README.md

---
inference: false
tags:
- SeamlessM4T
- seamless_m4t
license: cc-by-nc-4.0
library_name: transformers
---

# SeamlessM4T Large

SeamlessM4T is a collection of models designed to provide high quality translation, allowing people from different 
linguistic communities to communicate effortlessly through speech and text. 

SeamlessM4T Large covers:
- 📥 101 languages for speech input
- ⌨️ [96 Languages](https://huggingface.co/ylacombe/hf-seamless-m4t-large/blob/main/generation_config.json#L48-L145) for text input/output
- 🗣️ [35 languages](https://huggingface.co/ylacombe/hf-seamless-m4t-large/blob/main/generation_config.json#L149-L184) for speech output. 

This is the "large" variant of the unified model, which enables multiple tasks without relying on multiple separate models:
- Speech-to-speech translation (S2ST)
- Speech-to-text translation (S2TT)
- Text-to-speech translation (T2ST)
- Text-to-text translation (T2TT)
- Automatic speech recognition (ASR)

You can perform all the above tasks from one single model - `SeamlessM4TModel`, but each task also has its own dedicated sub-model.



## Usage

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")
```

You can seamlessly use this model on text or on audio, to generated either translated text or translated audio.

### Speech

You can easily generate translated speech with [`SeamlessM4TModel.generate`]. Here is an example showing how to generate speech from English to Russian.

```python
>>> inputs = processor(text = "Hello, my dog is cute", src_lang="eng", return_tensors="pt")

>>> audio_array = model.generate(**inputs, tgt_lang="rus")
>>> audio_array = audio_array[0].cpu().numpy().squeeze()
```

You can also translate directly from a speech waveform. Here is an example from Arabic to English:

```python
>>> from datasets import load_dataset

>>> dataset = load_dataset("arabic_speech_corpus", split="test[0:1]")

>>> audio_sample = dataset["audio"][0]["array"]
 
>>> inputs = processor(audios = audio_sample, return_tensors="pt")

>>> audio_array = model.generate(**inputs, tgt_lang="rus")
>>> audio_array = audio_array[0].cpu().numpy().squeeze()
```

#### Tips

[`SeamlessM4TModel`] is transformers top level model to generate speech and text, but you can also use dedicated models that perform the task without additional components, thus reducing the memory footprint.
For example, you can replace the previous snippet with the model dedicated to the S2ST task:

```python
>>> from transformers import SeamlessM4TForSpeechToSpeech
>>> model = SeamlessM4TForSpeechToSpeech.from_pretrained("ylacombe/hf-seamless-m4t-medium")
```


### Text

Similarly, you can generate translated text from text or audio files, this time using the dedicated models.

```python
>>> from transformers import SeamlessM4TForSpeechToText
>>> model = SeamlessM4TForSpeechToText.from_pretrained("ylacombe/hf-seamless-m4t-medium")
>>> audio_sample = dataset["audio"][0]["array"]
 
>>> inputs = processor(audios = audio_sample, return_tensors="pt")

>>> output_tokens = model.generate(**inputs, tgt_lang="fra")
>>> translated_text = processor.decode(output_tokens.tolist()[0], skip_special_tokens=True)
```

And from text:

```python
>>> from transformers import SeamlessM4TForTextToText
>>> model = SeamlessM4TForTextToText.from_pretrained("ylacombe/hf-seamless-m4t-medium")
>>> inputs = processor(text = "Hello, my dog is cute", src_lang="eng", return_tensors="pt")

>>> output_tokens = model.generate(**inputs, tgt_lang="fra")
>>> translated_text = processor.decode(output_tokens.tolist()[0], skip_special_tokens=True)
```

#### Tips

Three last tips:

1. [`SeamlessM4TModel`] can generate text and/or speech. Pass `generate_speech=False` to [`SeamlessM4TModel.generate`] to only generate text. You also have the possibility to pass `return_intermediate_token_ids=True`, to get both text token ids and the generated speech.
2. You have the possibility to change the speaker used for speech synthesis with the `spkr_id` argument.
3. You can use different [generation strategies](./generation_strategies) for speech and text generation, e.g `.generate(input_ids=input_ids, text_num_beams=4, speech_do_sample=True)` which will successively perform beam-search decoding on the text model, and multinomial sampling on the speech model.