v0.13.0:2 - per-segment confidence in diarize-chunk response

Recap Relay dev asked: can the diarization output include a confidence
level per segment so the UI can render "Speaker_0?" for uncertain
assignments rather than confidently mislabeling?

Answer: yes. Sortformer's diarize() with include_tensor_outputs=True
returns the per-frame per-speaker sigmoid scores (shape [B, T, 4spk],
~12.6 fps frame rate). The current code argmaxes those into segment
strings and throws the raw scores away. Now: for each output segment,
compute mean probability of the assigned speaker across the segment's
frames → confidence in [0, 1].

Implementation:
  - diarizer.py: diarize_chunk() now calls diarize() with
    include_tensor_outputs=True, and a new _attach_confidence() helper
    derives the per-segment mean probability after parsing the segment
    strings. The frame-rate is computed from tensor shape vs audio
    duration (no need to hard-code the model's stride).
  - All failure paths return confidence=None gracefully — Recap Relay
    can treat None as "no info" or fall back to a default threshold.

Endpoint shape change: segments[] now have an optional `confidence`
field in [0, 1] (or None). All other fields unchanged. Existing callers
that ignore the field aren't affected.

Verified with a 5s test signal that the tensor has shape [1, 63, 4]
(63 frames / 5s = 12.6 fps) and values in [0, 1] (sigmoid outputs,
independent per speaker so overlap detection works). Real speech values
will be much higher than the near-zero values of the pure-tone test
signal.

Reapply patches on the Speech Models card after installing v0.13.0:2
to pick up the updated diarizer.py + main.py in the parakeet container.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

image/parakeet_patches/main.py

@@ -175,7 +175,10 @@ async def diarize_chunk(
     Response shape:
         {
           "duration": 300.0,
-          "segments": [{"start_s": 1.2, "end_s": 4.8, "speaker": "Speaker_0"}, ...],
+          "segments": [
+            {"start_s": 1.2, "end_s": 4.8, "speaker": "Speaker_0", "confidence": 0.78},
+            ...
+          ],
           "speakers_detected": ["Speaker_0", "Speaker_1", "Speaker_2"],
           "fingerprints": {
             "Speaker_0": [0.123, -0.045, ..., 0.211],   # 192-dim TitaNet embedding
@@ -188,6 +191,13 @@ async def diarize_chunk(
           }
         }
 
+    confidence per segment: mean probability that the assigned speaker was
+    active across the segment's frames (Sortformer's raw per-frame per-
+    speaker sigmoid outputs). Range [0, 1], higher = more confident.
+    Clean speech typically >0.5; ambiguous regions (overlap, weak signal)
+    fall lower. None on derivation failure. Recap Relay can threshold
+    this to render uncertain segments as "Speaker_0?" in the UI.
+
     Speaker labels are LOCAL to this chunk. Run cosine-similarity clustering
     across the fingerprints from all chunks to merge `chunkA.Speaker_0` with
     `chunkB.Speaker_2` when they're the same voice. Recommended threshold: