Build OpenAI-compatible TTS endpoints with streaming and batch inference.
npx skills add https://github.com/jayll1303/AIEKit --skill openai-audio-api
Please help me install this Agent Skill. Skill: openai-audio-api Source: https://github.com/jayll1303/AIEKit/tree/main/.kiro/skills/openai-audio-api Command: npx skills add https://github.com/jayll1303/AIEKit --skill openai-audio-api
Building, deploying, and operating production-quality audio/speech HTTP APIs that are compatible with OpenAI-style endpoints is complex and error-prone; this Skill provides patterns to implement low-latency TTS synthesis, sentence-level streaming, concurrency control, and robust production concerns so teams can reliably serve audio models behind HTTP.
Create a FastAPI audio server exposing POST /v1/audio/speech that streams sentence-level PCM and uses a Semaphore plus ThreadPoolExecutor with optional dynamic batching for high-throughput inference.
High-intent search queries and answers about installing and using this skill.
Build an OpenAI-compatible TTS API with FastAPI by exposing POST /v1/audio/speech endpoints that stream sentence-level PCM output. This approach wraps audio ML models behind HTTP, enabling drop-in SDK compatibility and low-latency speech synthesis for single-GPU servers.
Handle concurrency in a streaming audio API using a Semaphore combined with a ThreadPoolExecutor to offload inference tasks. This pattern controls parallel requests, prevents memory overload on single-worker servers, and maintains responsive audio streaming under high load.
Yes, pydantic-settings configuration manages audio API deployments by validating environment variables and application settings. This ensures robust configuration for model aliasing, concurrency limits, and endpoint routing in production OpenAI-compatible audio servers.
Yes, the audio API supports WAV encoding utilities alongside raw PCM output streaming. These encoding utilities transform sentence-level PCM chunks into properly formatted WAV files, ensuring broad compatibility with standard audio playback clients.
Implement an adapter pattern for upstream model changes by creating compatibility layers that translate between the OpenAI-compatible API interface and underlying audio ML models. This isolates model-specific logic, allowing seamless swapping of upstream TTS engines without breaking the HTTP API contract.
yashvinthanManage Python application configuration with pydantic-settings and environment variables.
Supporter09Serve large language models with vLLM high-throughput inference and OpenAI-compatible endpoints.
iani-kuliDeploy vLLM inference servers with quantization, tensor parallelism, and OpenAI-compatible APIs.
akrindevGenerates speech from text using Google Gemini TTS models via Python scripts.
