feat(transcriber): реализована обёртка над faster-whisper (шаг 3)

- Зачем:
  - необходим модуль транскрипции с CUDA fallback для основного flow приложения.
- Что:
  - добавлена зависимость faster-whisper>=1.2.1 в pyproject.toml.
  - реализована функция transcribe() с fallback CUDA→CPU на всех этапах (загрузка модели, вызов transcribe, итерация сегментов).
  - исправлен IndexError в get_gpu_name() при пустом stdout nvidia-smi.
  - добавлено 6 тестов в test_transcriber.py и 1 тест в test_utils.py (16 тестов зелёные).
- Проверка:
  - uv run pytest -v (16 passed).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

src/local_transcriber/transcriber.py

@@ -1,7 +1,10 @@
+import warnings
 from collections.abc import Callable
 from dataclasses import dataclass
 from pathlib import Path
 
+from faster_whisper import WhisperModel
+
 
 @dataclass
 class Segment:
@@ -27,4 +30,59 @@ def transcribe(
     language: str | None = None,
     on_segment: Callable[[Segment], None] | None = None,
 ) -> TranscribeResult:
-    raise NotImplementedError
+    actual_device = device
+    lang_arg = language if language and language != "auto" else None
+
+    try:
+        model = WhisperModel(model_name, device=device, compute_type=compute_type)
+    except (RuntimeError, ValueError) as exc:
+        if device != "cpu" and _is_cuda_error(exc):
+            warnings.warn(
+                f"Не удалось загрузить модель на {device}: {exc}. "
+                "Переключение на CPU.",
+                stacklevel=2,
+            )
+            actual_device = "cpu"
+            model = WhisperModel(model_name, device="cpu", compute_type=compute_type)
+        else:
+            raise
+
+    try:
+        segments, info = _run_transcription(model, file_path, lang_arg, on_segment)
+    except (RuntimeError, ValueError) as exc:
+        if actual_device != "cpu" and _is_cuda_error(exc):
+            warnings.warn(
+                f"CUDA ошибка при транскрипции: {exc}. "
+                "Переключение на CPU и повтор.",
+                stacklevel=2,
+            )
+            actual_device = "cpu"
+            model = WhisperModel(model_name, device="cpu", compute_type=compute_type)
+            segments, info = _run_transcription(model, file_path, lang_arg, on_segment)
+        else:
+            raise
+
+    return TranscribeResult(
+        segments=segments,
+        language=info.language,
+        language_probability=info.language_probability,
+        duration=info.duration,
+        device_used=actual_device,
+    )
+
+
+def _run_transcription(model, file_path, lang_arg, on_segment):
+    """Run model.transcribe and iterate segments. Returns (segments, info)."""
+    segment_generator, info = model.transcribe(str(file_path), language=lang_arg)
+    segments: list[Segment] = []
+    for raw_seg in segment_generator:
+        seg = Segment(start=raw_seg.start, end=raw_seg.end, text=raw_seg.text)
+        if on_segment is not None:
+            on_segment(seg)
+        segments.append(seg)
+    return segments, info
+
+
+def _is_cuda_error(exc: BaseException) -> bool:
+    msg = str(exc).lower()
+    return "cuda" in msg or "out of memory" in msg

src/local_transcriber/utils.py

@@ -39,8 +39,10 @@ def get_gpu_name() -> str | None:
             check=False,
         )
         if result.returncode == 0:
-            name = result.stdout.strip().splitlines()[0].strip()
-            return name if name else None
+            lines = result.stdout.strip().splitlines()
+            if lines:
+                name = lines[0].strip()
+                return name if name else None
     except FileNotFoundError:
         pass
     return None