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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-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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| | | - `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, `e.g.`: asr_example.pcm, |
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| | | - audio bytes stream, `e.g.`: bytes data from a microphone |
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| | | - audio sample point，`e.g.`: `audio, rate = soundfile.read("asr_example_zh.wav")`, the dtype is numpy.ndarray or torch.Tensor |
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| | | - wav.scp, kaldi style wav list (`wav_id \t wav_path``), `e.g.`: |
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| | | ```cat wav.scp |
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| | | asr_example1 ./audios/asr_example1.wav |
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| | | asr_example2 ./audios/asr_example2.wav |
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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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| | | |
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| | | #### Inference with you data |
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