ylacombe / mms-guj-finetuned-monospeaker

Model

This is a finetuned version of the Gujarati version of Massively Multilingual Speech (MMS) models, which are light-weight, low-latency TTS models based on the VITS architecture .

It was trained in around 20 minutes with as little as 80 to 150 samples , on this Gujarati dataset .

Training recipe available in this github repository: ylacombe/finetune-hf-vits .

Usage

Transformers

from transformers import pipeline
import scipy

model_id = "ylacombe/mms-guj-finetuned-monospeaker"
synthesiser = pipeline("text-to-speech", model_id) # add device=0 if you want to use a GPU

speech = synthesiser("Hola, ¿cómo estás hoy?")

scipy.io.wavfile.write("finetuned_output.wav", rate=speech["sampling_rate"], data=speech["audio"])

Transformers.js

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

npm i @xenova/transformers

Example: Generate Gujarati speech with ylacombe/mms-guj-finetuned-monospeaker .

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

// Create a text-to-speech pipeline
const synthesizer = await pipeline('text-to-speech', 'ylacombe/mms-guj-finetuned-monospeaker', {
    quantized: false, // Remove this line to use the quantized version (default)
});

// Generate speech
const output = await synthesizer('Hola, ¿cómo estás hoy?');
console.log(output);
// {
//   audio: Float32Array(69888) [ ... ],
//   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, output.sampling_rate, '32f', output.audio);
fs.writeFileSync('out.wav', wav.toBuffer());