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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](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope) to inference and finetine. Here we take the typic models as examples 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/model_zoo/modelscope_models.html#pretrained-models-on-modelscope) to inference and finetine. Here we take the typic models as examples to demonstrate the usage. |
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| | | |
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| | | ## Inference |
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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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| | | There are three decoding mode for UniASR model(`fast`、`normal`、`offline`), for more model details, 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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| | | 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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| | | For more model details, 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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| | | ### API-reference |
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| | | #### Define pipeline |
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| | | - `task`: `Tasks.auto_speech_recognition` |
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| | | - `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk |
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| | | - `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk |
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| | | - `ngpu`: `1` (Default), decoding on GPU. If ngpu=0, decoding on CPU |
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| | | - `ncpu`: `1` (Default), sets the number of threads used for intraop parallelism on CPU |
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| | | - `output_dir`: `None` (Default), the output path of results if set |
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| | | FunASR also offer recipes [egs_modelscope/asr/TEMPLATE/infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/infer.sh) to decode with multi-thread CPUs, or multi GPUs. |
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| | | |
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| | | #### Settings of `infer.sh` |
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| | | - `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk |
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| | | - `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/model_zoo/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk |
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| | | - `data_dir`: the dataset dir needs to include `wav.scp`. If `${data_dir}/text` is also exists, CER will be computed |
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| | | - `output_dir`: output dir of the recognition results |
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| | | - `batch_size`: `64` (Default), batch size of inference on gpu |
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