Implementation of Automatic Court Hearing Transcription
A secretary spends up to 40% of their time on transcription: each minute of audio requires 5–10 minutes of manual work. A mistake in the protocol risks overturning a decision. Our automatic court hearing transcription system combines on-premise ASR with speaker diarization and legal vocabulary to achieve WER less than 2% on clean recordings. The model is fine-tuned on a corpus of 100+ hours of court hearings, including regional specifics. This is not just recognition — it is a complete ML pipeline for courts, ensuring confidentiality and accuracy that cloud services cannot match.
Why On-Premise Is Safer Than Cloud
Cloud services transmit audio to a third party — a violation of the secrecy of the deliberation room (Article 241 of the Criminal Procedure Code). On-premise architecture guarantees that data never leaves the court's perimeter. Moreover, commercial STT services yield 10–15% WER on legal vocabulary, while our solution achieves <2% (5–10 times more accurate). Our on-premise solution outperforms cloud services by 5–10x in accuracy on legal vocabulary.
How We Achieve >98% Accuracy
We take Whisper large-v3 and fine-tune it on a corpus of court hearings (100+ hours, 20+ courts). We use LoRA adapters for rapid adaptation to a specific speaker's voice. The dictionary includes 5,000+ legal terms and patterns: "article one hundred fifty-two" → "Art. 152", "part one of article" → "Part 1 of Art.". The normalizer also processes dates, names, and abbreviations (CPC, CCP, APC).
class LegalTextNormalizer: def normalize(self, text: str) -> str: text = re.sub(r'article (\d+)', r'Art. \1', text) text = re.sub(r'part (\w+)', lambda m: f'Part {ROMAN_TO_INT[m.group(1)]}', text) return text Additionally, we use Voice Activity Detection (VAD) to filter noise and pauses, reducing WER by another 1–2%. Without VAD, the model "hears" background conversations and generates phantom phrases.
How Much Can You Save on Transcription?
| Metric | Manual transcription | Our system |
|---|---|---|
| Time per 1 hour of audio | 5–10 hours | 15–20 minutes (post-editing) |
| Accuracy | 100% (but slow) | >98% (WER<2%) |
| Secretary workload | 40% of time | 5–10% (only control) |
Payroll savings at typical load amount to over $11k–16k per year for a court with 10 judges. The exact value depends on the volume of hearings and region.
How Does Diarization Handle Overlapping Speech?
We use pyannote/speaker-diarization-3.1 with a threshold of 0.6. When overlapping occurs, the label SPEAKER_00+SPEAKER_01 is assigned, and the utterance is flagged for manual verification. Attribution accuracy is 92% for 2–4 participants, 85% for 5+.
Что входит в работу
- Fine-tuning Whisper on your recordings (5–10 hours, 2–3 weeks).
- On-premise deployment on a server with GPU (NVIDIA A10G, L40S, etc.).
- Integration with GAS Justice via REST API or XML exchange.
- Training secretaries in post-editing (1 day).
- Handover of model source code, configs, and documentation.
- Access to model repository and support for 6 months.
On-Premise vs Cloud STT: Key Differences
| Parameter | On-premise (our solution) | Cloud STT |
|---|---|---|
| Confidentiality | Data stays within perimeter | Audio sent to third party |
| WER on legal vocabulary | <2% | 10–15% |
| Customer-specific fine-tuning | Yes | No |
| Diarization | PyAnnote 3.1 (92% accuracy) | Basic (70–80%) |
| Integration with GAS | Certified module | Requires adapter |
Typical Mistakes and How to Avoid Them
- Skipping VAD filtering: The model "hears" noise and generates phantom phrases — WER jumps to 20%. Our VAD removes silence and noise, leaving only speech.
- Using the model without fine-tuning: Standard Whisper is not adapted for legal vocabulary, yielding 10–15% WER. Fine-tuning is mandatory to achieve the stated accuracy.
- Ignoring post-editing: Even at 98% accuracy, manual control is needed for complex sections. We include a post-editing interface that highlights uncertain fragments — this speeds up verification by 2–3 times.
Implementation Process
- Infrastructure audit and collection of 5+ hours of audio.
- Data labeling and fine-tuning (3–4 weeks).
- Development of integration modules (2–3 weeks).
- Testing on a control sample (1 week).
- Deployment and staff training (1 week).
- Pilot operation with our support (2 weeks).
Timelines and How to Get Started
Basic system: from 4 weeks. Full cycle with fine-tuning and GAS integration: up to 12 weeks. Contact us for an individual proposal — we will assess your project and prepare a turnkey quote. Order a demo and verify accuracy on your own recordings — get a free engineer consultation.
Architecture described in Whisper and pyannote/speaker-diarization-3.1. Our company has 5+ years of NLP experience and 20+ transcription projects for courts and law firms.