| --- |
| license: apache-2.0 |
| language: |
| - en |
| base_model: |
| - hexgrad/Kokoro-82M |
| pipeline_tag: text-to-speech |
| --- |
| <div align="center"> |
| <img src="https://huggingface.co/datasets/Quantamhash/Assets/resolve/main/images/dark_logo.png" |
| alt="Title card" |
| style="width: 500px; |
| height: auto; |
| object-position: center top;"> |
| </div> |
| **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. |
|
|
| <audio controls><source src="https://huggingface.co/Quantamhash/Qhash-TTS/resolve/main/samples/HEARME.wav" type="audio/wav"></audio> |
|
|
|
|
| ### 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 |
|
|
