Overview
Production-grade pipeline combining Whisper for transcription and Pyannote for speaker diarization, processing 1000+ hours of audio with 80% cost reduction vs manual transcription.
The Problem
Transcribing multi-speaker meetings manually is time-consuming (4 hours of work per 1 hour of audio), expensive ($1.50/min), and requires specialized skills. Automated solutions often struggle with overlapping speech and speaker identification.
The Solution
Developed an end-to-end pipeline that first performs speaker diarization to identify 'who spoke when', then transcribes each speaker segment separately using Whisper. Post-processing aligns timestamps and formats output as structured JSON or SRT subtitles.
Project Gallery
Technical Architecture
Two-stage pipeline with speaker diarization and speech-to-text
Audio Preprocessing
Noise reduction, normalization, and VAD (Voice Activity Detection)
Pyannote Diarization
Identifies speaker segments and assigns speaker IDs
Whisper Transcription
Transcribes each speaker segment with timestamps
Alignment & Formatting
Merges diarization and transcription, formats output
Methodology
- Audio preprocessing: Noise reduction with noisereduce, normalization to -20dB
- Diarization: Pyannote 3.0 with custom speaker embedding model
- Transcription: Whisper-large-v3 with language detection
- Post-processing: Speaker label alignment, punctuation restoration
- Quality assurance: Confidence scoring and manual review flagging
Results & Impact
Key Impact
- Processed 1000+ hours of meeting audio
- Reduced transcription costs from $90K to $18K annually
- Enabled searchable meeting archives
- Improved accessibility with automated captions
- Deployed for 5+ enterprise clients
Challenges & Solutions
Overlapping Speech
Implemented overlap detection and separate transcription of overlapped segments
Accent Variability
Fine-tuned Whisper on client-specific accent data
Speaker Confusion
Added speaker verification using voice embeddings
Key Implementation
Transcription Pipeline
class TranscriptionPipeline:
def __init__(self):
self.diarization = Pipeline.from_pretrained(
"pyannote/speaker-diarization-3.0"
)
self.whisper = whisper.load_model("large-v3")
def process(self, audio_path):
# Step 1: Speaker diarization
diarization = self.diarization(audio_path)
# Step 2: Extract speaker segments
segments = []
for turn, _, speaker in diarization.itertracks(yield_label=True):
segments.append({
'start': turn.start,
'end': turn.end,
'speaker': speaker,
'audio': self.extract_segment(audio_path, turn)
})
# Step 3: Transcribe each segment
for segment in segments:
result = self.whisper.transcribe(segment['audio'])
segment['text'] = result['text']
segment['confidence'] = result['confidence']
# Step 4: Format output
return self.format_transcript(segments)