offline-whisper-decode-files.py
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#!/usr/bin/env python3
"""
This file shows how to use a non-streaming whisper model from
https://github.com/openai/whisper
to decode files.
Please download model files from
https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models
For instance,
wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-whisper-tiny.en.tar.bz2
tar xvf sherpa-onnx-whisper-tiny.en.tar.bz2
rm sherpa-onnx-whisper-tiny.en.tar.bz2
"""
import datetime as dt
from pathlib import Path
import sherpa_onnx
import soundfile as sf
def create_recognizer():
encoder = "./sherpa-onnx-whisper-tiny.en/tiny.en-encoder.int8.onnx"
decoder = "./sherpa-onnx-whisper-tiny.en/tiny.en-decoder.int8.onnx"
tokens = "./sherpa-onnx-whisper-tiny.en/tiny.en-tokens.txt"
test_wav = "./sherpa-onnx-whisper-tiny.en/test_wavs/0.wav"
if not Path(encoder).is_file() or not Path(test_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_whisper(
encoder=encoder,
decoder=decoder,
tokens=tokens,
debug=True,
),
test_wav,
)
def main():
recognizer, wave_filename = create_recognizer()
audio, sample_rate = sf.read(wave_filename, dtype="float32", always_2d=True)
audio = audio[:, 0] # only use the first channel
# audio is a 1-D float32 numpy array normalized to the range [-1, 1]
# sample_rate does not need to be 16000 Hz
start_t = dt.datetime.now()
stream = recognizer.create_stream()
stream.accept_waveform(sample_rate, audio)
recognizer.decode_stream(stream)
end_t = dt.datetime.now()
elapsed_seconds = (end_t - start_t).total_seconds()
duration = audio.shape[-1] / sample_rate
rtf = elapsed_seconds / duration
print(stream.result)
print(wave_filename)
print("Text:", stream.result.text)
print(f"Audio duration:\t{duration:.3f} s")
print(f"Elapsed:\t{elapsed_seconds:.3f} s")
print(f"RTF = {elapsed_seconds:.3f}/{duration:.3f} = {rtf:.3f}")
if __name__ == "__main__":
main()