Update README.md

README.md

@@ -37,9 +37,9 @@ We provide extensive evaluation results of SeamlessM4T-Medium and SeamlessM4T-La
 First, load the processor and a checkpoint of the model:
 
 ```python
-from transformers import AutoProcessor, SeamlessM4TModel
-processor = AutoProcessor.from_pretrained("facebook/hf-seamless-m4t-large")
-model = SeamlessM4TModel.from_pretrained("facebook/hf-seamless-m4t-large")
+>>> from transformers import AutoProcessor, SeamlessM4TModel
+>>> processor = AutoProcessor.from_pretrained("facebook/hf-seamless-m4t-large")
+>>> model = SeamlessM4TModel.from_pretrained("facebook/hf-seamless-m4t-large")
 ```
 
 You can seamlessly use this model on text or on audio, to generated either translated text or translated audio.
@@ -47,14 +47,14 @@ You can seamlessly use this model on text or on audio, to generated either trans
 Here is how to use the processor to process text and audio:
 
 ```python
-# let's load an audio sample from an Arabic speech corpus
-from datasets import load_dataset
-dataset = load_dataset("arabic_speech_corpus", split="test", streaming=True)
-audio_sample = next(iter(dataset))["audio"]
-# now, process it
-audio_inputs = processor(audios=audio_sample["array"], return_tensors="pt")
-# now, process some English test as well
-text_inputs = processor(text = "Hello, my dog is cute", src_lang="eng", return_tensors="pt")
+>>> # let's load an audio sample from an Arabic speech corpus
+>>> from datasets import load_dataset
+>>> dataset = load_dataset("arabic_speech_corpus", split="test", streaming=True)
+>>> audio_sample = next(iter(dataset))["audio"]
+>>> # now, process it
+>>> audio_inputs = processor(audios=audio_sample["array"], return_tensors="pt")
+>>> # now, process some English test as well
+>>> text_inputs = processor(text = "Hello, my dog is cute", src_lang="eng", return_tensors="pt")
 ```
 
 
@@ -63,8 +63,8 @@ text_inputs = processor(text = "Hello, my dog is cute", src_lang="eng", return_t
 [`SeamlessM4TModel`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TModel) can *seamlessly* generate text or speech with few or no changes. Let's target Russian voice translation:
 
 ```python
-audio_array_from_text = model.generate(**text_inputs, tgt_lang="rus")[0].cpu().numpy().squeeze()
-audio_array_from_audio = model.generate(**audio_inputs, tgt_lang="rus")[0].cpu().numpy().squeeze()
+>>> audio_array_from_text = model.generate(**text_inputs, tgt_lang="rus")[0].cpu().numpy().squeeze()
+>>> audio_array_from_audio = model.generate(**audio_inputs, tgt_lang="rus")[0].cpu().numpy().squeeze()
 ```
 
 With basically the same code, I've translated English text and Arabic speech to Russian speech samples.
@@ -75,12 +75,12 @@ Similarly, you can generate translated text from audio files or from text with t
 This time, let's translate to French.
 
 ```python 
-# from audio
-output_tokens = model.generate(**audio_inputs, tgt_lang="fra", generate_speech=False)
-translated_text_from_audio = processor.decode(output_tokens[0].tolist()[0], skip_special_tokens=True)
-# from text
-output_tokens = model.generate(**text_inputs, tgt_lang="fra", generate_speech=False)
-translated_text_from_text = processor.decode(output_tokens[0].tolist()[0], skip_special_tokens=True)
+>>> # from audio
+>>> output_tokens = model.generate(**audio_inputs, tgt_lang="fra", generate_speech=False)
+>>> translated_text_from_audio = processor.decode(output_tokens[0].tolist()[0], skip_special_tokens=True)
+>>> # from text
+>>> output_tokens = model.generate(**text_inputs, tgt_lang="fra", generate_speech=False)
+>>> translated_text_from_text = processor.decode(output_tokens[0].tolist()[0], skip_special_tokens=True)
 ```