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Add a standalone long-form transcription demo.
Uses WAV file speech to display Moonshine model transcriptions as a single transcript on the console. Adds a 1.5 minute WAV file.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,86 @@ | ||
| """WAV file long-form transcription with Moonshine ONNX models.""" | ||
| import argparse | ||
| import os | ||
| import sys | ||
| import time | ||
| import wave | ||
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| import numpy as np | ||
| import tokenizers | ||
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| from silero_vad import get_speech_timestamps, load_silero_vad | ||
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| MOONSHINE_DEMO_DIR = os.path.dirname(__file__) | ||
| sys.path.append(os.path.join(MOONSHINE_DEMO_DIR, "..")) | ||
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| from onnx_model import MoonshineOnnxModel | ||
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| def main(model_name, wav_path): | ||
| model = MoonshineOnnxModel(model_name=model_name) | ||
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| tokenizer = tokenizers.Tokenizer.from_file( | ||
| os.path.join(MOONSHINE_DEMO_DIR, "..", "assets", "tokenizer.json") | ||
| ) | ||
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| with wave.open(wav_path) as f: | ||
| params = f.getparams() | ||
| assert ( | ||
| params.nchannels == 1 and | ||
| params.framerate == 16000 and | ||
| params.sampwidth == 2 | ||
| ), f"WAV file must have 1 channel, 16KHz rate, and int16 precision." | ||
| audio = f.readframes(params.nframes) | ||
| audio = np.frombuffer(audio, np.int16) / np.iinfo(np.int16).max | ||
| audio = audio.astype(np.float32) | ||
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| vad_model = load_silero_vad() | ||
| speech_timestamps = get_speech_timestamps( | ||
| audio, | ||
| vad_model, | ||
| max_speech_duration_s=30, | ||
| min_silence_duration_ms=2000, | ||
| min_speech_duration_ms=250, | ||
| speech_pad_ms=400, | ||
| ) | ||
| chunks = [audio[ts['start']:ts['end']] for ts in speech_timestamps] | ||
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| chunks_length = 0 | ||
| transcription = "" | ||
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| start_time = time.time() | ||
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| for chunk in chunks: | ||
| tokens = model.generate(chunk[None, ...]) | ||
| transcription += tokenizer.decode_batch(tokens)[0] + " " | ||
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| chunks_length += len(chunk) | ||
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| time_took = time.time() - start_time | ||
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| print(f""" | ||
| {transcription} | ||
| model realtime factor: {((chunks_length / 16000) / time_took):.2f}x | ||
| """) | ||
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| if __name__ == "__main__": | ||
| parser = argparse.ArgumentParser( | ||
| prog="file_transcription.py", | ||
| description="Standalone file transcription with Moonshine ONNX models.", | ||
| ) | ||
| parser.add_argument( | ||
| "--model_name", | ||
| help="Model to run the demo with.", | ||
| default="moonshine/base", | ||
| choices=["moonshine/base", "moonshine/tiny"], | ||
| ) | ||
| parser.add_argument( | ||
| "--wav_path", | ||
| help="Path to speech WAV file.", | ||
| default=os.path.join( | ||
| MOONSHINE_DEMO_DIR, "..", "assets", "a_tale_of_two_cities.wav"), | ||
| ) | ||
| args = parser.parse_args() | ||
| main(**vars(args)) |