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#!/usr/bin/env python3
"""Run the same sherpa-onnx multilingual Whisper model that the app uses.
Usage:
python whisper_baseline.py <audio.wav> [tiny|base] [language]
Requires a virtualenv with `sherpa-onnx` installed (see README.md). The model
files are expected under ./models/<size>/.
"""
import argparse
import os
import sys
import wave
import numpy as np
import sherpa_onnx
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument("audio")
parser.add_argument("size", nargs="?", default="base", choices=["tiny", "base"])
parser.add_argument("language", nargs="?", default="en")
args = parser.parse_args()
model_dir = os.path.join(os.path.dirname(__file__), "models", args.size)
encoder = os.path.join(model_dir, "encoder.int8.onnx")
decoder = os.path.join(model_dir, "decoder.int8.onnx")
tokens = os.path.join(model_dir, "tokens.txt")
for path in (encoder, decoder, tokens):
if not os.path.isfile(path):
print(f"missing {path}", file=sys.stderr)
return 2
recognizer = sherpa_onnx.OfflineRecognizer.from_whisper(
encoder=encoder,
decoder=decoder,
tokens=tokens,
language=args.language,
task="transcribe",
num_threads=2,
decoding_method="greedy_search",
debug=False,
)
with wave.open(args.audio, "rb") as wav:
sample_rate = wav.getframerate()
channels = wav.getnchannels()
width = wav.getsampwidth()
frames = wav.readframes(wav.getnframes())
if width != 2:
print("only 16-bit PCM WAV is supported", file=sys.stderr)
return 2
samples = np.frombuffer(frames, dtype=np.int16).astype(np.float32) / 32768.0
if channels > 1:
samples = samples.reshape(-1, channels).mean(axis=1)
stream = recognizer.create_stream()
stream.accept_waveform(sample_rate, samples)
recognizer.decode_stream(stream)
print(stream.result.text)
return 0
if __name__ == "__main__":
raise SystemExit(main())
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