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*This model was released on 2023-08-22 and added to Hugging Face Transformers on 2023-10-23.*

# SeamlessM4T

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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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## Overview

The SeamlessM4T model was proposed in [SeamlessM4T — Massively Multilingual & Multimodal Machine Translation](https://huggingface.co/papers/2308.11596) by the Seamless Communication team from Meta AI.

This is the **version 1** release of the model. For the updated **version 2** release, refer to the [Seamless M4T v2 docs](https://huggingface.co/docs/transformers/main/model_doc/seamless_m4t_v2).

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 enables multiple tasks without relying on 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)

[`SeamlessM4TModel`] can perform all the above tasks, but each task also has its own dedicated sub-model.

The abstract from the paper is the following:

*What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages? While recent breakthroughs in text-based models have pushed machine translation coverage beyond 200 languages, unified speech-to-speech translation models have yet to achieve similar strides. More specifically, conventional speech-to-speech translation systems rely on cascaded systems that perform translation progressively, putting high-performing unified systems out of reach. To address these gaps, we introduce SeamlessM4T, a single model that supports speech-to-speech translation, speech-to-text translation, text-to-speech translation, text-to-text translation, and automatic speech recognition for up to 100 languages. To build this, we used 1 million hours of open speech audio data to learn self-supervised speech representations with w2v-BERT 2.0. Subsequently, we created a multimodal corpus of automatically aligned speech translations. Filtered and combined with human-labeled and pseudo-labeled data, we developed the first multilingual system capable of translating from and into English for both speech and text. On FLEURS, SeamlessM4T sets a new standard for translations into multiple target languages, achieving an improvement of 20% BLEU over the previous SOTA in direct speech-to-text translation. Compared to strong cascaded models, SeamlessM4T improves the quality of into-English translation by 1.3 BLEU points in speech-to-text and by 2.6 ASR-BLEU points in speech-to-speech. Tested for robustness, our system performs better against background noises and speaker variations in speech-to-text tasks compared to the current SOTA model. Critically, we evaluated SeamlessM4T on gender bias and added toxicity to assess translation safety. Finally, all contributions in this work are open-sourced and accessible at https://github.com/facebookresearch/seamless_communication*

## 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-medium")
>>> model = SeamlessM4TModel.from_pretrained("facebook/hf-seamless-m4t-medium")
```

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("halabi2016/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`] 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`].
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)
```

### Tips

#### 1. Use dedicated models

[`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 audio-to-audio generation snippet with the model dedicated to the S2ST task, the rest is exactly the same code:

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

Or you can replace the text-to-text generation snippet with the model dedicated to the T2TT task, you only have to remove `generate_speech=False`.

```python
>>> from transformers import SeamlessM4TForTextToText
>>> model = SeamlessM4TForTextToText.from_pretrained("facebook/hf-seamless-m4t-medium")
```

Feel free to try out [`SeamlessM4TForSpeechToText`] and [`SeamlessM4TForTextToSpeech`] as well.

#### 2. Change the speaker identity

You have the possibility to change the speaker used for speech synthesis with the `spkr_id` argument. Some `spkr_id` works better than other for some languages!

#### 3. Change the generation strategy

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.

#### 4. Generate speech and text at the same time

Use `return_intermediate_token_ids=True` with [`SeamlessM4TModel`] to return both speech and text !

## Model architecture

SeamlessM4T features a versatile architecture that smoothly handles the sequential generation of text and speech. This setup comprises two sequence-to-sequence (seq2seq) models. The first model translates the input modality into translated text, while the second model generates speech tokens, known as "unit tokens," from the translated text.

Each modality has its own dedicated encoder with a unique architecture. Additionally, for speech output, a vocoder inspired by the [HiFi-GAN](https://huggingface.co/papers/2010.05646) architecture is placed on top of the second seq2seq model.

Here's how the generation process works:

- Input text or speech is processed through its specific encoder.
- A decoder creates text tokens in the desired language.
- If speech generation is required, the second seq2seq model, following a standard encoder-decoder structure, generates unit tokens.
- These unit tokens are then passed through the final vocoder to produce the actual speech.

This model was contributed by [ylacombe](https://huggingface.co/ylacombe). The original code can be found [here](https://github.com/facebookresearch/seamless_communication).

## SeamlessM4TModel

[[autodoc]] SeamlessM4TModel
    - generate

## SeamlessM4TForTextToSpeech

[[autodoc]] SeamlessM4TForTextToSpeech
    - generate

## SeamlessM4TForSpeechToSpeech

[[autodoc]] SeamlessM4TForSpeechToSpeech
    - generate

## SeamlessM4TForTextToText

[[autodoc]] transformers.SeamlessM4TForTextToText
    - forward
    - generate

## SeamlessM4TForSpeechToText

[[autodoc]] transformers.SeamlessM4TForSpeechToText
    - forward
    - generate

## SeamlessM4TConfig

[[autodoc]] SeamlessM4TConfig

## SeamlessM4TTokenizer

[[autodoc]] SeamlessM4TTokenizer
    - __call__
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary

## SeamlessM4TTokenizerFast

[[autodoc]] SeamlessM4TTokenizerFast
    - __call__

## SeamlessM4TFeatureExtractor

[[autodoc]] SeamlessM4TFeatureExtractor
    - __call__

## SeamlessM4TProcessor

[[autodoc]] SeamlessM4TProcessor
    - __call__

## SeamlessM4TCodeHifiGan

[[autodoc]] SeamlessM4TCodeHifiGan

## SeamlessM4THifiGan

[[autodoc]] SeamlessM4THifiGan

## SeamlessM4TTextToUnitModel

[[autodoc]] SeamlessM4TTextToUnitModel

## SeamlessM4TTextToUnitForConditionalGeneration

[[autodoc]] SeamlessM4TTextToUnitForConditionalGeneration
