This notebook implements a Python pipeline for generating subtitles from audio files, featuring speaker diarization and a quality check agent. It's useful for analyzing short clips with multiple speakers, producing SRT files with labeled dialogues.

• Speaker diarization with consistent labeling.
• Dialogue-level transcription.
• Confidence-based quality evaluation per segment.

1. Audio Preparation: Standardize audio to mono PCM WAV.
2. Transcription: Leverage Whisper for accurate segmenting.
3. Diarization: Use pyannote embeddings and clustering for speaker assignment—more reliable than basic methods.
4. Merging: Combine segments for natural flow.
5. Output: SRT with labels.
6. Quality Agent: Rule-based confidence scoring and feedback.

Input audio → Convert → Transcribe → Embed & Cluster → Merge → SRT & Quality Report.

• Limitations: Fixed speaker count; best on clean audio.
• Improvements: Add overlap detection; LLM feedback; auto-speaker estimation.