| | |
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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_pipline = pipeline( |
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| | | inference_pipeline = pipeline( |
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| | | task=Tasks.speech_timestamp, |
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| | | model='damo/speech_timestamp_prediction-v1-16k-offline', |
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| | | output_dir=None) |
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| | | |
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| | | rec_result = inference_pipline( |
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| | | rec_result = inference_pipeline( |
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| | | audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_timestamps.wav', |
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| | | text_in='一 个 东 太 平 洋 国 家 为 什 么 跑 到 西 太 平 洋 来 了 呢',) |
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| | | print(rec_result) |
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| | |
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| | | |
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| | | |
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| | | |
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| | | #### API-reference |
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| | | ##### Define pipeline |
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| | | ### API-reference |
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| | | #### Define pipeline |
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| | | - `task`: `Tasks.speech_timestamp` |
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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` (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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| | | - `batch_size`: `1` (Default), batch size when decoding |
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| | | ##### Infer pipeline |
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| | | #### Infer pipeline |
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| | | - `audio_in`: the input speech to predict, which could be: |
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| | | - wav_path, `e.g.`: asr_example.wav (wav in local or url), |
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| | | - wav.scp, kaldi style wav list (`wav_id wav_path`), `e.g.`: |
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