74 lines
3.0 KiB
Python
Executable File
74 lines
3.0 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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This file shows how to use a non-streaming transducer model from NeMo
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to decode files.
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Please download model files from
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https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
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The example model supports 10 languages and it is converted from
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https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_multilingual_fastconformer_hybrid_large_pc
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"""
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from pathlib import Path
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import sherpa_onnx
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import soundfile as sf
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def create_recognizer():
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encoder = "./sherpa-onnx-nemo-fast-conformer-transducer-be-de-en-es-fr-hr-it-pl-ru-uk-20k/encoder.onnx"
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decoder = "./sherpa-onnx-nemo-fast-conformer-transducer-be-de-en-es-fr-hr-it-pl-ru-uk-20k/decoder.onnx"
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joiner = "./sherpa-onnx-nemo-fast-conformer-transducer-be-de-en-es-fr-hr-it-pl-ru-uk-20k/joiner.onnx"
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tokens = "./sherpa-onnx-nemo-fast-conformer-transducer-be-de-en-es-fr-hr-it-pl-ru-uk-20k/tokens.txt"
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test_wav = "./sherpa-onnx-nemo-fast-conformer-transducer-be-de-en-es-fr-hr-it-pl-ru-uk-20k/test_wavs/de-german.wav"
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# test_wav = "./sherpa-onnx-nemo-fast-conformer-transducer-be-de-en-es-fr-hr-it-pl-ru-uk-20k/test_wavs/en-english.wav"
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# test_wav = "./sherpa-onnx-nemo-fast-conformer-transducer-be-de-en-es-fr-hr-it-pl-ru-uk-20k/test_wavs/es-spanish.wav"
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# test_wav = "./sherpa-onnx-nemo-fast-conformer-transducer-be-de-en-es-fr-hr-it-pl-ru-uk-20k/test_wavs/fr-french.wav"
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# test_wav = "./sherpa-onnx-nemo-fast-conformer-transducer-be-de-en-es-fr-hr-it-pl-ru-uk-20k/test_wavs/hr-croatian.wav"
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# test_wav = "./sherpa-onnx-nemo-fast-conformer-transducer-be-de-en-es-fr-hr-it-pl-ru-uk-20k/test_wavs/it-italian.wav"
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# test_wav = "./sherpa-onnx-nemo-fast-conformer-transducer-be-de-en-es-fr-hr-it-pl-ru-uk-20k/test_wavs/po-polish.wav"
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# test_wav = "./sherpa-onnx-nemo-fast-conformer-transducer-be-de-en-es-fr-hr-it-pl-ru-uk-20k/test_wavs/ru-russian.wav"
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# test_wav = "./sherpa-onnx-nemo-fast-conformer-transducer-be-de-en-es-fr-hr-it-pl-ru-uk-20k/test_wavs/uk-ukrainian.wav"
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if not Path(encoder).is_file() or not Path(test_wav).is_file():
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raise ValueError(
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"""Please download model files from
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https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
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"""
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)
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return (
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sherpa_onnx.OfflineRecognizer.from_transducer(
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encoder=encoder,
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decoder=decoder,
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joiner=joiner,
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tokens=tokens,
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model_type="nemo_transducer",
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debug=True,
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),
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test_wav,
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)
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def main():
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recognizer, wave_filename = create_recognizer()
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audio, sample_rate = sf.read(wave_filename, dtype="float32", always_2d=True)
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audio = audio[:, 0] # only use the first channel
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# audio is a 1-D float32 numpy array normalized to the range [-1, 1]
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# sample_rate does not need to be 16000 Hz
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stream = recognizer.create_stream()
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stream.accept_waveform(sample_rate, audio)
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recognizer.decode_stream(stream)
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print(wave_filename)
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print(stream.result)
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if __name__ == "__main__":
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main()
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