Initial commit: Børnetime app scaffold

FastAPI + Celery + Svelte app for English→Danish video translation
with voice cloning. Includes diarization, STT, translation, TTS,
and audio mixing pipeline.

backend/app/services/diarization.py

@@ -0,0 +1,84 @@
+import logging
+import torch
+from typing import Optional
+from pyannote.audio import Pipeline
+from ..config import settings
+
+logger = logging.getLogger(__name__)
+
+_diarization_pipeline: Optional[Pipeline] = None
+
+
+def get_diarization_pipeline() -> Pipeline:
+    global _diarization_pipeline
+    if _diarization_pipeline is None:
+        import os
+
+        hf_token = settings.hf_token or os.environ.get("HUGGINGFACE_TOKEN")
+        logger.info("Loading diarization model on %s", settings.diarization_device)
+        _diarization_pipeline = Pipeline.from_pretrained(
+            settings.diarization_model,
+            use_auth_token=hf_token,
+        )
+        _diarization_pipeline.to(torch.device(settings.diarization_device))
+    return _diarization_pipeline
+
+
+def diarize(audio_path: str) -> list[dict]:
+    pipeline = get_diarization_pipeline()
+    diarization = pipeline(audio_path)
+
+    segments = []
+    for turn, _, speaker in diarization.itertracks(yield_label=True):
+        segments.append(
+            {
+                "speaker_label": speaker,
+                "start_time": turn.start,
+                "end_time": turn.end,
+            }
+        )
+
+    segments.sort(key=lambda s: s["start_time"])
+    segments = _merge_adjacent_same_speaker(segments)
+    segments = _relabel_speakers(segments)
+
+    return segments
+
+
+def _merge_adjacent_same_speaker(
+    segments: list[dict], max_gap: float = 0.5
+) -> list[dict]:
+    if not segments:
+        return []
+
+    merged = [segments[0]]
+    for seg in segments[1:]:
+        prev = merged[-1]
+        if (
+            prev["speaker_label"] == seg["speaker_label"]
+            and (seg["start_time"] - prev["end_time"]) <= max_gap
+        ):
+            prev["end_time"] = seg["end_time"]
+        else:
+            merged.append(seg)
+    return merged
+
+
+def _relabel_speakers(segments: list[dict]) -> list[dict]:
+    label_map = {}
+    counter = 0
+    for seg in segments:
+        spk = seg["speaker_label"]
+        if spk not in label_map:
+            label = chr(65 + counter)
+            label_map[spk] = f"Speaker {label}"
+            counter += 1
+        seg["speaker_label"] = label_map[spk]
+    return segments
+
+
+def unload_model():
+    global _diarization_pipeline
+    _diarization_pipeline = None
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()