From a0879e4663a0bcb6e6cdb7f532cde5dc37d54848 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=8C=97=E5=BF=B5?= Date: Wed, 22 Mar 2023 19:27:15 +0800 Subject: [PATCH] update paraformer recipe --- .../infer.py | 2 +- .../infer.sh | 8 +- .../README.md | 38 +++++- .../infer.py | 108 +++--------------- .../infer.sh | 70 ++++++++++++ .../utils | 1 + 6 files changed, 125 insertions(+), 102 deletions(-) create mode 100644 egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/infer.sh create mode 120000 egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/utils diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.py b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.py index 19731912e..6726a4133 100644 --- a/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.py +++ b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.py @@ -16,7 +16,7 @@ def modelscope_infer(args): if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--model', type=str, default="speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch") + 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") parser.add_argument('--output_dir', type=str, default="./results/") parser.add_argument('--batch_size', type=int, default=64) diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.sh b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.sh index ab6484924..f0802575e 100644 --- a/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.sh +++ b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.sh @@ -64,8 +64,8 @@ fi if [ $stage -le 2 ] && [ $stop_stage -ge 2 ];then echo "Computing WER ..." python utils/proce_text.py ${output_dir}/1best_recog/text ${output_dir}/1best_recog/text.proc - python utils/proce_text.py ${data_dir}/text ${data_dir}/text.proc - python utils/compute_wer.py ${data_dir}/text.proc ${output_dir}/1best_recog/text.proc ${output_dir}/1best_recog/text.cer + python utils/proce_text.py ${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 @@ -75,7 +75,7 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ];then ./utils/textnorm_zh.py \ --has_key --to_upper \ ${data_dir}/text \ - ${data_dir}/ref.txt + ${output_dir}/1best_recog/ref.txt echo "$0 --> Normalizing HYP text ..." ./utils/textnorm_zh.py \ @@ -87,7 +87,7 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ];then echo "$0 --> computing WER/CER and alignment ..." ./utils/error_rate_zh \ --tokenizer char \ - --ref ${data_dir}/ref.txt \ + --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 diff --git a/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/README.md b/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/README.md index 1587d3d5d..8bf63e50b 100644 --- a/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/README.md +++ b/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/README.md @@ -6,8 +6,9 @@ - Modify finetune training related parameters in `finetune.py` - output_dir: # result dir - - data_dir: # the dataset dir needs to include files: train/wav.scp, train/text; validation/wav.scp, validation/text. - - batch_bins: # batch size + - data_dir: # the dataset dir needs to include files: `train/wav.scp`, `train/text`; `validation/wav.scp`, `validation/text` + - dataset_type: # for dataset larger than 1000 hours, set as `large`, otherwise set as `small` + - batch_bins: # batch size. For dataset_type is `small`, `batch_bins` indicates the feature frames. For dataset_type is `large`, `batch_bins` indicates the duration in ms - max_epoch: # number of training epoch - lr: # learning rate @@ -20,11 +21,38 @@ Or you can use the finetuned model for inference directly. -- Setting parameters in `infer.py` - - data_dir: # the dataset dir +- Setting parameters in `infer.sh` + - model: # model name on ModelScope + - data_dir: # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed - output_dir: # result dir + - batch_size: # batchsize of inference + - gpu_inference: # whether to perform gpu decoding, set false for cpu decoding + - gpuid_list: # set gpus, e.g., gpuid_list="0,1" + - njob: # the number of jobs for CPU decoding, if `gpu_inference`=false, use CPU decoding, please set `njob` - Then you can run the pipeline to infer with: ```python - python infer.py + sh infer.sh ``` + +- Results + +The decoding results can be found in `$output_dir/1best_recog/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set. + +### Inference using local finetuned model + +- Modify inference related parameters in `infer_after_finetune.py` + - modelscope_model_name: # model name on ModelScope + - output_dir: # result dir + - data_dir: # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed + - decoding_model_name: # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pb` + - batch_size: # batchsize of inference + +- Then you can run the pipeline to finetune with: +```python + python infer_after_finetune.py +``` + +- Results + +The decoding results can be found in `$output_dir/decoding_results/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set. diff --git a/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/infer.py b/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/infer.py index 0b508fbc2..b4f633afd 100644 --- a/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/infer.py +++ b/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/infer.py @@ -1,101 +1,25 @@ import os import shutil -from multiprocessing import Pool - +import argparse from modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks -from funasr.utils.compute_wer import compute_wer - - -def modelscope_infer_core(output_dir, split_dir, njob, idx, batch_size, ngpu, model): - output_dir_job = os.path.join(output_dir, "output.