**Qhash-TTS** is an open-weight TTS model with 84 million parameters. Despite its lightweight architecture, it delivers comparable quality to larger models while being significantly faster and more cost-efficient. With Apache-licensed weights, Qhash-TTS can be deployed anywhere from production environments to personal projects.
### Releases
| Model | Published | Training Data | Langs & Voices | SHA256 |
| ----- | --------- | ------------- | -------------- | ------ |
| **v1.0** | **2025 Jan 27** | **Few hundred hrs** | [**8 & 54**](https://huggingface.co/Quantamhash/Qhash-TTS/blob/main/VOICES.md) | `496dba11` |
| [v0.19] | 2024 Dec 25 | <100 hrs | 1 & 10 | `3b0c392f` |
| Training Costs | v0.19 | v1.0 | **Total** |
| -------------- | ----- | ---- | ----- |
| in A100 80GB GPU hours | 500 | 500 | **1000** |
| average hourly rate | $0.80/h | $1.20/h | **$1/h** |
| in USD | $400 | $600 | **$1000** |
### Usage
You can run this basic cell on [Google Colab](https://colab.research.google.com/). [Listen to samples](https://huggingface.co/Quantamhash/Qhash-TTS/blob/main/SAMPLES.md). For more languages and details, see [Advanced Usage](https://github.com/hexgrad/kokoro?tab=readme-ov-file#advanced-usage).
```py
!pip install -q kokoro>=0.9.2 soundfile
!apt-get -qq -y install espeak-ng > /dev/null 2>&1
from kokoro import KPipeline
from IPython.display import display, Audio
import soundfile as sf
import torch
pipeline = KPipeline(lang_code='a')
text = '''
Qhash is an open-weight TTS model with 84 million parameters. Despite its lightweight architecture, it delivers comparable quality to larger models while being significantly faster and more cost-efficient. With Apache-licensed weights, Qhash-TTS can be deployed anywhere from production environments to personal projects.
'''
generator = pipeline(text, voice='af_heart')
for i, (gs, ps, audio) in enumerate(generator):
print(i, gs, ps)
display(Audio(data=audio, rate=24000, autoplay=i==0))
sf.write(f'{i}.wav', audio, 24000)
```
Under the hood, `Qhash-TTS` uses [`misaki`](https://pypi.org/project/misaki/), a G2P library at https://github.com/hexgrad/misaki
