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| | | ## Inference |
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| | | |
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| | | ### Quick start |
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| | | #### Paraformer model |
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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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| | | |
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| | | inference_pipeline = pipeline( |
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| | | task=Tasks.auto_speech_recognition, |
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| | | model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch', |
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| | | ) |
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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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| | | |
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| | | #### API-docs |
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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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| | | - `ngpu`: 1 (Defalut), decoding on GPU. If ngpu=0, decoding on CPU |
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| | | - `ncpu`: 1 (Defalut), sets the number of threads used for intraop parallelism on CPU |
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| | | - `output_dir`: None (Defalut), the output path of results if set |
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| | | - `batch_size`: 1 (Defalut), batch size when decoding |
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| | | ##### infer pipeline |
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| | | - `audio_in`: the input to decode, which could be: |
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| | | - wav_path, `e.g.`: asr_example.wav, |
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| | | - pcm_path, |
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| | | - audio bytes stream |
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| | | - audio sample point |
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| | | - wav.scp |
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| | | |
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| | | #### Inference with you data |
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| | | |
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