mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
675b4605e8
* update * update * update * update onnx * update with main (#1492) * contextual&seaco ONNX export (#1481) * contextual&seaco ONNX export * update ContextualEmbedderExport2 * update ContextualEmbedderExport2 * update code * onnx (#1482) * qwenaudio qwenaudiochat * qwenaudio qwenaudiochat * whisper * whisper * llm * llm * llm * llm * llm * llm * llm * llm * export onnx * export onnx * export onnx * dingding * dingding * llm * doc * onnx * onnx * onnx * onnx * onnx * onnx * v1.0.15 * qwenaudio * qwenaudio * issue doc * update * update * bugfix * onnx * update export calling * update codes * remove useless code * update code --------- Co-authored-by: zhifu gao <zhifu.gzf@alibaba-inc.com> * acknowledge --------- Co-authored-by: Shi Xian <40013335+R1ckShi@users.noreply.github.com> * update onnx * update onnx * train update * train update * train update * train update * punc update --------- Co-authored-by: Shi Xian <40013335+R1ckShi@users.noreply.github.com>
47 lines
1.8 KiB
Python
47 lines
1.8 KiB
Python
#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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from funasr import AutoModel
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model = AutoModel(model="iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
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model_revision="v2.0.4",
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# vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
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# vad_model_revision="v2.0.4",
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# punc_model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
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# punc_model_revision="v2.0.4",
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# spk_model="iic/speech_campplus_sv_zh-cn_16k-common",
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# spk_model_revision="v2.0.2",
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)
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# example1
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res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
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hotword='达摩院 魔搭',
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# return_raw_text=True, # return raw text recognition results splited by space of equal length with timestamp
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# preset_spk_num=2, # preset speaker num for speaker cluster model
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# sentence_timestamp=True, # return sentence level information when spk_model is not given
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)
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print(res)
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'''
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# tensor or numpy as input
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# example2
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import torchaudio
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import os
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wav_file = os.path.join(model.model_path, "example/asr_example.wav")
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input_tensor, sample_rate = torchaudio.load(wav_file)
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input_tensor = input_tensor.mean(0)
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res = model.generate(input=[input_tensor], batch_size_s=300, is_final=True)
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# example3
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import soundfile
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wav_file = os.path.join(model.model_path, "example/asr_example.wav")
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speech, sample_rate = soundfile.read(wav_file)
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res = model.generate(input=[speech], batch_size_s=300, is_final=True)
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'''
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