RECCoon 1.0.0: rebrand to Tom Cooks, Parakeet-only, GPLv3, F-Droid-ready

1 files changed, 65 insertions, 0 deletions

diff --git a/tools/whistle-eval/whisper_baseline.py b/tools/whistle-eval/whisper_baseline.py
new file mode 100644
index 0000000..9be7014
--- /dev/null
+++ b/tools/whistle-eval/whisper_baseline.py
@@ -0,0 +1,65 @@
+#!/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())