Added chatterbox connections

labs/glitch_odin/prepare_teddy_dataset.py

@@ -0,0 +1,86 @@
+"""Create conservative draft Piper clips using local ASR word timestamps.
+Run with .training/venv/Scripts/python.exe. No audio is uploaded.
+Only sentences matching the supplied transcript exactly after normalization
+are exported. Review timing and excluded sentences before training.
+"""
+import difflib
+import json
+import re
+from pathlib import Path
+import numpy as np
+from scipy.io import wavfile
+from scipy.signal import resample_poly
+from faster_whisper import WhisperModel
+
+ROOT = Path(__file__).resolve().parent / ".training" / "teddy-v1"
+def tokens(text):
+    return re.findall(r"[a-z0-9]+(?:'[a-z]+)?", text.lower().replace("’", "'"))
+
+def main():
+    text = (ROOT / "teddy-transcript.txt").read_text(encoding="utf-8-sig")
+    cache = ROOT / "asr-words.json"
+    if cache.exists():
+        words = json.loads(cache.read_text(encoding="utf-8"))
+    else:
+        model = WhisperModel("small.en", device="cpu", compute_type="int8", cpu_threads=8,
+                             download_root=str(ROOT.parent / "models"))
+        rate, source = wavfile.read(ROOT / "TeddyFull.wav")
+        assert source.ndim == 1 and source.dtype == np.int16
+        recognition_audio = resample_poly(source.astype(np.float32) / 32768, 16000, rate)
+        segments, _ = model.transcribe(recognition_audio, language="en",
+                                       word_timestamps=True, vad_filter=True, beam_size=5)
+        words = []
+        for segment in segments:
+            for word in segment.words:
+                words.append({"text":word.word, "start":word.start, "end":word.end})
+            print(f"Aligned through {segment.end:.1f} seconds", flush=True)
+        cache.write_text(json.dumps(words, indent=2), encoding="utf-8")
+    recognized = []
+    for word in words:
+        for token in tokens(word["text"]):
+            recognized.append(dict(word, token=token))
+    reference = tokens(text)
+    matcher = difflib.SequenceMatcher(None, reference, [w["token"] for w in recognized], autojunk=False)
+    mapping = {}
+    for block in matcher.get_matching_blocks():
+        for offset in range(block.size):
+            mapping[block.a+offset] = block.b+offset
+    rate, pcm = wavfile.read(ROOT / "TeddyFull.wav")
+    assert pcm.ndim == 1 and pcm.dtype == np.int16
+    audio = pcm.astype(np.float64) / 32768
+    clips = ROOT / "audio"
+    clips.mkdir(exist_ok=True)
+    accepted, excluded, rows = [], [], []
+    cursor = 0
+    for index, sentence in enumerate(re.split(r"(?<=[.!?])\s+", text.strip()), 1):
+        count = len(tokens(sentence))
+        positions = [mapping.get(i) for i in range(cursor,cursor+count)]
+        cursor += count
+        if not count: continue
+        if any(i is None for i in positions) or positions != list(range(positions[0],positions[0]+count)):
+            excluded.append({"sentence":index,"text":sentence,"reason":"Transcript / recognition mismatch; review required"})
+            continue
+        first,last = positions[0],positions[-1]
+        start = max(0,recognized[first]["start"]-.10)
+        end = min(len(audio)/rate,recognized[last]["end"]+.10)
+        # Do not include neighboring spoken words in the padding.
+        if first: start=max(start,(recognized[first-1]["end"]+recognized[first]["start"])/2)
+        if last+1<len(recognized): end=min(end,(recognized[last]["end"]+recognized[last+1]["start"])/2)
+        if not .7 <= end-start <= 15:
+            excluded.append({"sentence":index,"text":sentence,"reason":"Duration outside 0.7-15 seconds"})
+            continue
+        clip = resample_poly(audio[int(start*rate):int(end*rate)],22050,rate)
+        filename=f"teddy_{index:04}.wav"
+        wavfile.write(clips/filename,22050,np.clip(clip*32768,-32768,32767).astype(np.int16))
+        rows.append(filename+"|"+sentence.replace("\n"," "))
+        accepted.append({"file":filename,"start":start,"end":end,"text":sentence})
+    (ROOT/"metadata.draft.csv").write_text("\n".join(rows)+"\n",encoding="utf-8")
+    report={"status":"DRAFT - inspect timing and transcript before training", "matched_word_ratio":len(mapping)/len(reference),
+            "source_seconds":len(audio)/rate,"source_peak":float(np.max(np.abs(audio))),
+            "source_clipped_samples":int(np.sum(np.abs(audio)>=32767/32768)),
+            "clip_count":len(accepted),"clip_seconds":sum(c["end"]-c["start"] for c in accepted),
+            "clips":accepted,"excluded":excluded}
+    (ROOT/"alignment-report.json").write_text(json.dumps(report,indent=2),encoding="utf-8")
+    print(json.dumps({k:v for k,v in report.items() if k not in ("clips","excluded")},indent=2))
+    print(f"Excluded {len(excluded)} sentences for review")
+if __name__ == "__main__": main()