Create README.md

README.md

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+---
+library_name: transformers
+pipeline_tag: text-to-speech
+tags:
+- transformers.js
+- mms
+- vits
+license: cc-by-nc-4.0
+datasets:
+- ylacombe/google-colombian-spanish
+language:
+- es
+---
+
+## Model
+
+This is a finetuned version of the [Spanish version](https://huggingface.co/facebook/mms-tts-spa) of Massively Multilingual Speech (MMS) models, which are light-weight, low-latency TTS models based on the [VITS architecture](https://huggingface.co/docs/transformers/model_doc/vits).
+
+It was trained in around **20 minutes** with as little as **80 to 150 samples**, on this [Colombian Spanish dataset](https://huggingface.co/datasets/ylacombe/google-colombian-spanish).
+
+Training recipe available in this [github repository: **ylacombe/finetune-hf-vits**](https://github.com/ylacombe/finetune-hf-vits).
+
+
+## Usage
+
+### Transformers
+
+```python
+from transformers import pipeline
+import scipy
+
+model_id = "ylacombe/mms-spa-finetuned-colombian-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](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 Spanish speech with `ylacombe/mms-spa-finetuned-colombian-monospeaker`.
+```js
+import { pipeline } from '@xenova/transformers';
+
+// Create a text-to-speech pipeline
+const synthesizer = await pipeline('text-to-speech', 'ylacombe/mms-spa-finetuned-colombian-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):
+```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());
+```
+
+
+<audio controls src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/6FvN6zFSHGeenWS2-H8xv.wav"></audio>