mirror of
https://github.com/k2-fsa/sherpa-onnx.git
synced 2026-11-10 21:11:03 +08:00
130 lines
4.0 KiB
Python
Executable File
130 lines
4.0 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 Omnilingual ASR CTC model from
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https://github.com/facebookresearch/omnilingual-asr
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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-omnilingual-asr-1600-languages-300M-ctc-int8-2025-11-12.tar.bz2
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tar xvf sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-int8-2025-11-12.tar.bz2
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rm sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-int8-2025-11-12.tar.bz2
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"""
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from pathlib import Path
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import numpy as np
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import time
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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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model = "./sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-int8-2025-11-12/model.int8.onnx"
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tokens = "./sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-int8-2025-11-12/tokens.txt"
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test_wav_en = "./sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-int8-2025-11-12/test_wavs/en.wav"
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test_wav_de = "./sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-int8-2025-11-12/test_wavs/de.wav"
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test_wav_fr = "./sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-int8-2025-11-12/test_wavs/fr.wav"
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test_wav_es = "./sherpa-onnx-omnilingual-asr-1600-languages-300M-ctc-int8-2025-11-12/test_wavs/es.wav"
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for f in [model, tokens, test_wav_en, test_wav_de, test_wav_fr, test_wav_es]:
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if not Path(f).is_file():
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print(f"{f} does not exist")
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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_omnilingual_asr_ctc(
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model=model,
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tokens=tokens,
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num_threads=1,
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),
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test_wav_en,
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test_wav_de,
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test_wav_fr,
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test_wav_es,
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)
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def load_audio(filename):
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audio, sample_rate = sf.read(filename, dtype="float32", always_2d=True)
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audio = audio[:, 0] # only use the first channel
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return np.ascontiguousarray(audio), sample_rate
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def decode_single_file(recognizer, filename):
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samples, sample_rate = load_audio(filename)
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start_time = time.time()
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stream = recognizer.create_stream()
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stream.accept_waveform(sample_rate=sample_rate, waveform=samples)
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recognizer.decode_stream(stream)
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end_time = time.time()
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elapsed_seconds = end_time - start_time
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audio_duration = len(samples) / sample_rate
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real_time_factor = elapsed_seconds / audio_duration
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print("---")
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print(filename)
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print(stream.result)
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print(f"Elapsed seconds: {elapsed_seconds:.3f}")
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print(f"Audio duration in seconds: {audio_duration:.3f}")
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print(f"RTF: {elapsed_seconds:.3f}/{audio_duration:.3f} = {real_time_factor:.3f}")
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print()
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def decode_multiple_files(recognizer, filenames):
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streams = []
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start_time = time.time()
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audio_duration = 0
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for filename in filenames:
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samples, sample_rate = load_audio(filename)
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audio_duration += len(samples) / sample_rate
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stream = recognizer.create_stream()
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stream.accept_waveform(sample_rate=sample_rate, waveform=samples)
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streams.append(stream)
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recognizer.decode_streams(streams)
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end_time = time.time()
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elapsed_seconds = end_time - start_time
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real_time_factor = elapsed_seconds / audio_duration
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for name, stream in zip(filenames, streams):
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print("---")
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print(name)
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print(stream.result)
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print()
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print(f"Elapsed seconds: {elapsed_seconds:.3f}")
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print(f"Audio duration in seconds: {audio_duration:.3f}")
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print(f"RTF: {elapsed_seconds:.3f}/{audio_duration:.3f} = {real_time_factor:.3f}")
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print()
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print()
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def main():
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recognizer, *filenames = create_recognizer()
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decode_single_file(recognizer, filenames[0])
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decode_single_file(recognizer, filenames[1])
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decode_multiple_files(recognizer, filenames[2:])
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if __name__ == "__main__":
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main()
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