Update README.md

README.md

@@ -34,8 +34,8 @@ First, load the processor and a checkpoint of the model:
 ```python
 from transformers import AutoProcessor, SeamlessM4TModel
 
-processor = AutoProcessor.from_pretrained("ylacombe/hf-seamless-m4t-medium")
-model = SeamlessM4TModel.from_pretrained("ylacombe/hf-seamless-m4t-medium")
+processor = AutoProcessor.from_pretrained("ylacombe/hf-seamless-m4t-large")
+model = SeamlessM4TModel.from_pretrained("ylacombe/hf-seamless-m4t-large")
 ```
 
 You can seamlessly use this model on text or on audio, to generated either translated text or translated audio.
@@ -89,17 +89,17 @@ For example, you can replace the previous snippet with the model dedicated to th
 
 ```python
 from transformers import SeamlessM4TForSpeechToSpeech
-model = SeamlessM4TForSpeechToSpeech.from_pretrained("ylacombe/hf-seamless-m4t-medium")
+model = SeamlessM4TForSpeechToSpeech.from_pretrained("ylacombe/hf-seamless-m4t-large")
 ```
 
 
 ### Text
 
-Similarly, you can generate translated text from text or audio files, this time using the dedicated models.
+Similarly, you can generate translated text from text or audio files. This time, let's use the dedicated models as example.
 
 ```python
 from transformers import SeamlessM4TForSpeechToText
-model = SeamlessM4TForSpeechToText.from_pretrained("ylacombe/hf-seamless-m4t-medium")
+model = SeamlessM4TForSpeechToText.from_pretrained("ylacombe/hf-seamless-m4t-large")
 audio_sample = dataset["audio"][0]["array"]
 inputs = processor(audios = audio_sample, return_tensors="pt")
 
@@ -111,7 +111,7 @@ And from text:
 
 ```python
 from transformers import SeamlessM4TForTextToText
-model = SeamlessM4TForTextToText.from_pretrained("ylacombe/hf-seamless-m4t-medium")
+model = SeamlessM4TForTextToText.from_pretrained("ylacombe/hf-seamless-m4t-large")
 inputs = processor(text = "Hello, my dog is cute", src_lang="eng", return_tensors="pt")
 
 output_tokens = model.generate(**inputs, tgt_lang="fra")