Update README.md

README.md

@@ -16,6 +16,8 @@ provide speech technology across a diverse range of languages. You can find more
 and their ISO 639-3 codes in the [MMS Language Coverage Overview](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html),
 and see all MMS-TTS checkpoints on the Hugging Face Hub: [facebook/mms-tts](https://huggingface.co/models?sort=trending&search=facebook%2Fmms-tts).
 
+MMS-TTS is available in the 🤗 Transformers library from version 4.33 onwards.
+
 ## Model Details
 
 VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end 
@@ -47,7 +49,7 @@ import torch
 model = VitsModel.from_pretrained("Matthijs/mms-tts-eng")
 tokenizer = AutoTokenizer.from_pretrained("Matthijs/mms-tts-eng")
 
-text = "Hey, it's Hugging Face on the phone!"
+text = "Hey, it's Hugging Face on the phone"
 inputs = tokenizer(text, return_tensors="pt")
 
 with torch.no_grad():
@@ -57,7 +59,6 @@ from IPython.display import Audio
 Audio(output[0], rate=16000)
 ```
 
-
 ## BibTex citation
 
 This model was developed by Vineel Pratap et al. from Meta AI. If you use the model, consider citing the MMS paper:
@@ -71,6 +72,6 @@ This model was developed by Vineel Pratap et al. from Meta AI. If you use the mo
 }
 ```
 
-## License
+## License
 
 The model is licensed as **CC-BY-NC 4.0**.