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| | | model_revision="v1.0.0") |
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| | | |
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| | | |
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| | | # example1 |
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| | | |
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| | | res = model.generate(input=("https://www.modelscope.cn/api/v1/models/iic/LCB-NET/repo?Revision=master&FilePath=example/asr_example.wav","https://www.modelscope.cn/api/v1/models/iic/LCB-NET/repo?Revision=master&FilePath=example/ocr.txt"),data_type=("sound", "text")) |
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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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