Qhash-v0.1 is a leading open-weight text-to-speech model trained on more than 200k hours of varied multilingual speech, delivering expressiveness and quality on par with—or even surpassing—top TTS providers.
Our model enables highly natural speech generation from text prompts when given a speaker embedding or audio prefix, and can accurately perform speech cloning when given a reference clip spanning just a few seconds. The conditioning setup also allows for fine control over speaking rate, pitch variation, audio quality, and emotions such as happiness, fear, sadness, and anger. The model outputs speech natively at 44kHz.
Qhash follows a straightforward architecture: text normalization and phonemization via eSpeak, followed by DAC token prediction through a transformer or hybrid backbone. An overview of the architecture can be seen below.
1uv run gradio_interface.py
2# python gradio_interface.py
This should produce a sample.wav file in your project root directory.
For repeated sampling we highly recommend using the gradio interface instead, as the minimal example needs to load the model every time it is run.
Features
Zero-shot TTS with voice cloning: Input desired text and a 10-30s speaker sample to generate high quality TTS output
Audio prefix inputs: Add text plus an audio prefix for even richer speaker matching. Audio prefixes can be used to elicit behaviours such as whispering which can otherwise be challenging to replicate when cloning from speaker embeddings
Multilingual support: Qhash-v0.1 supports English, Japanese, Chinese, French, and German
Audio quality and emotion control: Qhash offers fine-grained control of many aspects of the generated audio. These include speaking rate, pitch, maximum frequency, audio quality, and various emotions such as happiness, anger, sadness, and fear.
Fast: our model runs with a real-time factor of ~2x on an RTX 4090
Gradio WebUI: Qhash comes packaged with an easy to use gradio interface to generate speech
Simple installation and deployment: Qhash can be installed and deployed simply using the docker file packaged with our repository.
Installation
At the moment this repository only supports Linux systems (preferably Ubuntu 22.04/24.04) with recent NVIDIA GPUs (3000-series or newer, 6GB+ VRAM).
System dependencies
Qhash depends on the eSpeak library phonemization. You can install it on Ubuntu with the following command:
apt install -y espeak-ng
Python dependencies
We highly recommend using a recent version of uv for installation. If you don't have uv installed, you can install it via pip: pip install -U uv.
Installing into a new uv virtual environment (recommended)
bash
1uv sync2uv sync --extra compile
Installing into the system/actived environment using uv
bash
1uv pip install -e .2uv pip install -e .[compile]
Installing into the system/actived environment using pip
