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| | | ## Using paraformer with ONNXRuntime |
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| | | <p align="left"> |
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| | | <a href=""><img src="https://img.shields.io/badge/Python->=3.7,<=3.10-aff.svg"></a> |
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| | | <a href=""><img src="https://img.shields.io/badge/OS-Linux%2C%20Win%2C%20Mac-pink.svg"></a> |
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| | | </p> |
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| | | |
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| | | ### Introduction |
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| | | - Model comes from [speech_paraformer](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary). |
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| | | |
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| | | |
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| | | ### Steps: |
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| | | 1. Export the model. |
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| | | |
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| | | `Tips`: torch 1.11.0 is required. |
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| | | |
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| | | ```shell |
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| | | python -m funasr.export.export_model [model_name] [export_dir] [true] |
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| | | ``` |
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| | | `model_name`: the model is to export. |
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| | | |
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| | | `export_dir`: the dir where the onnx is export. |
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| | | |
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| | | More details ref to ([export docs](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/export)) |
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| | | |
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| | | - `e.g.`, Export model from modelscope |
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| | | ```shell |
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| | | python -m funasr.export.export_model 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" true |
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| | | ``` |
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| | | - `e.g.`, Export model from local path, the model'name must be `model.pb`. |
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| | | ```shell |
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| | | python -m funasr.export.export_model '/mnt/workspace/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch' "./export" true |
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| | | ``` |
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| | | |
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| | | |
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| | | 2. Install the `rapid_paraformer` |
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| | | - Build the rapid_paraformer `whl` |
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| | | ```shell |
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| | | git clone https://github.com/alibaba/FunASR.git && cd FunASR |
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| | | cd funasr/runtime/python/onnxruntime/rapid_paraformer |
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| | | python setup.py bdist_wheel |
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| | | ``` |
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| | | - Install the build `whl` |
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| | | ```bash |
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| | | pip install dist/rapid_paraformer-0.0.1-py3-none-any.whl |
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| | | ``` |
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| | | |
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| | | 3. Run the demo. |
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| | | - Model_dir: the model path, which contains `model.onnx`, `config.yaml`, `am.mvn`. |
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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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| | | - Example: |
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| | | ```python |
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| | | from rapid_paraformer import Paraformer |
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| | | |
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| | | model_dir = "/nfs/zhifu.gzf/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" |
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| | | model = Paraformer(model_dir, batch_size=1) |
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| | | wav_path = ['/nfs/zhifu.gzf/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav'] |
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| | | |
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| | | result = model(wav_path) |
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| | | print(result) |
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| | | ``` |
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| | | |
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| | | ## Speed |
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| | | |
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| | | Environment：Intel(R) Xeon(R) Platinum 8163 CPU @ 2.50GHz |
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| | | |
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| | | Test [wav, 5.53s, 100 times avg.](https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav) |
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| | | |
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| | | | Backend | RTF | |
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| | | |:-------:|:-----------------:| |
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| | | | Pytorch | 0.110 | |
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| | | | Onnx | 0.038 | |
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| | | |
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| | | ## Acknowledge |
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| | | 1. We acknowledge [SWHL](https://github.com/RapidAI/RapidASR) for contributing the onnxruntime(python api). |
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