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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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| | | The decoding mode of `fast` and `normal` is fake streaming, which could be used for evaluating of recognition accuracy. |
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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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| | | ##### 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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| | | - `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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| | |
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| | | FunASR also offer recipes [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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| | | - Setting parameters in `infer.sh` |
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| | | - `model`: model name on ModelScope |
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| | | - `data_dir`: 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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| | | - `output_dir`: result dir |
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| | | - `batch_size`: batchsize of inference |
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| | | - `gpu_inference`: whether to perform gpu decoding, set false for cpu decoding |
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| | | - `gpuid_list`: set gpus, e.g., `gpuid_list`="0,1" |
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| | | - `njob`: the number of jobs for CPU decoding, if `gpu_inference`=false, use CPU decoding, please set `njob` |
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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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| | | - `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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| | | - `gpu_inference`: `true` (Default), whether to perform gpu decoding, set false for CPU inference |
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| | | - `gpuid_list`: `0,1` (Default), which gpu_ids are used to infer |
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| | | - `njob`: only used for CPU inference (`gpu_inference`=`false`), `64` (Default), the number of jobs for CPU decoding |
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| | | - `checkpoint_dir`: only used for infer finetuned models, the path dir of finetuned models |
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| | | - `checkpoint_name`: only used for infer finetuned models, `valid.cer_ctc.ave.pb` (Default), which checkpoint is used to infer |
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