Verified project record

canopyai/Orpheus-TTS

Orpheus TTS is an open-source text-to-speech framework that generates natural speech, enables zero-shot voice cloning, and supports low-latency streaming. It is intended for developers and researchers who require Python-based integration and are equipped to manage large language model dependencies.

Project overview

Project type: Model Runtime · Audio & Speech
Use cases: Video Creation · Audio & Speech
Deployment: Refer to project documentation
License: Apache-2.0

Best for

Developers, AI engineers, and researchers who require an open-source library for audio and local-llm voice synthesis.

Key capabilities

Generates text-to-speech with realistic intonation, emotion, and rhythm.
Clones voices without requiring prior fine-tuning.
Provides approximately 200ms streaming latency for realtime applications, reducible to approximately 100ms with input streaming.
Allows users to fine-tune the model on custom text and speech datasets for specific voices.
Supports training the base model from scratch or extended training using HuggingFace Trainer and Transformers.
Supports adding watermarks to generated audio using Silent Cipher.
Provides an implementation example for running inference without a GPU using llama.cpp.

Limitations and risks

The realtime streaming package has a glitch that occasionally skips frames.
vllm may have slightly buggy versions requiring reverts.
Cannot verify that recommended third-party implementations are completely accurate or bug free.

Getting started

Requires Python package installation and loading large LLM models. Setup difficulty is medium, with potential issues surrounding vllm dependencies requiring specific version installs.

Evidence and sources

README: [5/2025] We've partnered with [Baseten](https://www.baseten.co/blog/canopy-labs-selects-baseten-as-preferred-inference-provider-for-orpheus-tts-model) to bring highly optimized in…
README: Orpheus TTS is a SOTA open-source text-to-speech system built on the Llama-3b backbone. Orpheus demonstrates the emergent capabilities of using LLMs for speech synthesis.
README: - **Human-Like Speech**: Natural intonation, emotion, and rhythm that is superior to SOTA closed source models - **Zero-Shot Voice Cloning**: Clone voices without prior fine-tunin…
README: We provide 2 English models, and additionally we offer the data processing scripts and sample datasets to make it very straightforward to create your own finetune.
README: We offer a standardised prompt format across languages, and these notebooks illustrate how to use our models in English.

Reviewed at: 2026-07-15T18:38:23Z