offline-nemo-canary-decode-files.py
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#!/usr/bin/env python3
"""
This file shows how to use a non-streaming Canary model from NeMo
to decode files.
Please download model files from
https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
The example model supports 4 languages and it is converted from
https://huggingface.co/nvidia/canary-180m-flash
It supports automatic speech-to-text recognition (ASR) in 4 languages
(English, German, French, Spanish) and translation from English to
German/French/Spanish and from German/French/Spanish to English with or
without punctuation and capitalization (PnC).
"""
from pathlib import Path
import sherpa_onnx
import soundfile as sf
def create_recognizer():
encoder = "./sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8/encoder.int8.onnx"
decoder = "./sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8/decoder.int8.onnx"
tokens = "./sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8/tokens.txt"
en_wav = "./sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8/test_wavs/en.wav"
de_wav = "./sherpa-onnx-nemo-canary-180m-flash-en-es-de-fr-int8/test_wavs/de.wav"
if not Path(encoder).is_file() or not Path(en_wav).is_file():
raise ValueError(
"""Please download model files from
https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
"""
)
return (
sherpa_onnx.OfflineRecognizer.from_nemo_canary(
encoder=encoder,
decoder=decoder,
tokens=tokens,
debug=True,
),
en_wav,
de_wav,
)
def decode(recognizer, samples, sample_rate, src_lang, tgt_lang):
stream = recognizer.create_stream()
stream.accept_waveform(sample_rate, samples)
recognizer.recognizer.set_config(
config=sherpa_onnx.OfflineRecognizerConfig(
model_config=sherpa_onnx.OfflineModelConfig(
canary=sherpa_onnx.OfflineCanaryModelConfig(
src_lang=src_lang,
tgt_lang=tgt_lang,
)
)
)
)
recognizer.decode_stream(stream)
return stream.result.text
def main():
recognizer, en_wav, de_wav = create_recognizer()
en_audio, en_sample_rate = sf.read(en_wav, dtype="float32", always_2d=True)
en_audio = en_audio[:, 0] # only use the first channel
de_audio, de_sample_rate = sf.read(de_wav, dtype="float32", always_2d=True)
de_audio = de_audio[:, 0] # only use the first channel
en_wav_en_result = decode(
recognizer, en_audio, en_sample_rate, src_lang="en", tgt_lang="en"
)
en_wav_es_result = decode(
recognizer, en_audio, en_sample_rate, src_lang="en", tgt_lang="es"
)
en_wav_de_result = decode(
recognizer, en_audio, en_sample_rate, src_lang="en", tgt_lang="de"
)
en_wav_fr_result = decode(
recognizer, en_audio, en_sample_rate, src_lang="en", tgt_lang="fr"
)
de_wav_en_result = decode(
recognizer, de_audio, de_sample_rate, src_lang="de", tgt_lang="en"
)
de_wav_de_result = decode(
recognizer, de_audio, de_sample_rate, src_lang="de", tgt_lang="de"
)
print("en_wav_en_result", en_wav_en_result)
print("en_wav_es_result", en_wav_es_result)
print("en_wav_de_result", en_wav_de_result)
print("en_wav_fr_result", en_wav_fr_result)
print("-" * 10)
print("de_wav_en_result", de_wav_en_result)
print("de_wav_de_result", de_wav_de_result)
if __name__ == "__main__":
main()