fix(backend): enable long-form Whisper transcription on PyTorch path

The PyTorch Whisper transcribe() called the HF processor without
truncation=False and model.generate() without return_timestamps=True.
With those defaults, WhisperFeatureExtractor silently truncates inputs
to 30 s (Whisper's native receptive field), so any dictation longer
than ~30 s lost its tail.

Setting truncation=False + padding="longest" + return_attention_mask=True
on the processor, then forwarding the attention mask plus
return_timestamps=True to generate(), flips HF Whisper into long-form
mode: autoregressive decoding over rolling 30 s windows.

Verified by round-tripping a 56.6 s Kokoro TTS sample through
/transcribe — full text returned including content past the 30 s mark;
previously the transcript was cut off roughly halfway through.

MLX backend (mlx_backend.py) is intentionally unchanged: mlx_audio.stt's
generate() already implements rolling-window long-form transcription
with condition_on_previous_text in the upstream library, so it does not
have the same bug. The HF-only kwargs added here would also break the
MLX call signature.

Co-Authored-By: Claude Opus 4.7 <[email protected]>

backend/backends/pytorch_backend.py

@@ -343,11 +343,18 @@ class PyTorchSTTBackend:
             # state — forcing offline here (issue #462) broke online users
             # whose `get_decoder_prompt_ids` / tokenizer calls issue
             # legitimate metadata lookups.
-            # Process audio
+            # Process audio.
+            # truncation=False + padding="longest" + return_attention_mask=True
+            # are required for long-form transcription. Without them the
+            # feature extractor silently truncates to 30s (Whisper's native
+            # window) and audio past that point is dropped.
             inputs = self.processor(
                 audio,
                 sampling_rate=16000,
                 return_tensors="pt",
+                truncation=False,
+                padding="longest",
+                return_attention_mask=True,
             )
             inputs = inputs.to(self.device)
 
@@ -364,6 +371,8 @@ class PyTorchSTTBackend:
             with torch.no_grad():
                 predicted_ids = self.model.generate(
                     inputs["input_features"],
+                    attention_mask=inputs["attention_mask"],
+                    return_timestamps=True,
                     **generate_kwargs,
                 )