Update README.md

README.md

@@ -32,6 +32,58 @@ This is the "large" variant of the unified model, which enables multiple tasks w
 
 We provide extensive evaluation results of SeamlessM4T-Medium and SeamlessM4T-Large in the SeamlessM4T paper (as averages) in the `metrics` files above.
 
+## 🤗 Transformers Usage
+
+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")
+```
+
+You can seamlessly use this model on text or on audio, to generated either translated text or translated audio.
+
+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")
+```
+
+
+### Speech
+
+[`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()
+```
+
+With basically the same code, I've translated English text and Arabic speech to Russian speech samples.
+
+### Text
+
+Similarly, you can generate translated text from audio files or from text with the same model. You only have to pass `generate_speech=False` to [`SeamlessM4TModel.generate`](https://huggingface.co/docs/transformers/main/en/model_doc/seamless_m4t#transformers.SeamlessM4TModel.generate).
+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)
+```
+
+
 ## Instructions to run inference with SeamlessM4T models
 
 The SeamlessM4T models are currently available through the `seamless_communication` package. The `seamless_communication`