Update README.md

README.md

@@ -1,3 +1,67 @@
 ---
 library_name: transformers.js
----
+---
+
+https://huggingface.co/microsoft/speecht5_hifigan with ONNX weights to be compatible with Transformers.js.
+
+
+## Usage (Transformers.js)
+
+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:
+```bash
+npm i @xenova/transformers
+```
+
+**Example:** Generate speech from text.
+```js
+import { AutoTokenizer, AutoProcessor, SpeechT5ForTextToSpeech, SpeechT5HifiGan, Tensor } from '@xenova/transformers';
+
+// Load the tokenizer and processor
+const tokenizer = await AutoTokenizer.from_pretrained('Xenova/speecht5_tts');
+const processor = await AutoProcessor.from_pretrained('Xenova/speecht5_tts');
+
+// Load the models
+// NOTE: We use the unquantized versions as they are more accurate
+const model = await SpeechT5ForTextToSpeech.from_pretrained('Xenova/speecht5_tts', { quantized: false });
+const vocoder = await SpeechT5HifiGan.from_pretrained('Xenova/speecht5_hifigan', { quantized: false });
+
+// Load speaker embeddings from URL
+const speaker_embeddings_data = new Float32Array(
+    await (await fetch('https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/speaker_embeddings.bin')).arrayBuffer()
+);
+const speaker_embeddings = new Tensor(
+    'float32',
+    speaker_embeddings_data,
+    [1, speaker_embeddings_data.length]
+)
+
+// Run tokenization
+const { input_ids } = tokenizer('Hello, my dog is cute');
+
+// Generate waveform
+const { waveform } = await model.generate_speech(input_ids, speaker_embeddings, { vocoder });
+console.log(waveform)
+// Tensor {
+//   dims: [ 26112 ],
+//   type: 'float32',
+//   size: 26112,
+//   data: Float32Array(26112) [ -0.00043630177970044315, -0.00018082228780258447, ... ],
+// }
+```
+
+Optionally, save the audio to a wav file (Node.js):
+```js
+// Write to file (Node.js)
+import wavefile from 'wavefile';
+import fs from 'fs';
+
+const wav = new wavefile.WaveFile();
+wav.fromScratch(1, processor.feature_extractor.config.sampling_rate, '32f', waveform.data);
+fs.writeFileSync('out.wav', wav.toBuffer());
+```
+
+<audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/on1ij9Y269ne9zlYN9mdb.wav"></audio>
+
+---
+
+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`).