| The following code example runs a forward pass using the MMS-TTS English checkpoint: | |
| thon | |
| import torch | |
| from transformers import VitsTokenizer, VitsModel, set_seed | |
| tokenizer = VitsTokenizer.from_pretrained("facebook/mms-tts-eng") | |
| model = VitsModel.from_pretrained("facebook/mms-tts-eng") | |
| inputs = tokenizer(text="Hello - my dog is cute", return_tensors="pt") | |
| set_seed(555) # make deterministic | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| waveform = outputs.waveform[0] | |
| The resulting waveform can be saved as a .wav file: | |
| thon | |
| import scipy | |
| scipy.io.wavfile.write("synthesized_speech.wav", rate=model.config.sampling_rate, data=waveform) | |
| Or displayed in a Jupyter Notebook / Google Colab: | |
| thon | |
| from IPython.display import Audio | |
| Audio(waveform, rate=model.config.sampling_rate) | |
| For certain languages with non-Roman alphabets, such as Arabic, Mandarin or Hindi, the uroman | |
| perl package is required to pre-process the text inputs to the Roman alphabet. |