Problem solved

Real-time voice agents need transcription that is fast enough to feel conversational and smart enough to attribute speech to the right speaker. Traditional batch or high-latency ASR adds perceptible delay and often loses speaker context, which breaks turn-taking in live calls. Agent STT addresses this by providing a streaming speech-to-text API with approximately 350ms latency, built-in speaker identification, and broad language coverage, so developers can plug low-latency, speaker-aware transcription into voice agent frameworks instead of building or tuning ASR infrastructure themselves.

Use cases

• Powering real-time speech recognition inside AI voice agents and phone bots
• Adding speaker diarization so agents know which participant said what on a call
• Live captioning and real-time subtitling for broadcasts and streams
• Transcribing contact-center and customer-support conversations in real time
• Building multilingual voice interfaces across 55+ languages
• Integrating low-latency STT into agent frameworks such as LiveKit or Pipecat
• Media monitoring and content indexing workflows that need speaker attribution

Audience

• Developers
• ML Engineers
• Product Managers
• Enterprise
• Startups

Pros

• Low ~350ms latency tuned for conversational voice agents
• Built-in speaker diarization/speaker identification
• Coverage of 55+ languages
• Accessible via direct API or popular agent frameworks (LiveKit, Pipecat)
• Transparent usage pricing starting at $0.30/hour with $100 free credit

Cons

• Proprietary, usage-based commercial API rather than open source
• No public GitHub repository or self-hosting option for the hosted service
• Pricing and quotas beyond the entry tier require contacting sales or checking the pricing page
• Exact model architecture and benchmarks are only partially disclosed publicly
• Latency and accuracy may vary by language, audio quality, and network conditions