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--- |
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license: apache-2.0 |
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--- |
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## Install the `funasr_onnx` |
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```shell |
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pip install -U funasr_onnx |
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# For the users in China, you could install with the command: |
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# pip install -U funasr_onnx -i https://mirror.sjtu.edu.cn/pypi/web/simple |
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``` |
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## Download the model |
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```shell |
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git clone https://huggingface.co/funasr/paraformer-large |
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``` |
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## Inference with runtime |
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### Speech Recognition |
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#### Paraformer |
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```python |
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from funasr_onnx import Paraformer |
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model_dir = "./export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" |
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model = Paraformer(model_dir, batch_size=1, quantize=True) |
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wav_path = ['./export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav'] |
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result = model(wav_path) |
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print(result) |
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``` |
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- `model_dir`: the model path, which contains `model.onnx`, `config.yaml`, `am.mvn` |
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- `batch_size`: `1` (Default), the batch size duration inference |
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- `device_id`: `-1` (Default), infer on CPU. If you want to infer with GPU, set it to gpu_id (Please make sure that you have install the onnxruntime-gpu) |
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- `quantize`: `False` (Default), load the model of `model.onnx` in `model_dir`. If set `True`, load the model of `model_quant.onnx` in `model_dir` |
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- `intra_op_num_threads`: `4` (Default), sets the number of threads used for intraop parallelism on CPU |
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Input: wav formt file, support formats: `str, np.ndarray, List[str]` |
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Output: `List[str]`: recognition result |
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## Performance benchmark |
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Please ref to [benchmark](https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/runtime/python/benchmark_onnx.md) |
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