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

1 files changed, 141 insertions, 0 deletions

diff --git a/tools/whistle-eval/evaluate.py b/tools/whistle-eval/evaluate.py
new file mode 100644
index 0000000..f4437ea
--- /dev/null
+++ b/tools/whistle-eval/evaluate.py
@@ -0,0 +1,141 @@
+#!/usr/bin/env python3
+"""Compare Cactus Whistle with the app's Whisper models on the same clips.
+
+Usage:
+    python evaluate.py samples/jfk.wav en
+    python evaluate.py samples/*.wav        # language guessed from a *.it.wav suffix
+
+For every clip this runs:
+  * Whisper tiny (int8, multilingual) through sherpa-onnx
+  * Whisper base (int8, multilingual) through sherpa-onnx
+  * Cactus Whistle through its reference Node pipeline
+and prints the transcript plus the word error rate (WER) against
+`samples/<name>.txt` when that reference file exists.
+
+Setup (see README.md):
+    npm install
+    node build-data.mjs
+    python3 -m venv .venv && .venv/bin/pip install sherpa-onnx numpy
+"""
+import argparse
+import glob
+import os
+import re
+import subprocess
+import sys
+import unicodedata
+import wave
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+VENV = os.environ.get("WHISTLE_EVAL_PYTHON", sys.executable)
+
+
+def normalize(text: str) -> list:
+    text = unicodedata.normalize("NFKD", text.lower())
+    text = "".join(c for c in text if not unicodedata.combining(c))
+    text = re.sub(r"[^a-z0-9]+", " ", text)
+    return text.split()
+
+
+def wer(reference: list, hypothesis: list) -> float:
+    if not reference:
+        return 0.0 if not hypothesis else 1.0
+    previous = list(range(len(hypothesis) + 1))
+    for i, ref in enumerate(reference, start=1):
+        current = [i]
+        for j, hyp in enumerate(hypothesis, start=1):
+            cost = 0 if ref == hyp else 1
+            current.append(min(previous[j] + 1, current[j - 1] + 1, previous[j - 1] + cost))
+        previous = current
+    return previous[-1] / len(reference)
+
+
+def read_reference(audio: str):
+    base = os.path.splitext(audio)[0]
+    for candidate in (base + ".txt", audio + ".txt"):
+        if os.path.isfile(candidate):
+            with open(candidate, encoding="utf-8") as handle:
+                return handle.read().strip()
+    return None
+
+
+def run_whistle(audio: str) -> str:
+    out = subprocess.run(
+        ["node", os.path.join(HERE, "vendor", "js", "example.mjs"), audio],
+        cwd=HERE, capture_output=True, text=True, timeout=600)
+    if out.returncode != 0:
+        return f"<whistle failed: {out.stderr.strip().splitlines()[-1] if out.stderr else '?'}>"
+    first = out.stdout.strip().splitlines()[0] if out.stdout.strip() else ""
+    return re.sub(r"^\[[a-z]{2}\]\s*", "", first)
+
+
+def parakeet_available() -> bool:
+    return os.path.isfile(os.path.join(
+        HERE, "models", "parakeet-v3-int8", "encoder.int8.onnx"))
+
+
+def run_parakeet(audio: str) -> str:
+    script = os.path.join(HERE, "parakeet_baseline.py")
+    out = subprocess.run(
+        [VENV, script, audio], capture_output=True, text=True, timeout=1200)
+    if out.returncode != 0:
+        tail = out.stderr.strip().splitlines()
+        return f"<parakeet failed: {tail[-1] if tail else '?'}>"
+    return out.stdout.strip()
+
+
+def run_whisper(audio: str, size: str, language: str) -> str:
+    script = os.path.join(HERE, "whisper_baseline.py")
+    out = subprocess.run(
+        [VENV, script, audio, size, language],
+        capture_output=True, text=True, timeout=900)
+    if out.returncode != 0:
+        tail = out.stderr.strip().splitlines()
+        return f"<whisper {size} failed: {tail[-1] if tail else '?'}>"
+    return out.stdout.strip()
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("clips", nargs="+")
+    parser.add_argument("language", nargs="?", default=None)
+    args = parser.parse_args()
+
+    clips = []
+    for pattern in args.clips:
+        clips.extend(sorted(glob.glob(pattern)) or [pattern])
+
+    print(f"{'clip':<16} {'model':<14} {'WER':>7}  transcript")
+    print("-" * 100)
+    for audio in clips:
+        if not os.path.isfile(audio):
+            print(f"{audio}: not found")
+            continue
+        language = args.language
+        name = os.path.basename(audio).lower()
+        if language is None:
+            language = "it" if (name.startswith("it") or ".it." in name) else "en"
+        reference = read_reference(audio)
+        ref_words = normalize(reference) if reference else None
+
+        results = [
+            ("whistle", run_whistle(audio)),
+            ("whisper tiny", run_whisper(audio, "tiny", language)),
+            ("whisper base", run_whisper(audio, "base", language)),
+        ]
+        if parakeet_available():
+            results.append(("parakeet v3", run_parakeet(audio)))
+        for model, text in results:
+            if ref_words is None:
+                score = "   n/a"
+            else:
+                score = f"{wer(ref_words, normalize(text)) * 100:6.1f}%"
+            print(f"{os.path.basename(audio):<16} {model:<14} {score}  {text}")
+        if reference:
+            print(f"{'':<16} {'reference':<14} {'':>7}  {reference}")
+        print()
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())