fix: Restore all files lost during destructive rebase
A previous `git pull --rebase origin main` dropped 177 local commits,
losing 3400+ files across admin-v2, backend, studio-v2, website,
klausur-service, and many other services. The partial restore attempt
(660295e2) only recovered some files.
This commit restores all missing files from pre-rebase ref 98933f5e
while preserving post-rebase additions (night-scheduler, night-mode UI,
NightModeWidget dashboard integration).
Restored features include:
- AI Module Sidebar (FAB), OCR Labeling, OCR Compare
- GPU Dashboard, RAG Pipeline, Magic Help
- Klausur-Korrektur (8 files), Abitur-Archiv (5+ files)
- Companion, Zeugnisse-Crawler, Screen Flow
- Full backend, studio-v2, website, klausur-service
- All compliance SDKs, agent-core, voice-service
- CI/CD configs, documentation, scripts
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
211
backend/transcription_worker/transcriber.py
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211
backend/transcription_worker/transcriber.py
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"""
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BreakPilot Whisper Transcriber
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Uses faster-whisper (MIT License) for GPU-optimized transcription.
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Based on CTranslate2 for fast inference.
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"""
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import os
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import structlog
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from typing import List, Dict, Optional
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log = structlog.get_logger(__name__)
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class WhisperTranscriber:
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"""
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Whisper-based audio transcription using faster-whisper.
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faster-whisper is a reimplementation of OpenAI Whisper using CTranslate2,
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which is significantly faster than the original implementation.
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License: MIT
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Source: https://github.com/SYSTRAN/faster-whisper
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"""
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def __init__(
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self,
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model_name: str = "large-v3",
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device: str = "cpu",
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compute_type: str = "int8"
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):
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"""
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Initialize the transcriber.
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Args:
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model_name: Whisper model to use (tiny, base, small, medium, large-v3)
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device: Device to run on ("cpu", "cuda", "auto")
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compute_type: Quantization type ("int8", "float16", "float32")
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"""
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self.model_name = model_name
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self.device = device
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self.compute_type = compute_type
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self._model = None
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def _load_model(self):
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"""Lazy load the model on first use."""
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if self._model is not None:
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return
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try:
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from faster_whisper import WhisperModel
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log.info(
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"loading_whisper_model",
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model=self.model_name,
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device=self.device,
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compute_type=self.compute_type
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)
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self._model = WhisperModel(
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self.model_name,
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device=self.device,
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compute_type=self.compute_type
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)
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log.info("whisper_model_loaded")
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except ImportError:
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log.error("faster_whisper_not_installed")
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raise ImportError(
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"faster-whisper is not installed. "
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"Install with: pip install faster-whisper"
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)
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def transcribe(
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self,
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audio_path: str,
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language: str = "de",
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beam_size: int = 5,
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word_timestamps: bool = True,
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vad_filter: bool = True,
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vad_parameters: Optional[dict] = None
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) -> List[Dict]:
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"""
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Transcribe an audio file.
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Args:
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audio_path: Path to audio file (WAV, MP3, etc.)
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language: Language code (de, en, fr, etc.) or None for auto-detection
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beam_size: Beam size for decoding (higher = better but slower)
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word_timestamps: Include word-level timestamps
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vad_filter: Enable Voice Activity Detection to filter silence
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vad_parameters: Custom VAD parameters
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Returns:
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List of segments with text, timestamps, and confidence scores
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"""
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self._load_model()
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if not os.path.exists(audio_path):
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raise FileNotFoundError(f"Audio file not found: {audio_path}")
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log.info(
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"transcribing_audio",
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audio_path=audio_path,
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language=language,
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beam_size=beam_size
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)
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# Default VAD parameters for better speech detection
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if vad_parameters is None:
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vad_parameters = {
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"min_silence_duration_ms": 500,
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"speech_pad_ms": 400
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}
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# Run transcription
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segments_gen, info = self._model.transcribe(
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audio_path,
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language=language,
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beam_size=beam_size,
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word_timestamps=word_timestamps,
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vad_filter=vad_filter,
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vad_parameters=vad_parameters
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)
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log.info(
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"transcription_info",
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detected_language=info.language,
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language_probability=info.language_probability,
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duration=info.duration
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)
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# Convert generator to list of segments
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segments = []
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for i, segment in enumerate(segments_gen):
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seg_dict = {
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"index": i,
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"start_time_ms": int(segment.start * 1000),
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"end_time_ms": int(segment.end * 1000),
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"text": segment.text.strip(),
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"confidence": round(segment.avg_logprob, 3) if segment.avg_logprob else None,
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"no_speech_prob": segment.no_speech_prob
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}
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# Add word-level timestamps if available
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if word_timestamps and segment.words:
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seg_dict["words"] = [
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{
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"word": word.word,
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"start": int(word.start * 1000),
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"end": int(word.end * 1000),
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"probability": round(word.probability, 3)
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}
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for word in segment.words
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]
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segments.append(seg_dict)
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log.info(
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"transcription_complete",
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segments_count=len(segments),
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duration_seconds=info.duration
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)
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return segments
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def detect_language(self, audio_path: str) -> dict:
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"""
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Detect the language of an audio file.
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Args:
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audio_path: Path to audio file
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Returns:
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dict with language code and probability
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"""
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self._load_model()
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if not os.path.exists(audio_path):
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raise FileNotFoundError(f"Audio file not found: {audio_path}")
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# Transcribe first 30 seconds to detect language
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_, info = self._model.transcribe(
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audio_path,
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language=None, # Auto-detect
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beam_size=1,
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without_timestamps=True
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)
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return {
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"language": info.language,
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"probability": info.language_probability
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}
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@property
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def available_languages(self) -> List[str]:
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"""List of supported languages."""
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return [
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"de", "en", "fr", "es", "it", "pt", "nl", "pl", "ru",
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"zh", "ja", "ko", "ar", "tr", "hi", "vi", "th", "id"
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]
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def get_model_info(self) -> dict:
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"""Get information about the loaded model."""
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return {
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"model_name": self.model_name,
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"device": self.device,
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"compute_type": self.compute_type,
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"loaded": self._model is not None
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}
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