Instructions to use Xenova/tiny-random-vits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use Xenova/tiny-random-vits with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-to-speech', 'Xenova/tiny-random-vits');
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Download README.md from Xenova/tiny-random-vits: direct link, hf CLI and curl.
- Browser
- Download file 596 Bytes
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https://huggingface.co/Xenova/tiny-random-vits/resolve/main/README.md
- Command line
-
hf download hf://Xenova/tiny-random-vits/README.md
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curl -L -o README.md https://huggingface.co/Xenova/tiny-random-vits/resolve/main/README.md
596 Bytes
| base_model: echarlaix/tiny-random-vits | |
| library_name: transformers.js | |
| pipeline_tag: text-to-speech | |
| tags: | |
| - text-to-audio | |
| https://huggingface.co/echarlaix/tiny-random-vits with ONNX weights to be compatible with Transformers.js. | |
| Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`). |