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Shunyalabs.ai, a leader in Voice AI infrastructure for the enterprise segment, has announces the launch of Zero STT Med, a breakthrough domain-optimized automatic speech recognition (ASR) system tailored to medical and clinical workflows. Leveraging Shunyalabs’ proprietary training technology, Zero STT Med delivers state-of-the-art accuracy, real-time responsiveness, and flexible on-premises or cloud deployment—designed for use in hospitals, telemedicine, ambient-scribe systems, and regulated healthcare environments.
Zero STT Med at a GlanceZero STT Med achieves a WER of 11.1% and a CER of 5.1%, outperforming all major medical ASR competitors in terms of accuracy and reliability.Through ShunyaLab’s proprietary training methodology, Zero STT Med is trained with minimal real clinical audio, achieving full convergence in just 3 days of training on 2 × A100 GPUs — dramatically lowering data collection and compute barriers for medical ASR.Its fast-training cycle enables frequent releases, allowing Shunyalabs to keep the model continually updated with new drugs, procedures, and terminologies.Zero STT Med supports high RTFx performance, enabling real-time transcription in clinical settings — for example, during consultations, charting, or dictation.For environments with strict privacy, Zero STT Med can run on-premises on CPU-only servers (no cloud dependency), providing full data control and compliance with healthcare privacy standards (HIPAA, GDPR, etc.).
Why Zero STT Med Matters for Healthcare Workflows
Healthcare conversations are among the most challenging domains for ASR:·Clinicians speak quickly, often using acronyms, drug names, dosages, temporal expressions, and shorthand.· Multiple interlocutors (clinician, patient, nurse) may overlap or interrupt each other.· Domain-critical terms (diagnoses, medications, laterality, numeric values) require extreme precision — even small transcription errors can change meaning dramatically:- Privacy and regulatory constraints often limit usable training data and prohibit cloud-based processing.Zero STT Med directly addresses these challenges through:Domain-aware vocabulary and formatting
Zero STT Med includes extensive support for medical terminology, drug names, clinical procedures, ICD/LOINC codes, numeric and dosage normalization, and abbreviation expansion, minimizing manual correction.
Robust speaker diarization and context tracking
Zero STT Med distinguishes between speakers (clinician, patient, caregiver) in real time, even with background noise or overlapping speech, producing accurate, attributed transcripts.
Accent-robust, low-domain bias
Trained on diverse data, Zero STT Med generalizes across accents, dialects, and speaking styles, ensuring consistency in real-world clinical use.
Low-data, fast training
The proprietary training methodology enables Zero STT Med to reach top-tier performance using limited real audio. Its 3-day, 2×A100 GPU training window makes frequent retraining and domain adaptation practical — the key USP that allows Shunyalabs to stay ahead of evolving medical vocabulary.
Real-time-first architecture and high throughput
Optimized for low-latency streaming and robust batch inference, Zero STT Med delivers identical recognition quality in both real-time and offline modes.
On-premises CPU deployment for privacy and compliance
Zero STT Med runs efficiently on standard CPU infrastructure, ensuring compliance with HIPAA, GDPR, and local data governance requirements — ideal for hospitals and enterprises where data must remain on-prem.
Use Cases and Impact
Zero STT Med unlocks new capabilities across medical workflows:
· Ambient clinical scribing — generate structured notes in real time, reducing clinician screen time.
· Live dictation and charting — dictate orders or summaries with minimal post-editing.
· Telemedicine and virtual consults — produce live transcripts for documentation and analytics.
· Radiology and procedural transcription — enable real-time voice-to-text during surgeries or imaging.
· Backfill transcription and archive conversion — batch transcribe legacy audio with the same model.
· On-device and edge deployment — support real-time transcription in mobile or offline environments.
By reducing transcription error rates, Zero STT Med lowers correction workloads, enabling downstream.
NLP/LLM analytics enhances clinical efficiency and record accuracy.
Benchmarking and Competitive Position
Zero STT Med leads the medical ASR landscape across accuracy, efficiency, and deployment flexibility.
Model / Company Word Error Rate Character Error Rate Training Time On-Prem CPU Real-Time Ready
Shunyalabs Zero STT Med (2025) Whisper V3 Large | 11.1%
15.7% | 5.1%
5.6% | 3 days (2×A100 GPUs) Weeks |
+ |
|
ElevenLabs Scribe V1 | 18.6% | 8.7% | Weeks | + |
|
Gemini 2.5 Flash | 14.8% | 5.5% | Weeks | + |
|
AWS Transcribe | 18.3% | 7.4% | Weeks | + |
|
Accuracy: Zero STT Med achieves industry-leading accuracy, exceeding commercial baselines. Training efficiency: It's a fast 3-day training on limited data, enabling frequent releases and rapid domain adaptation.
Deployment flexibility: Runs on both GPU and CPU, unlike most cloud-only medical ASR systems. Real-time-first architecture: Zero STT Med’s streaming mode delivers the same accuracy as batch, eliminating legacy trade-offs.
Quotes
“At Shunyalabs, we believe medical transcription must be not just fast, but flawlessly accurate — every dosage, diagnosis, and timestamp matters. Zero STT Med embodies that vision. We’ve reduced the cost and time to train, making high-fidelity ASR accessible to more healthcare systems.”
— Ritu Mehrotra, CEO & Founder, Shunyalabs.ai
“Our goal with Zero STT Med wasn’t incremental improvement — it was to redefine medical speech
recognition: fewer corrections, lower latency, and complete data privacy.”
— Sourav Banerjee, CTO, Shunyalabs.ai