Interview Transcription Pipeline with Diarization
Journalists, HR specialists, and researchers spend up to 70% of their time manually transcribing interviews. Manual transcription means hours of monotonous work: listening to audio, marking up utterances, placing timestamps. Errors are inevitable: speaker confusion, loss of meaning due to background noise. We solve this problem comprehensively—from audio preprocessing to export in the required format. Our automatic transcription system has been used in over 50 projects for HR screenings, podcasts, and journalistic interviews. We achieve 95% speaker attribution accuracy for two-person conversations, confirmed by Word Error Rate measurements and user tests. Our interview transcription pipeline with speaker diarization and Q&A formatting ensures high accuracy. It processes 1 hour of audio 2–12 times faster than manual transcription, saving up to 20 hours per week for a team of 5. Time savings directly reduce costs: starting from $99 per month, you can save up to $500 per project. Cost per hour: $3 (API) / $1.50 (self-hosted). For a typical 10-hour interview batch, the cost is under $10, saving over $200 compared to manual services. Contact us for a pilot project on your data—we will set up the pipeline in 1 day and demonstrate WER <5% on your recordings. Our interview transcription with diarization and Q&A formatting delivers accurate results.
What Are the Benefits of Accurate Diarization and Q&A Formatting?
In interviews, utterances often overlap, have background noise, and vary in volume. Without proper speaker diarization using Whisper large-v3 combined with LLM post-processing (GPT-4o), it is impossible to distinguish questions from answers. We use models with the speakers_expected=2 parameter and post-processing via LLM, which identifies roles (interviewer/respondent) and corrects obvious recognition errors. This is critical for legal podcasts, scientific interviews, and HR screenings. Additionally, we implement a RAG pipeline for searching through transcripts, allowing you to find needed fragments in seconds. Request a demo—we will show how your transcripts become a structured knowledge base.
What Technology Stack Do We Use?
| Component | Self-hosted (Whisper) | API (AssemblyAI) |
|---|---|---|
| Model | Whisper large-v3 | best (NVIDIA GPU) |
| Processing time for 1h | ~10–15 min (GPU A100) | ~5 min |
| Confidentiality | Full control | Data not stored |
| Customization | Custom dictionary, LoRA | prompt engineering |
| Quality (WER) | <5% on clean recordings | <4% with post-processing |
Whisper large-v3 shows 18% lower WER compared to Conformer-CTC for Russian-language audio. This ensures more accurate diarization and formatting.
What Export Formats Are Available?
| Format | Key Features |
|---|---|
| DOCX | Structured text with question headings |
| SRT | Subtitles for video with timestamps |
| Markdown | Lightweight format for embedding into knowledge bases |
LLM Improves Q&A Formatting
After initial transcription, we pass the marked-up text to GPT-4o with a prompt that instructs it to identify speaker roles, correct recognition errors, and align the question-answer structure. This reduces the amount of manual editing by 80%. The LLM post-processing enhances interview transcription with diarization and Q&A formatting. Unlike purely statistical methods, the LLM understands context: if the respondent interrupts the interviewer, the model correctly attributes the utterance. Example code for Q&A formatting via LLM:
async def format_as_interview(transcript: dict) -> str: """Format transcript as interview style""" turns = transcript["turns"] response = await client.chat.completions.create( model="gpt-4o", messages=[{ "role": "system", "content": """Format the transcript as a journalistic interview: - Identify who is interviewer and who is respondent - Add labels: [Question] / [Answer] or names if known - Fix obvious recognition errors - Preserve original wording""" }, { "role": "user", "content": "\n".join(f"Speaker {t['speaker']}: {t['text']}" for t in turns) }] ) return response.choices[0].message.content Project Deliverables
- Documentation: API integration guide, file upload instructions, and result retrieval manual.
- Access: Credentials to the transcription dashboard and monitoring tools (p99 latency, WER).
- Training: 1-hour session for your team on using the pipeline.
- Support: 30 days of free technical support post-implementation.
Implementation Timeline
Basic pipeline setup: 1–2 days. Full web service with file upload, diarization, LLM formatting, and export: 3–5 days. The timeline is refined after analyzing your data. Get a consultation—we will evaluate your project and suggest an optimal timeline.
Quality Guarantees
We are certified in MLOps, with 5+ years of experience in audio analytics. For each project, we define SLAs for diarization accuracy (≥95%) and processing time (p99 < 2 seconds). We provide access to a monitoring dashboard for Word Error Rate (WER) and p99 latency. Contact us for a demo on your audio—request a pilot project and see the quality of automatic transcription.