speecht5 tts EU AI Act Compliance Profile

https://huggingface.co/microsoft/speecht5_tts with ONNX weights to be compatible with Transformers.js.

Usage (Transformers.js)

If you haven't already, you can install the Transformers.js JavaScript library from NPM using:

npm i @huggingface/transformers

Example: Text-to-speech pipeline.

import { pipeline } from '@huggingface/transformers';

// Create a text-to-speech pipeline
const synthesizer = await pipeline('text-to-speech', 'Xenova/speecht5_tts', { dtype: 'fp32' });  // Options: "fp32", "fp16", "q8", "q4"

// Generate speech
const speaker_embeddings = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/speaker_embeddings.bin';
const result = await synthesizer('Hello, my dog is cute', { speaker_embeddings });
console.log(result);
// {
//   audio: Float32Array(26112) [-0.00005657337896991521, 0.00020583874720614403, ...],
//   sampling_rate: 16000
// }

Optionally, save the audio to a wav file (Node.js):

import wavefile from 'wavefile';
import fs from 'fs';

const wav = new wavefile.WaveFile();
wav.fromScratch(1, result.sampling_rate, '32f', result.audio);
fs.writeFileSync('result.wav', wav.toBuffer());

<audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/on1ij9Y269ne9zlYN9mdb.wav"></audio>

Example: Load processor, tokenizer, and models separately.

import { AutoTokenizer, AutoProcessor, SpeechT5ForTextToSpeech, SpeechT5HifiGan, Tensor } from '@huggingface/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', { dtype: 'fp

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Library

Base Model