Fish Audio
Clone a voice from a 10-second clip and generate lifelike speech via API or playground, the same TTS technology running inside HeyGen and Retell.
Overview
Fish Audio builds the text-to-speech models that quietly power voice features inside HeyGen, Retell, Sanas, and other AI products, and it also ships that same technology as a standalone playground and API. Its flagship model, S1, was trained on more than 2 million hours of audio and hits a 0.8% word error rate on the Seed TTS benchmark, with support for open-domain emotion and tone markers, cross-lingual speech, and voice cloning from as little as 10 to 30 seconds of reference audio with no separate training step. The API is pay-as-you-go from the first call, priced at $15 per million UTF-8 bytes of input text with no seat fees or minimum commitment, and a free-tier model variant exists for testing and prototyping without production guarantees. For teams that need to self-host, Fish Audio's core models ship as open weights under a paid commercial license, deployable in a private VPC, on-prem, or air-gapped environment. The consumer-facing playground adds credit-based subscription plans with unlimited public voice slots on every tier, making it accessible for solo creators as well as engineering teams shipping voice into their own product.
Key Features
- Zero-shot voice cloning from 10-30 seconds of reference audio
- S1 flagship model with open-domain emotion and tone markers
- Pay-as-you-go API with WebSocket streaming and Python/TypeScript SDKs
- Open-weight self-hosting option under a commercial license
- Multilingual, cross-lingual speech with no phoneme dependency
- • Same model quality already trusted in production by HeyGen and Retell
- • Genuinely usable free tier plus API pricing with no minimum commitment
- • Voice cloning quality holds up well even from short reference samples
- • Credit-to-minute math on the consumer plans takes a minute to understand
- • Best commercial voice cloning rights require a paid plan, not the free tier
- • Self-hosting the open-weight models is an enterprise-level engagement, not a quick setup
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