Transform your applications and workflows into powerful agentic systems at global scale.
Gemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
Get Started Free
$300 Free Credits for Your Google Cloud Projects
Start building on Google Cloud with $300 in free credits. No commitment, no credit card required until you're ready to scale.
Launch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
The official Python library for the Fish Audio API
...The package is designed for Python applications that need speech generation without manually handling raw HTTP requests. It supports synchronous usage for simple scripts and provides utilities that make generated audio easier to play or export. The SDK is useful for developers building voice products, prototypes, assistants, content tools, or automated audio pipelines. Its main value is simplifying integration with Fish Audio services through a clean Python interface.
A fast TTS architecture with conditional flow matching
Matcha-TTS is a non-autoregressive neural text-to-speech architecture that uses conditional flow matching to generate speech quickly while maintaining natural quality. It models speech as an ODE-based generative process, and conditional flow matching lets it reach high-quality audio in only a few synthesis steps, which greatly reduces latency compared to score-matching diffusion approaches. The model is fully probabilistic, so it can generate diverse realizations of the same text while still...
...It provides emotional modeling through “emo embeddings,” allowing voices to be conditioned on different affective states during synthesis. Releases include optimizations for Japanese and English alignment, expanded training data, spec caching and pre-generation tools, as well as ONNX export for more lightweight inference deployments.