Как реализовать voice-agent с low-latency (streaming STT + LLM + TTS)?

To implement a low-latency voice agent that includes streaming speech recognition (streaming STT), working with large language models (LLM), and speech synthesis (TTS), it is necessary to ensure efficient and parallel data processing.

Main components and approaches:

1. Streaming STT (Speech-to-Text)

• Use models that support streaming recognition, such as Whisper Streaming, DeepSpeech, or commercial APIs with low latency.
• Process audio in chunks (frames), immediately sending intermediate text results.

2. Integration with LLM

• Pass the obtained text to the LLM for understanding and response generation.
• To reduce latency, use local models or optimized versions of LLM.
• Use asynchronous processing to avoid blocking the STT stream.

3. Streaming TTS (Text-to-Speech)

• Use TTS systems that support streaming audio generation (e.g., Tacotron 2 with WaveGlow).
• Start speech synthesis immediately after receiving the initial parts of the response from the LLM.

4. Architecture and communication

• Use message queues or event-driven architecture for data transfer between components.
• Process audio, text, and synthesis in parallel.

5. Optimizations

• Minimize buffering and delays at each stage.
• Use hardware acceleration (GPU, TPU).
• Cache frequently used responses.

Example of a simplified flow:

Audio -> Streaming STT -> Partial text -> LLM (asynchronously) -> Partial response -> Streaming TTS -> Audio output

This approach allows starting the playback of the response while the LLM is still generating the rest, reducing overall latency.