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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- hexgrad/Kokoro-82M |
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pipeline_tag: text-to-speech |
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--- |
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<div align="center"> |
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<img src="https://huggingface.co/datasets/Quantamhash/Assets/resolve/main/images/dark_logo.png" |
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alt="Title card" |
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style="width: 500px; |
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height: auto; |
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object-position: center top;"> |
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</div> |
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**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. |
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<audio controls><source src="https://huggingface.co/Quantamhash/Qhash-TTS/resolve/main/samples/HEARME.wav" type="audio/wav"></audio> |
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### Releases |
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| Model | Published | Training Data | Langs & Voices | SHA256 | |
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| ----- | --------- | ------------- | -------------- | ------ | |
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| **v1.0** | **2025 Jan 27** | **Few hundred hrs** | [**8 & 54**](https://huggingface.co/Quantamhash/Qhash-TTS/blob/main/VOICES.md) | `496dba11` | |
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| [v0.19] | 2024 Dec 25 | <100 hrs | 1 & 10 | `3b0c392f` | |
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| Training Costs | v0.19 | v1.0 | **Total** | |
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| -------------- | ----- | ---- | ----- | |
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| in A100 80GB GPU hours | 500 | 500 | **1000** | |
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| average hourly rate | $0.80/h | $1.20/h | **$1/h** | |
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| in USD | $400 | $600 | **$1000** | |
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### Usage |
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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). |
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```py |
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!pip install -q kokoro>=0.9.2 soundfile |
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!apt-get -qq -y install espeak-ng > /dev/null 2>&1 |
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from kokoro import KPipeline |
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from IPython.display import display, Audio |
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import soundfile as sf |
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import torch |
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pipeline = KPipeline(lang_code='a') |
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text = ''' |
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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. |
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''' |
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generator = pipeline(text, voice='af_heart') |
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for i, (gs, ps, audio) in enumerate(generator): |
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print(i, gs, ps) |
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display(Audio(data=audio, rate=24000, autoplay=i==0)) |
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sf.write(f'{i}.wav', audio, 24000) |
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``` |
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Under the hood, `Qhash-TTS` uses [`misaki`](https://pypi.org/project/misaki/), a G2P library at https://github.com/hexgrad/misaki |
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