OHM-Tanglish-MedASR-1.7B-v152

OHM Tanglish MedASR 1.7B v152

OHM Tanglish MedASR 1.7B v152 is the first checkpoint from our one-Indic-language + English medical ASR effort: a Tamil + English clinical speech transcription model at compact 1.7B scale, aimed at smartphone-class edge medical transcription.

This checkpoint is a research collaboration between osmAPI, OHM - Open Holistic Medicine, and the Terv Student Research Team.

We use Tanglish here to mean the Tamil + English speech environment common in many clinical conversations: Tamil, English, transliterated terms, medical vocabulary, patient symptom descriptions, and doctor-patient dialogue. The model is designed as the first language-pair checkpoint in a repeatable path toward one-Indic-language + English medical ASR systems that can eventually run near the patient on edge devices.

Base Model

• Base model: knoveleng/polyglot-lion-1.7b-v1.5
• Released checkpoint: OHM Tanglish MedASR 1.7B v152
• Parameter scale: 1.7B
• Training method: full-parameter finetune
• Adapter type: none
• Primary target: Tamil + English medical ASR
• Deployment direction: smartphone-class edge transcription after quantization and runtime packaging

Why This Size

Medical ASR should not always require a cloud-scale model. Larger ASR systems can be stronger on broad benchmarks, but they are not the natural first target for phone-side or clinic-side transcription.

We target 1.7B parameters because this size is a practical middle ground:

• large enough to preserve multilingual ASR behavior
• small enough to target 4-bit or 8-bit quantized inference
• plausible for offline or low-connectivity medical transcription workflows
• suitable for privacy-preserving edge research
• repeatable for future one-Indic-language + English medical ASR checkpoints

This repository contains the research checkpoint. Actual "runs on any smartphone" support requires mobile export, quantization, runtime integration, and device-level benchmarking.

Finetune Data

v152 was selected from a mixed Tamil + English + medical training recipe designed to preserve all three capabilities at once.

Validation used held-out English, Tamil, medical-intent, and clinical-dialogue data:

All test sets were held out for evaluation only.

Finetuning

OHM Tanglish MedASR 1.7B v152 was trained as a full-parameter finetune from Polyglot-Lion 1.7B. We selected the checkpoint with a conservative guard-suite approach:

• train on a Tamil + English + medical mixture
• evaluate on medical symptom speech, clinical dialogue, English speech, Tamil FLEURS, and IISc-MILE Tamil
• retain only checkpoints that improve without introducing regressions across the mixed guard suite

v152 remained the local champion after additional dataset-focused passes using public Tamil and medical-dialogue data. Later candidates either tied v152 or regressed on at least one guard gate.

Results

Lower is better.

Macro WER across the five gates: 18.32.

Sample-weighted WER across 1,769 evaluation samples: 12.43.

Claim

OHM Tanglish MedASR 1.7B v152 is our first validated Tamil + English medical ASR checkpoint for the one-Indic-language + English medical ASR direction.

We claim that this checkpoint:

• improves WER over vanilla Polyglot-Lion on all five local Tamil + English + medical evaluation gates
• is a compact 1.7B full-finetuned model selected for mixed clinical, English, and Tamil ASR
• is sized for smartphone-class edge deployment research after quantization and runtime packaging
• establishes the first checkpoint in a repeatable path toward other Indic-language + English medical ASR models

We do not claim that this checkpoint is:

• global Tamil ASR state of the art
• a certified medical device
• ready for unsupervised clinical documentation
• universally runnable on every smartphone without further mobile packaging and validation
• superior to Whisper, Qwen3-ASR, MERaLiON, IndicWhisper, Canary, or commercial medical ASR systems without apples-to-apples evaluation on the same manifests

Intended Use

This model is intended for:

• Tamil + English medical ASR research
• clinical speech transcription prototypes
• patient symptom transcription experiments
• medical dialogue ASR benchmarking
• offline and edge ASR studies
• smartphone-oriented ASR quantization and packaging experiments
• future one-Indic-language + English medical ASR research

Safety And Limitations

This checkpoint is for research and prototyping. It is not a certified medical device and should not be used as the sole source for clinical care, diagnosis, treatment, billing, or legal documentation.

Known limitations:

• The benchmark suite is local and not directly comparable to every public ASR leaderboard.
• Public Tamil-specialized systems may perform better on other Tamil benchmarks.
• English performance improves over our local vanilla baseline but is not claimed as state of the art.
• Medical-dialogue evaluation used public/simulated benchmark material, not private real-world deployment audio.
• Mobile deployment still requires export, quantization, runtime integration, and device testing.
• Human review is required for any clinical workflow.

Collaboration

OHM Tanglish MedASR 1.7B v152 is a research collaboration between:

• osmAPI
• OHM - Open Holistic Medicine
• Terv Student Research Team

Acknowledgements

Built from the Polyglot-Lion/Qwen3-ASR ecosystem and evaluated with public and local ASR research manifests. Please cite the upstream model, datasets, and this checkpoint when using it in research.