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https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
zhifu gao
GitHub
commit
b141ac7990
@ -58,6 +58,22 @@ Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/
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#### [RNN-T-online model]()
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Undo
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#### [MFCCA Model](https://www.modelscope.cn/models/NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/summary)
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For more model detailes, please refer to [docs](https://www.modelscope.cn/models/NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/summary)
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```python
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from modelscope.pipelines import pipeline
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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='NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950',
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model_revision='v3.0.0'
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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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```
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#### API-reference
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##### Define pipeline
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- `task`: `Tasks.auto_speech_recognition`
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@ -1,53 +0,0 @@
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# ModelScope Model
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## How to finetune and infer using a pretrained Paraformer-large Model
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### Finetune
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- Modify finetune training related parameters in `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 files: `train/wav.scp`, `train/text`; `validation/wav.scp`, `validation/text`
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- <strong>dataset_type:</strong> # for dataset larger than 1000 hours, set as `large`, otherwise set as `small`
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- <strong>batch_bins:</strong> # 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
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- <strong>max_epoch:</strong> # number of training epoch
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- <strong>lr:</strong> # learning rate
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- Then you can run the pipeline to finetune with:
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```python
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python finetune.py
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```
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### Inference
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Or you can use the finetuned model for inference directly.
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- Setting parameters in `infer.py`
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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>output_dir:</strong> # result dir
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- <strong>ngpu:</strong> # the number of GPUs for decoding
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- <strong>njob:</strong> # the number of jobs for each GPU
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- Then you can run the pipeline to infer with:
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```python
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python infer.py
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```
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- Results
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The decoding results can be found in `$output_dir/1best_recog/text.sp.cer` and `$output_dir/1best_recog/text.nosp.cer`, which includes recognition results with or without separating character (src) of each sample and the CER metric of the whole test set.
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### Inference using local finetuned model
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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.pb`
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- Then you can run the pipeline to finetune with:
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```python
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python infer_after_finetune.py
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```
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- Results
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The decoding results can be found in `$output_dir/1best_recog/text.sp.cer` and `$output_dir/1best_recog/text.nosp.cer`, which includes recognition results with or without separating character (src) of each sample and the CER metric of the whole test set.
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@ -0,0 +1 @@
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../../TEMPLATE/README.md
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@ -1,102 +1,27 @@
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import os
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import shutil
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from multiprocessing import Pool
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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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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_pipline = pipeline(
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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='NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950',
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model_revision='v3.0.0',
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output_dir=output_dir_job,
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batch_size=1,
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model=args.model,
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model_revision=args.model_revision,
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output_dir=args.output_dir,
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batch_size=args.batch_size,
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)
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audio_in = os.path.join(split_dir, "wav.{}.scp".format(idx))
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inference_pipline(audio_in=audio_in)
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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, "token")
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text_proc_file2 = os.path.join(best_recog_path, "token_nosep")
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with open(text_proc_file, 'r') as hyp_reader:
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with open(text_proc_file2, 'w') as hyp_writer:
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for line in hyp_reader:
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new_context = line.strip().replace("src","").replace(" "," ").replace(" "," ").strip()
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hyp_writer.write(new_context+'\n')
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text_in2 = os.path.join(best_recog_path, "ref_text_nosep")
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with open(text_in, 'r') as ref_reader:
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with open(text_in2, 'w') as ref_writer:
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for line in ref_reader:
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new_context = line.strip().replace("src","").replace(" "," ").replace(" "," ").strip()
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ref_writer.write(new_context+'\n')
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compute_wer(text_in, text_proc_file, os.path.join(best_recog_path, "text.sp.cer"))
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compute_wer(text_in2, text_proc_file2, os.path.join(best_recog_path, "text.nosp.cer"))
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inference_pipeline(audio_in=args.audio_in)
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if __name__ == "__main__":
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params = {}
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params["data_dir"] = "./example_data/validation"
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params["output_dir"] = "./output_dir"
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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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parser = argparse.ArgumentParser()
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parser.add_argument('--model', type=str, default="NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950")
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parser.add_argument('--model_revision', type=str, default="v3.0.0")
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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('--batch_size', type=int, default=1)
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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 +1,70 @@
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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=3
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model="NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950"
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data_dir="./data/test"
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output_dir="./results_pl_gpu"
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batch_size=1
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gpu_inference=true # whether to perform gpu decoding
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gpuid_list="3,4" # set gpus, e.g., gpuid_list="0,1"
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njob=4 # the number of jobs for CPU decoding, if gpu_inference=false, use CPU decoding, please set njob
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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 [ $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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./utils/run.pl JOB=1:${nj} ${output_dir}/log/infer.JOB.log \
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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_list_array[JOB-1]}
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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/token ${output_dir}/1best_recog/text.proc
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cp ${data_dir}/text ${output_dir}/1best_recog/text.ref
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sed -e 's/src//g' ${output_dir}/1best_recog/text.proc | sed -e 's/ \+/ /g' > ${output_dir}/1best_recog/text_nosp.proc
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sed -e 's/src//g' ${output_dir}/1best_recog/text.ref | sed -e 's/ \+/ /g' > ${output_dir}/1best_recog/text_nosp.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.sp.cer
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tail -n 3 ${output_dir}/1best_recog/text.sp.cer
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python utils/compute_wer.py ${output_dir}/1best_recog/text_nosp.ref ${output_dir}/1best_recog/text_nosp.proc ${output_dir}/1best_recog/text.nosp.cer
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tail -n 3 ${output_dir}/1best_recog/text.nosp.cer
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fi
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@ -0,0 +1 @@
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../../../../egs/aishell/transformer/utils
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