AI Model Training
General models are trained to be decent for everyone and perfect for nobody. When your work depends on words, labels or decisions a general model gets wrong, a model trained for the job does better, and often runs smaller, cheaper and on your own hardware. I train those models, with speech recognition at the front, and advise teams who train their own.
Flagship: ASR fine-tuning
Speech recognition is where I do most of my own training work, and where a fine-tune makes the most visible difference.
ASR Fine-Tuning
Speech recognition models fine-tuned for your vocabulary, accents and speakers who switch languages mid-sentence, then deployed offline, on a public API or as a private internal API.
See ASR fine-tuning →- Niche vocabulary: legal, medical, technical, brand and project names
- Under-represented accents
- Code-switching, such as Hebrew and English
- Base model chosen by workload and target device
- Real or synthetic speech training data
- Retraining as your vocabulary changes
My own fine-tunes are public: see the open-source ASR models catalogue.
Also in this area
The models beyond speech, and the data, training plans and evals behind every model.
Small Models & Classifiers
Small models built for one job: pipeline accessory models, classifiers with JSON output, replacements for API dependencies, and models that run on-premises.
Custom model development → CC-SVC-11Datasets & Training Recipes
Dataset preparation and blending, drafting and reviewing training plans, and version control and release cadence for models you deploy internally.
Datasets & training recipes → CC-SVC-12Benchmarks & Evals
Evals for your own tasks, a stable frame of comparison for new model releases, and evaluation run as an independent service.
Benchmarks & evals →Have a task a general model keeps getting wrong?
Tell me what it is and I'll tell you whether a trained model is worth it.