0 +1 @@
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../../../../egs/aishell/transformer/utils
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