{}".format(idx)) - if ngpu > 0: - use_gpu = 1 - gpu_id = int(idx) - 1 - else: - use_gpu = 0 - gpu_id = -1 - if "CUDA_VISIBLE_DEVICES" in os.environ.keys(): - gpu_list = os.environ['CUDA_VISIBLE_DEVICES'].split(",") - os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_list[gpu_id]) - else: - os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id) - inference_pipline = pipeline( +def modelscope_infer(args): + os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpuid) + inference_pipeline = pipeline( task=Tasks.auto_speech_recognition, - model=model, - output_dir=output_dir_job, - batch_size=batch_size, - ngpu=use_gpu, + model=args.model, + output_dir=args.output_dir, + batch_size=args.batch_size, ) - audio_in = os.path.join(split_dir, "wav.{}.scp".format(idx)) - inference_pipline(audio_in=audio_in) - - -def modelscope_infer(params): - # prepare for multi-GPU decoding - ngpu = params["ngpu"] - njob = params["njob"] - batch_size = params["batch_size"] - output_dir = params["output_dir"] - model = params["model"] - if os.path.exists(output_dir): - shutil.rmtree(output_dir) - os.mkdir(output_dir) - split_dir = os.path.join(output_dir, "split") - os.mkdir(split_dir) - if ngpu > 0: - nj = ngpu - elif ngpu == 0: - nj = njob - wav_scp_file = os.path.join(params["data_dir"], "wav.scp") - with open(wav_scp_file) as f: - lines = f.readlines() - num_lines = len(lines) - num_job_lines = num_lines // nj - start = 0 - for i in range(nj): - end = start + num_job_lines - file = os.path.join(split_dir, "wav.{}.scp".format(str(i + 1))) - with open(file, "w") as f: - if i == nj - 1: - f.writelines(lines[start:]) - else: - f.writelines(lines[start:end]) - start = end - - p = Pool(nj) - for i in range(nj): - p.apply_async(modelscope_infer_core, - args=(output_dir, split_dir, njob, str(i + 1), batch_size, ngpu, model)) - p.close() - p.join() - - # combine decoding results - best_recog_path = os.path.join(output_dir, "1best_recog") - os.mkdir(best_recog_path) - files = ["text", "token", "score"] - for file in files: - with open(os.path.join(best_recog_path, file), "w") as f: - for i in range(nj): - job_file = os.path.join(output_dir, "output.{}/1best_recog".format(str(i + 1)), file) - with open(job_file) as f_job: - lines = f_job.readlines() - f.writelines(lines) - - # If text exists, compute CER - text_in = os.path.join(params["data_dir"], "text") - if os.path.exists(text_in): - text_proc_file = os.path.join(best_recog_path, "token") - compute_wer(text_in, text_proc_file, os.path.join(best_recog_path, "text.cer")) - + inference_pipeline(audio_in=args.audio_in) if __name__ == "__main__": - params = {} - params["model"] = "damo/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1" - params["data_dir"] = "./data/test" - params["output_dir"] = "./results" - params["ngpu"] = 1 # if ngpu > 0, will use gpu decoding - params["njob"] = 1 # if ngpu = 0, will use cpu decoding - params["batch_size"] = 64 - modelscope_infer(params) \ No newline at end of file + parser = argparse.ArgumentParser() + parser.add_argument('--model', type=str, default="damo/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1") + parser.add_argument('--audio_in', type=str, default="./data/test") + parser.add_argument('--output_dir', type=str, default="./results/") + parser.add_argument('--batch_size', type=int, default=64) + parser.add_argument('--gpuid', type=str, default="0") + args = parser.parse_args() + modelscope_infer(args) \ No newline at end of file diff --git a/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/infer.sh b/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/infer.sh new file mode 100644 index 000000000..cdf81dcbf --- /dev/null +++ b/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/infer.sh @@ -0,0 +1,70 @@ +#!/usr/bin/env bash + +set -e +set -u +set -o pipefail + +stage=1 +stop_stage=2 +model="damo/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1" +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=4 # the number of jobs for CPU decoding, if gpu_inference=false, use CPU decoding, please set njob + + +if ${gpu_inference}; then + nj=$(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 [ $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 ..." + python utils/proce_text.py ${output_dir}/1best_recog/text ${output_dir}/1best_recog/text.proc + python utils/proce_text.py ${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 diff --git a/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/utils b/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/utils new file mode 120000 index 000000000..2ac163ff4 --- /dev/null +++ b/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/utils @@ -0,0 +1 @@ +../../../../egs/aishell/transformer/utils \ No newline at end of file