Private audio AI
Compare speech recognition, text-to-speech and conversational audio models. Real-time usability depends on the complete audio path, including capture, codec, chunking, inference and playback, not only the checkpoint size.
Selection approach
Choose the model class first: transcription, speech generation or end-to-end conversation. Language coverage, diarisation, voice rights and streaming latency can rule out an otherwise capable model.
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6 current models
Showing 6 models
Qwen
A compact automatic speech recognition checkpoint released in July 2026 for multilingual transcription and audio understanding.
OpenAI
OpenAI's 1.55B-parameter multilingual speech-recognition and translation checkpoint, widely supported across transcription runtimes.
Mistral AI
A 4B-class real-time automatic speech recognition model released by Mistral AI for low-latency streaming transcription.
Google DeepMind
Google DeepMind's 12-billion-parameter instruction-tuned Gemma 4 model, supporting text, images, video and audio input with text output.
Qwen
A multilingual text-to-speech model with streaming and non-streaming generation, instruction-controlled delivery and nine supplied voice timbres.
StepFun
An end-to-end audio-language model for spoken conversation, audio understanding, paralinguistic cues and audio tool use.
No model matches that search and workload combination.
Model-to-hardware fit
Use the precise model version, numerical format and complete software pipeline intended for production.
Check whether it can load on one GPU or requires supported multi-GPU loading.
Allow for context, cache, batch, media encoders and concurrent users.
Measure quality, latency and stability on representative work.
Model weights are only one part of the system. Data access, runtime software, storage, monitoring and administrator handover also affect a reliable deployment.
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