AIAny
AI Audio2023
GitHub

fish-speech

Generates expressive multilingual speech from text, with sub-word control over prosody and emotion via inline tags like [whisper] or [angry]. Handles multi-speaker, multi-turn dialogue; the weights ship under a research-only license.

Introduction

Most open text-to-speech projects treat emotion as an afterthought — a neutral voice with maybe a speed slider. Fish Speech inverts that priority: prosody and emotion are steerable at the sub-word level through inline natural-language tags, so a single sentence can slide from [whisper] to [excited] without re-recording. Under the hood sits Fish Audio S2 Pro, a Dual-Autoregressive model trained on 10M+ hours of audio across 80+ languages with reinforcement-learning alignment.

What Sets It Apart
  • Sub-word emotional control — tags like [angry] or [whisper] apply mid-sentence, giving direction-level nuance instead of one flat delivery per clip.
  • Native multi-speaker, multi-turn dialogue — generate whole conversations or character audio in a single pass rather than stitching separate renders.
  • Serving-ready by design — CLI, WebUI, and server inference plus SGLang-Omni and vLLM-Omni recipes mean it drops into production stacks, not just notebooks.
  • Breadth over 80 languages — the same model handles cross-lingual cloning, so you are not swapping engines per locale.
Who It's For

Great fit if you need expressive, controllable multilingual TTS or voice cloning and can operate within a research-oriented license. Look elsewhere if you need clean commercial redistribution rights out of the box — the Fish Audio Research License restricts how weights and outputs may be used, so read it before shipping anything customer-facing.

Information

  • Websitegithub.com
  • OrganizationsFish Audio
  • AuthorsFish Audio (fishaudio)
  • Published date2023/10/10

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