audio and transcript

cht/transcriber/engine.py

@@ -0,0 +1,98 @@
+"""
+Transcription engine using faster-whisper.
+
+Processes WAV chunks incrementally, assigns sequential IDs (T0001, T0002, ...),
+and persists to transcript/index.json in the session directory.
+"""
+
+import json
+import logging
+from dataclasses import dataclass, asdict
+from pathlib import Path
+
+log = logging.getLogger(__name__)
+
+
+@dataclass
+class TranscriptSegment:
+    id: str        # "T0001"
+    start: float   # seconds into recording
+    end: float     # seconds into recording
+    text: str      # transcribed text
+
+
+class TranscriberEngine:
+    """Incremental transcription via faster-whisper with GPU acceleration."""
+
+    def __init__(self, model_size="small", device="cuda"):
+        self._model = None
+        self._model_size = model_size
+        self._device = device
+        self._segments: list[TranscriptSegment] = []
+        self._next_id = 1
+
+    def _ensure_model(self):
+        if self._model is not None:
+            return
+        log.info("Loading whisper model: %s (device=%s)", self._model_size, self._device)
+        from faster_whisper import WhisperModel
+        self._model = WhisperModel(
+            self._model_size,
+            device=self._device,
+            compute_type="float16" if self._device == "cuda" else "int8",
+        )
+        log.info("Whisper model loaded")
+
+    def transcribe_chunk(self, wav_path, time_offset=0.0) -> list[TranscriptSegment]:
+        """Transcribe a WAV chunk. Returns new segments with absolute timestamps."""
+        self._ensure_model()
+        try:
+            segments_iter, _info = self._model.transcribe(
+                str(wav_path),
+                beam_size=5,
+                vad_filter=True,
+            )
+        except Exception as e:
+            log.error("Whisper transcription failed: %s", e)
+            return []
+
+        new_segments = []
+        for seg in segments_iter:
+            text = seg.text.strip()
+            if not text:
+                continue
+            tid = f"T{self._next_id:04d}"
+            self._next_id += 1
+            entry = TranscriptSegment(
+                id=tid,
+                start=time_offset + seg.start,
+                end=time_offset + seg.end,
+                text=text,
+            )
+            self._segments.append(entry)
+            new_segments.append(entry)
+
+        return new_segments
+
+    def all_segments(self) -> list[TranscriptSegment]:
+        return list(self._segments)
+
+    def save_index(self, path: Path):
+        data = [asdict(s) for s in self._segments]
+        path.write_text(json.dumps(data, indent=2))
+
+    def load_index(self, path: Path):
+        try:
+            data = json.loads(path.read_text())
+        except Exception as e:
+            log.warning("Failed to load transcript index: %s", e)
+            return
+        self._segments = [TranscriptSegment(**e) for e in data]
+        if self._segments:
+            last_num = max(int(s.id.lstrip("T")) for s in self._segments)
+            self._next_id = last_num + 1
+        log.info("Loaded %d transcript segments", len(self._segments))
+
+    def reset(self):
+        self._segments.clear()
+        self._next_id = 1