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| | | from funasr import AutoModel |
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| | | model = AutoModel(model="damo/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", |
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| | | model_revision="v2.0.2", |
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| | | vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", |
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| | | vad_model_revision="v2.0.2", |
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| | | punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch", |
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| | | punc_model_revision="v2.0.2", |
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| | | spk_model="damo/speech_campplus_sv_zh-cn_16k-common", |
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| | | spk_model="v2.0.2", |
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| | | ) |
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| | | model = AutoModel( |
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| | | model="iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", |
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| | | # vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch", |
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| | | # punc_model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch", |
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| | | # spk_model="iic/speech_campplus_sv_zh-cn_16k-common", |
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| | | ) |
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| | | |
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| | | res = model(input=f"{model.model_path}/example/asr_example.wav", |
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| | | hotword='达摩院 魔搭') |
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| | | print(res) |
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| | | |
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| | | # example1 |
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| | | res = model.generate( |
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| | | 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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| | | |
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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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| | | |
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| | | |
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| | | # example3 |
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| | | import soundfile |
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| | | |
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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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