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https://github.com/k2-fsa/sherpa-onnx.git
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36ef8d1fbc
This PR adds C++ and Python API support for Omnilingual ASR models with CTC decoding, enabling speech recognition across 1600+ languages using models from Facebook Research's omnilingual-asr project. Key changes: - Implements OfflineOmnilingualAsrCtcModel class with feature normalization and model inference - Adds Python bindings through from_omnilingual_asr_ctc() method on OfflineRecognizer - Integrates omnilingual models into existing CTC recognition pipeline with special handling for input shapes and frame shifts
83 lines
2.5 KiB
Python
Executable File
83 lines
2.5 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 Moonshine model from
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https://github.com/usefulsensors/moonshine
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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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For instance,
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wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-moonshine-tiny-en-int8.tar.bz2
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tar xvf sherpa-onnx-moonshine-tiny-en-int8.tar.bz2
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rm sherpa-onnx-moonshine-tiny-en-int8.tar.bz2
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"""
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import datetime as dt
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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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preprocessor = "./sherpa-onnx-moonshine-tiny-en-int8/preprocess.onnx"
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encoder = "./sherpa-onnx-moonshine-tiny-en-int8/encode.int8.onnx"
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uncached_decoder = "./sherpa-onnx-moonshine-tiny-en-int8/uncached_decode.int8.onnx"
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cached_decoder = "./sherpa-onnx-moonshine-tiny-en-int8/cached_decode.int8.onnx"
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tokens = "./sherpa-onnx-moonshine-tiny-en-int8/tokens.txt"
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test_wav = "./sherpa-onnx-moonshine-tiny-en-int8/test_wavs/0.wav"
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if not Path(preprocessor).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_moonshine(
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preprocessor=preprocessor,
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encoder=encoder,
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uncached_decoder=uncached_decoder,
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cached_decoder=cached_decoder,
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tokens=tokens,
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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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start_t = dt.datetime.now()
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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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end_t = dt.datetime.now()
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elapsed_seconds = (end_t - start_t).total_seconds()
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duration = audio.shape[-1] / sample_rate
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rtf = elapsed_seconds / duration
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print(stream.result)
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print(wave_filename)
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print("Text:", stream.result.text)
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print(f"Audio duration:\t{duration:.3f} s")
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print(f"Elapsed:\t{elapsed_seconds:.3f} s")
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print(f"RTF = {elapsed_seconds:.3f}/{duration:.3f} = {rtf:.3f}")
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if __name__ == "__main__":
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main()
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