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README.md
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@@ -3,5 +3,43 @@ library_name: transformers.js
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https://huggingface.co/facebook/mms-tts-hin with ONNX weights to be compatible with Transformers.js.
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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`).
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---
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https://huggingface.co/facebook/mms-tts-hin with ONNX weights to be compatible with Transformers.js.
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## Usage (Transformers.js)
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If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using:
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```bash
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npm i @xenova/transformers
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```
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**Example:** Generate Hindi speech with `Xenova/mms-tts-hin`.
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```js
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import { pipeline } from '@xenova/transformers';
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// Create a text-to-speech pipeline
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const synthesizer = await pipeline('text-to-speech', 'Xenova/mms-tts-hin', {
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quantized: false, // Remove this line to use the quantized version (default)
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});
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// Generate speech
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const output = await synthesizer('नमस्ते');
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console.log(output);
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// {
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// audio: Float32Array(11264) [ ... ],
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// sampling_rate: 16000
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// }
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```
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Optionally, save the audio to a wav file (Node.js):
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```js
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import wavefile from 'wavefile';
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import fs from 'fs';
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const wav = new wavefile.WaveFile();
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wav.fromScratch(1, output.sampling_rate, '32f', output.audio);
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fs.writeFileSync('out.wav', wav.toBuffer());
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```
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<audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/bvNGhhyJ5jX6WMdZVpYxI.wav"></audio>
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---
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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`).
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