src/audio_summary/models/config.py

@@ -9,7 +9,8 @@ threshold is *less than or equal to* the detected memory is selected (see
 ``audio_summary.device.select_models``).
 
 STT engines:
-    * ``"voxtral"``  -> Mistral Voxtral via ``mlx-audio`` (streaming, long audio).
+    * ``"voxtral"``  -> Mistral Voxtral (non-realtime batch model) via ``mlx-audio``.
+      Honors the requested language and transcribes whole files in one pass.
     * ``"whisper"``  -> ``mlx-whisper`` (lightweight fallback for low-RAM Macs).
 """
 
@@ -42,17 +43,17 @@ TIERS: list[Tier] = [
     ),
     Tier(
         min_ram_gb=16,
-        stt=STTModel("voxtral", "mlx-community/Voxtral-Mini-4B-Realtime-2602-4bit"),
+        stt=STTModel("voxtral", "mlx-community/Voxtral-Mini-3B-2507-bf16"),
         summarization_repo="mlx-community/Qwen3-8B-4bit",
     ),
     Tier(
         min_ram_gb=24,
-        stt=STTModel("voxtral", "mlx-community/Voxtral-Mini-4B-Realtime-2602-fp16"),
+        stt=STTModel("voxtral", "mlx-community/Voxtral-Mini-3B-2507-bf16"),
         summarization_repo="mlx-community/Qwen3-8B-8bit",
     ),
     Tier(
         min_ram_gb=32,
-        stt=STTModel("voxtral", "mlx-community/Voxtral-Mini-4B-Realtime-2602-fp16"),
+        stt=STTModel("voxtral", "mlx-community/Voxtral-Mini-3B-2507-bf16"),
         summarization_repo="mlx-community/Qwen3-30B-A3B-4bit",
     ),
 ]

src/audio_summary/pipeline/transcription.py

@@ -2,22 +2,30 @@
 
 Two engines are supported, selected through ``config.STTModel.engine``:
 
-* ``voxtral`` -> Mistral Voxtral via ``mlx-audio``. Outperforms Whisper
+* ``voxtral`` -> Mistral Voxtral (non-realtime batch model) via ``mlx-audio``.
-  large-v3 on most benchmarks and natively handles long audio with streaming.
+  Outperforms Whisper large-v3 on most benchmarks. The *realtime* Voxtral
+  variant is deliberately not used here: it ignores the requested language and
+  truncates when run one-shot on a full file.
 * ``whisper`` -> ``mlx-whisper`` fallback for memory-constrained Macs.
 """
 
 from ..models import STTModel
 
+# Upper bound on generated tokens for a full-file transcription. The non-realtime
+# Voxtral ``generate`` defaults to only 128 tokens, which would truncate any real
+# recording, so we raise it well above the length of a long-form transcript.
+_VOXTRAL_MAX_TOKENS = 32768
+
 
 def _transcribe_voxtral(file_path: str, model_repo: str, language: str | None) -> str:
     """Transcribe using Mistral Voxtral through mlx-audio."""
     from mlx_audio.stt.utils import load
 
     model = load(model_repo)
-    # Voxtral handles long audio natively; ``generate`` returns an object with a
+    # The non-realtime Voxtral model transcribes the whole file in one pass and
-    # ``.text`` attribute. ``language`` is optional (auto-detected when None).
+    # honors ``language``. ``generate`` returns an object with a ``.text``
-    kwargs: dict = {}
+    # attribute. ``language`` is optional (auto-detected when None).
+    kwargs: dict = {"max_tokens": _VOXTRAL_MAX_TOKENS}
     if language:
         kwargs["language"] = language
     result = model.generate(file_path, **kwargs)