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Voice AI

Speech-to-Text, Text-to-Speech, realtime models and voice AI agents.

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“Speech-to-Text” — 4 articles ✕

Voice AI· Important· 🧪 Worth Testing

IBM refreshes Granite Speech lineup: new ASR model, quicker edge transcription, sharper speaker attribution

IBM rolled out three updates to its Granite Speech family: a brand-new 2B speech-language model (granite-speech-5.0-2b), a revised 470M Turbo CTC model built for speed, and an improved 4.1-2B-Plus model tuned for multi-speaker recordings. Together they target high-accuracy transcription/translation, fast on-device recognition, and accurate speaker/timestamp labeling. All three remain under Apache 2.0.

Hugging Face
Voice AI· Important· 🧪 Worth Testing

Mistral launches Voxtral 3 voice stack: speech models plus a real-time agent SDK

Mistral AI released Voxtral 3, a set of speech recognition and speech synthesis models paired with an SDK that lets a single real-time pipeline listen, reason, call tools and speak back. The recognition models (Small, Mini and Realtime Transcribe 3) cover 50+ languages with speaker diarization, keyword-biasing for technical vocabulary, and transcription latency under 500ms, which Mistral claims beats Whisper, Scribe v2 and Gemini 3.5 Transcribe on its benchmarks. The speech synthesis model, Mini TTS 3, handles 35+ languages and 50+ dialects with emotion, pacing and emphasis that adapt to context, priced at $13 per million characters, and a separate Pro Voice feature can reproduce a person's voice and emotional range from just 30 minutes of recorded audio. All components can run through Mistral's EU-hosted API or be deployed on a customer's own servers.

Mistral AI
Voice AI· Important· 🧪 Worth Testing

Deepgram cuts batch transcription costs by porting Nova-3 to AWS Trainium2

AWS and Deepgram jointly rebuilt the Nova-3 speech-to-text model to run natively on Trainium2 chips instead of standard GPU-based PyTorch setups, targeting large offline transcription jobs. The work involved rewriting bottleneck layers as custom kernels and replacing Deepgram's Python serving layer with a Rust runtime calling Trainium directly. The result: a single trn2.3xlarge instance running eight parallel model copies delivers 11-70% more transcribed audio per dollar than Deepgram's prior infrastructure, with similar savings potentially available to customers self-hosting on SageMaker.

AWS Machine Learning
Voice AI· Important· 🧪 Worth Testing

Deepgram's Flux STT cuts unwanted interruptions in Anam's avatar conversations by ~50%

Anam, which builds photorealistic interactive avatars, switched to Deepgram's Flux speech-to-text model to solve turn-taking problems in unscripted, real-time conversation. In A/B testing against a self-hosted open-source STT option, Flux cut unwanted avatar barge-ins by roughly 50% with no added latency penalty, and complaints about turn-taking on English sessions dropped to near zero. Anam deployed Flux self-hosted on its own Kubernetes/H100 infrastructure, rolling it out to 100% of turnkey users after a two-week self-hosted deployment (versus days for Deepgram's hosted option).

Deepgram