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| | | |
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| | | More examples ref to [docs](https://github.com/alibaba-damo-academy/FunASR/tree/main/examples/industrial_data_pretraining) |
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| | | |
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| | | [//]: # (FunASR supports inference and fine-tuning of models trained on industrial datasets of tens of thousands of hours. For more details, please refer to ([modelscope_egs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)). It also supports training and fine-tuning of models on academic standard datasets. For more details, please refer to([egs](https://alibaba-damo-academy.github.io/FunASR/en/academic_recipe/asr_recipe.html)). The models include speech recognition (ASR), speech activity detection (VAD), punctuation recovery, language model, speaker verification, speaker separation, and multi-party conversation speech recognition. For a detailed list of models, please refer to the [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md):) |
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| | | |
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| | | ## Export ONNX |
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| | | |
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| | | ### Command-line usage |
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| | | ```shell |
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| | | funasr-export ++model=paraformer ++quantize=false ++device=cpu |
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| | | ``` |
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| | | |
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| | | ### Python |
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| | | ```python |
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| | | from funasr import AutoModel |
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| | | |
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| | | model = AutoModel(model="paraformer", device="cpu") |
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| | | |
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| | | res = model.export(quantize=False) |
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| | | ``` |
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| | | |
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| | | ### Text ONNX |
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| | | ```python |
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| | | # pip3 install -U funasr-onnx |
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| | | from funasr_onnx import Paraformer |
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| | | model_dir = "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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| | | |
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| | | wav_path = ['~/.cache/modelscope/hub/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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| | | More examples ref to [demo](runtime/python/onnxruntime) |
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| | | |
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| | | ## Deployment Service |
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| | | FunASR supports deploying pre-trained or further fine-tuned models for service. Currently, it supports the following types of service deployment: |
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| | | |
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| | | You can also scan the following DingTalk group or WeChat group QR code to join the community group for communication and discussion. |
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| | | |
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| | | |DingTalk group | WeChat group | |
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| | | |:---:|:-----------------------------------------------------:| |
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| | | |<div align="left"><img src="docs/images/dingding.jpg" width="250"/> | <img src="docs/images/wechat.png" width="215"/></div> | |
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| | | | DingTalk group | WeChat group | |
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| | | |:-------------------------------------------------------------------:|:-----------------------------------------------------:| |
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| | | | <div align="left"><img src="docs/images/dingding.png" width="250"/> | <img src="docs/images/wechat.png" width="215"/></div> | |
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| | | |
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| | | ## Contributors |
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| | | |
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