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@@ -1,7 +1,7 @@
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# Speech Recognition
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> **Note**:
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-> The modelscope pipeline supports all the models in [model zoo] to inference and finetine. Here we take model of Paraformer and Paraformer-online as example to demonstrate the usage.
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+> The modelscope pipeline supports all the models in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope) to inference and finetine. Here we take model of Paraformer and Paraformer-online as example to demonstrate the usage.
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## Inference
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@@ -33,14 +33,31 @@ chunk_stride = 7680# 480ms
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# first chunk, 480ms
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speech_chunk = speech[0:chunk_stride]
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rec_result = inference_pipeline(audio_in=speech_chunk, param_dict=param_dict)
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+print(rec_result)
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# next chunk, 480ms
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speech_chunk = speech[chunk_stride:chunk_stride+chunk_stride]
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rec_result = inference_pipeline(audio_in=speech_chunk, param_dict=param_dict)
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-
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print(rec_result)
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```
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Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/241)
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+#### [UniASR model](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/summary)
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+There are three decoding mode for UniASR model(`fast`、`normal`、`offline`), for more model detailes, please refer to [docs](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/summary)
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+```python
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+decoding_model = "fast" # "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-minnan-16k-common-vocab3825',
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+ param_dict={"decoding_model": decoding_model})
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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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+The decoding mode of `fast` and `normal`
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+Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/151)
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+#### [RNN-T-online model]()
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+Undo
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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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@@ -62,19 +79,118 @@ Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/
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```
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In this case of `wav.scp` input, `output_dir` must be set to save the output results
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- `audio_fs`: audio sampling rate, only set when audio_in is pcm audio
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+- `output_dir`: None (Defalut), the output path of results if set
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+### Inference with multi-thread CPUs or multi GPUs
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+FunASR also offer recipes [run.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.sh) to decode with multi-thread CPUs, or multi GPUs.
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+
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+- Setting parameters in `infer.sh`
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+ - <strong>model:</strong> # model name on ModelScope
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+ - <strong>data_dir:</strong> # the dataset dir needs to include `${data_dir}/wav.scp`. If `${data_dir}/text` is also exists, CER will be computed
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+ - <strong>output_dir:</strong> # result dir
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+ - <strong>batch_size:</strong> # batchsize of inference
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+ - <strong>gpu_inference:</strong> # whether to perform gpu decoding, set false for cpu decoding
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+ - <strong>gpuid_list:</strong> # set gpus, e.g., gpuid_list="0,1"
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+ - <strong>njob:</strong> # the number of jobs for CPU decoding, if `gpu_inference`=false, use CPU decoding, please set `njob`
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+
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+- Decode with multi GPUs:
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+```shell
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+ bash infer.sh \
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+ --model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
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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 \
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+ --gpuid_list "0,1"
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+```
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+- Decode with multi-thread CPUs:
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+```shell
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+ bash infer.sh \
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+ --model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
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+ --data_dir "./data/test" \
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+ --output_dir "./results" \
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+ --gpu_inference false \
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+ --njob 64
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+```
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+
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+- Results
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-### Inference with you data
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+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.
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-### Inference with multi-threads on CPU
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+If you decode the SpeechIO test sets, you can use textnorm with `stage`=3, and `DETAILS.txt`, `RESULTS.txt` record the results and CER after text normalization.
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-### Inference with multi GPU
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## Finetune with pipeline
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### Quick start
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+[finetune.py](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/finetune.py)
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+```python
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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 modelscope.msdatasets.audio.asr_dataset import ASRDataset
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+
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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 = ASRDataset.load(params.data_path, namespace='speech_asr')
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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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+
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+
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+if __name__ == '__main__':
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+ from funasr.utils.modelscope_param import modelscope_args
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+ params = modelscope_args(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
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+ params.output_dir = "./checkpoint" # 模型保存路径
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+ params.data_path = "speech_asr_aishell1_trainsets" # 数据路径，可以为modelscope中已上传数据，也可以是本地数据
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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 = 2000 # 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 = 50 # 最大训练轮数
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+ params.lr = 0.00005 # 设置学习率
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+
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+ modelscope_finetune(params)
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+```
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+
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+```shell
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+python finetune.py &> log.txt &
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+```
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### Finetune with your data
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+- Modify finetune training related parameters in [finetune.py](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/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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+
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+- Then you can run the pipeline to finetune with:
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+```shell
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+python finetune.py
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+```
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+If you want finetune with multi-GPUs, you could:
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+```shell
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+CUDA_VISIBLE_DEVICES=1,2 python -m torch.distributed.launch --nproc_per_node 2 finetune.py > log.txt 2>&1
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+```
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## Inference with your finetuned model
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-
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+- Modify inference related parameters in `infer_after_finetune.py`
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+ - <strong>modelscope_model_name: </strong> # model name on ModelScope
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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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+ - <strong>batch_size:</strong> # batchsize of inference
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+
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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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游雁
