Make the stack work on your real audio.
Wordcab Adapt covers the work between a promising pilot and a production-ready rollout — data preparation, evaluation, fine-tuning, and validation against the audio conditions that matter.
Benchmark WER is not production WER.
Generic ASR degrades 2.8–5.7× from benchmark to production. Clean dictation: 8.7% WER. Multi-speaker contact-center audio on the same model: over 50%. That's the gap Adapt closes.
Targeted fine-tuning on 10–100 hours of representative audio typically yields 10–30% relative WER reduction. No retraining from scratch. No customer audio leaving the boundary.
The gap is real
92% accuracy on clean headsets drops to 78% in a conference room and 65% on a mobile call. Your rollout sees the worst end.
Adapt closes it
10–100 hours of labeled domain audio, prepared and tuned inside your infrastructure. Iterate weekly — not quarterly.
This is the productionization layer.
Data intake and cleanup
Prepare raw customer audio in approved environments.
Audio comes in messy — noisy recordings, overlapping speakers, inconsistent formats. Data preparation happens inside the customer's approved infrastructure, not in an external pipeline the security team cannot inspect.
Evaluation and benchmarking
Test model options against the workflow and the success criteria that matter.
Benchmarks should match the actual workload — not a generic test set. Evaluate model candidates against real audio conditions, domain vocabulary, and the quality bar the downstream workflow requires.
Fine-tuning and adaptation
Improve performance on specialized language, accents, telephony audio, and multi-speaker conversations.
When the default model is close but not close enough, fine-tuning closes the gap. Domain vocabulary, accent coverage, and telephony noise handling improve without starting from scratch.
Rollout validation
Confirm that the stack is ready before wider deployment.
Validate before the rollout starts grading you. Run the tuned stack against held-out data, confirm the quality metrics, and make sure the system performs in the conditions that matter.
Evaluate the model stack against the workflow, not the hype.
Qwen3-ASR
Strong open STT baseline with room to optimize around real workloads, latency targets, and domain-specific audio.
Voxtral Realtime
For live latency. Tune the delay-versus-quality tradeoff instead of pretending it doesn't exist.
Cohere Transcribe 2B
Batch transcription at scale. The question is throughput — not a flashy live demo.
Qwen3-TTS
When speech quality and responsiveness both matter — private assistants and real-time products.
Kokoro
For a lighter local speech stack — simpler ops, practical path to fully local speech generation.
Wordcab Adapt matters when accuracy risk is the real blocker.
Frequently asked questions
We are close on quality, but not close enough. Is Adapt meant for that middle ground?
Is Adapt only about fine-tuning?
Can adaptation happen inside approved environments?
Do all customers need Adapt?
Get to production quality before the rollout starts grading you.
If your team already knows generic voice models will struggle on real production audio — Wordcab Adapt is the clearer path to usable performance.
Talk to an Engineer
We usually respond within one business day.