111 lines
3.4 KiB
Python
111 lines
3.4 KiB
Python
#!/usr/bin/env python3
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"""
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This file shows how to use a non-streaming Canary 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 4 languages and it is converted from
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https://huggingface.co/nvidia/canary-180m-flash
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It supports automatic speech-to-text recognition (ASR) in 4 languages
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(English, German, French, Spanish) and translation from English to
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German/French/Spanish and from German/French/Spanish to English with or
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without punctuation and capitalization (PnC).
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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-canary-180m-flash-en-es-de-fr-int8/encoder.int8.onnx"
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decoder = "./sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8/decoder.int8.onnx"
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tokens = "./sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8/tokens.txt"
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en_wav = "./sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8/test_wavs/en.wav"
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de_wav = "./sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8/test_wavs/de.wav"
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if not Path(encoder).is_file() or not Path(en_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_nemo_canary(
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encoder=encoder,
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decoder=decoder,
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tokens=tokens,
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debug=True,
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),
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en_wav,
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de_wav,
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)
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def decode(recognizer, samples, sample_rate, src_lang, tgt_lang):
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stream = recognizer.create_stream()
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stream.accept_waveform(sample_rate, samples)
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recognizer.recognizer.set_config(
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config=sherpa_onnx.OfflineRecognizerConfig(
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model_config=sherpa_onnx.OfflineModelConfig(
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canary=sherpa_onnx.OfflineCanaryModelConfig(
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src_lang=src_lang,
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tgt_lang=tgt_lang,
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)
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)
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)
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)
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recognizer.decode_stream(stream)
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return stream.result.text
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def main():
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recognizer, en_wav, de_wav = create_recognizer()
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en_audio, en_sample_rate = sf.read(en_wav, dtype="float32", always_2d=True)
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en_audio = en_audio[:, 0] # only use the first channel
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de_audio, de_sample_rate = sf.read(de_wav, dtype="float32", always_2d=True)
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de_audio = de_audio[:, 0] # only use the first channel
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en_wav_en_result = decode(
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recognizer, en_audio, en_sample_rate, src_lang="en", tgt_lang="en"
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)
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en_wav_es_result = decode(
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recognizer, en_audio, en_sample_rate, src_lang="en", tgt_lang="es"
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)
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en_wav_de_result = decode(
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recognizer, en_audio, en_sample_rate, src_lang="en", tgt_lang="de"
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)
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en_wav_fr_result = decode(
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recognizer, en_audio, en_sample_rate, src_lang="en", tgt_lang="fr"
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)
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de_wav_en_result = decode(
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recognizer, de_audio, de_sample_rate, src_lang="de", tgt_lang="en"
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)
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de_wav_de_result = decode(
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recognizer, de_audio, de_sample_rate, src_lang="de", tgt_lang="de"
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)
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print("en_wav_en_result", en_wav_en_result)
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print("en_wav_es_result", en_wav_es_result)
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print("en_wav_de_result", en_wav_de_result)
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print("en_wav_fr_result", en_wav_fr_result)
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print("-" * 10)
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print("de_wav_en_result", de_wav_en_result)
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print("de_wav_de_result", de_wav_de_result)
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if __name__ == "__main__":
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main()
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