Orpheus is a high-performance Text-to-Speech model fine-tuned for natural, emotional speech synthesis. This repository hosts the 8-bit quantised version of the 3B parameter model, optimised for efficiency while maintaining high-quality output.

Model Description

Orpheus-3b-FT-Q4_K_M is a 3 billion parameter Text-to-Speech model that converts text inputs into natural-sounding speech with support for multiple voices and emotional expressions. The model has been quantised to 8-bit (Q4_K_M) format for efficient inference, making it accessible on consumer hardware.

Key features:

8 distinct voice options with different characteristics
Support for emotion tags like laughter, sighs, etc.
Optimised for CUDA acceleration on RTX GPUs
Produces high-quality 24kHz mono audio
Fine-tuned for conversational naturalness

How to Use

This model is designed to be used with an LLM inference server that connects to the Orpheus-FastAPI frontend, which provides both a web UI and OpenAI-compatible API endpoints.

Compatible Inference Servers

This quantised model can be loaded into any of these LLM inference servers:

GPUStack - GPU optimised LLM inference server (My pick) - supports LAN/WAN tensor split parallelisation
LM Studio - Load the GGUF model and start the local server
llama.cpp server - Run with the appropriate model parameters
Any compatible OpenAI API-compatible server

Quick Start

Download this quantised model from lex-au's Orpheus-FASTAPI collection
Load the model in your preferred inference server and start the server.
Clone the Orpheus-FastAPI repository:

bash

git clone https://github.com/Lex-au/Orpheus-FastAPI.git
cd Orpheus-FastAPI

Configure the FastAPI server to connect to your inference server by setting the ORPHEUS_API_URL environment variable.
Follow the complete installation and setup instructions in the repository README.

Audio Samples

Listen to the model in action with different voices and emotions:

Default Voice Sample

Leah (Happy)

Tara (Sad)

Zac (Contemplative)

Available Voices

The model supports 8 different voices:

tara: Female, conversational, clear
leah: Female, warm, gentle
jess: Female, energetic, youthful
leo: Male, authoritative, deep
dan: Male, friendly, casual
mia: Female, professional, articulate
zac: Male, enthusiastic, dynamic
zoe: Female, calm, soothing

Emotion Tags

You can add expressiveness to speech by inserting tags:

<laugh>, <chuckle>: For laughter sounds
<sigh>: For sighing sounds
<cough>, <sniffle>: For subtle interruptions
<groan>, <yawn>, <gasp>: For additional emotional expression

Technical Specifications

Architecture: Specialised token-to-audio sequence model
Parameters: ~3 billion
Quantisation: 8-bit (GGUF Q4_K_M format)
Audio Sample Rate: 24kHz
Input: Text with optional voice selection and emotion tags
Output: High-quality WAV audio
Language: English
Hardware Requirements: CUDA-compatible GPU (recommended: RTX series)
Integration Method: External LLM inference server + Orpheus-FastAPI frontend

Limitations

Currently supports English text only
Best performance achieved on CUDA-compatible GPUs
Generation speed depends on GPU capability

License

This model is available under the Apache License 2.0.

Citation & Attribution

The original Orpheus model was created by Canopy Labs. This repository contains a quantised version optimised for use with the Orpheus-FastAPI server.

If you use this quantised model in your research or applications, please cite:

@misc{orpheus-tts-2025,
  author = {Canopy Labs},
  title = {Orpheus-3b-0.1-ft: Text-to-Speech Model},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/canopylabs/orpheus-3b-0.1-ft}}
}

@misc{orpheus-quantised-2025,
  author = {Lex-au},
  title = {Orpheus-3b-FT-Q4_K_M: Quantised TTS Model with FastAPI Server},
  note = {GGUF quantisation of canopylabs/orpheus-3b-0.1-ft},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/lex-au/Orpheus-3b-FT-Q4_K_M.gguf}}
}