seamless-m4t-v2-large

SeamlessM4T v2

SeamlessM4T is our foundational all-in-one Massively Multilingual and Multimodal Machine Translation model delivering high-quality translation for speech and text in nearly 100 languages.

SeamlessM4T models support the tasks of:

• Speech-to-speech translation (S2ST)
• Speech-to-text translation (S2TT)
• Text-to-speech translation (T2ST)
• Text-to-text translation (T2TT)
• Automatic speech recognition (ASR).

SeamlessM4T models support:

• 🎤 101 languages for speech input.
• 💬 96 Languages for text input/output.
• 🔊 35 languages for speech output.

🌟 We are releasing SeamlessM4T v2, an updated version with our novel UnitY2 architecture. This new model improves over SeamlessM4T v1 in quality as well as inference speed in speech generation tasks.

The v2 version of SeamlessM4T is a multitask adaptation of our novel UnitY2 architecture. Unity2 with its hierarchical character-to-unit upsampling and non-autoregressive text-to-unit decoding considerably improves over SeamlessM4T v1 in quality and inference speed.

SeamlessM4T v2 is also supported by 🤗 Transformers, more on it in the dedicated section below.

SeamlessM4T models

We provide the extensive evaluation results of seamlessM4T-Large and SeamlessM4T-Medium reported in the paper (as averages) in the metrics files above.

The evaluation data ids for FLEURS, CoVoST2 and CVSS-C can be found here

Evaluating SeamlessM4T models

To reproduce our results or to evaluate using the same metrics over your own test sets, please check out the Evaluation README here.

Finetuning SeamlessM4T models

Please check out the Finetuning README here.

Transformers usage

SeamlessM4T is available in the 🤗 Transformers library, requiring minimal dependencies. Steps to get started:

1. First install the 🤗 Transformers library from main and sentencepiece:

pip install git+https://github.com/huggingface/transformers.git sentencepiece

1. Run the following Python code to generate speech samples. Here the target language is Russian:

from transformers import AutoProcessor, SeamlessM4Tv2Model
import torchaudio

processor = AutoProcessor.from_pretrained("facebook/seamless-m4t-v2-large")
model = SeamlessM4Tv2Model.from_pretrained("facebook/seamless-m4t-v2-large")

# from text
text_inputs = processor(text = "Hello, my dog is cute", src_lang="eng", return_tensors="pt")
audio_array_from_text = model.generate(**text_inputs, tgt_lang="rus")[0].cpu().numpy().squeeze()

# from audio
audio, orig_freq =  torchaudio.load("https://www2.cs.uic.edu/~i101/SoundFiles/preamble10.wav")
audio =  torchaudio.functional.resample(audio, orig_freq=orig_freq, new_freq=16_000) # must be a 16 kHz waveform array
audio_inputs = processor(audios=audio, return_tensors="pt")
audio_array_from_audio = model.generate(**audio_inputs, tgt_lang="rus")[0].cpu().numpy().squeeze()

1. Listen to the audio samples either in an ipynb notebook:

from IPython.display import Audio

sample_rate = model.config.sampling_rate
Audio(audio_array_from_text, rate=sample_rate)
# Audio(audio_array_from_audio, rate=sample_rate)

Or save them as a .wav file using a third-party library, e.g. scipy:

import scipy

sample_rate = model.config.sampling_rate
scipy.io.wavfile.write("out_from_text.wav", rate=sample_rate, data=audio_array_from_text)
# scipy.io.wavfile.write("out_from_audio.wav", rate=sample_rate, data=audio_array_from_audio)

For more details on using the SeamlessM4T model for inference using the 🤗 Transformers library, refer to the SeamlessM4T v2 docs or to this hands-on Google Colab.

Supported Languages:

Listed below, are the languages supported by SeamlessM4T-large (v1/v2). The source column specifies whether a language is supported as source speech (Sp) and/or source text (Tx). The target column specifies whether a language is supported as target speech (Sp) and/or target text (Tx).

Note that seamlessM4T-medium supports 200 languages in the text modality, and is based on NLLB-200 (see full list in asset card)

Citation

For SeamlessM4T v2, please cite :

@inproceedings{seamless2023,
   title="Seamless: Multilingual Expressive and Streaming Speech Translation",
   author="{Seamless Communication}, Lo{\"i}c Barrault, Yu-An Chung, Mariano Coria Meglioli, David Dale, Ning Dong, Mark Duppenthaler, Paul-Ambroise Duquenne, Brian Ellis, Hady Elsahar, Justin Haaheim, John Hoffman, Min-Jae Hwang, Hirofumi Inaguma, Christopher Klaiber, Ilia Kulikov, Pengwei Li, Daniel Licht, Jean Maillard, Ruslan Mavlyutov, Alice Rakotoarison, Kaushik Ram Sadagopan, Abinesh Ramakrishnan, Tuan Tran, Guillaume Wenzek, Yilin Yang, Ethan Ye, Ivan Evtimov, Pierre Fernandez, Cynthia Gao, Prangthip Hansanti, Elahe Kalbassi, Amanda Kallet, Artyom Kozhevnikov, Gabriel Mejia, Robin San Roman, Christophe Touret, Corinne Wong, Carleigh Wood, Bokai Yu, Pierre Andrews, Can Balioglu, Peng-Jen Chen, Marta R. Costa-juss{\`a}, Maha Elbayad, Hongyu Gong, Francisco Guzm{\'a}n, Kevin Heffernan, Somya Jain, Justine Kao, Ann Lee, Xutai Ma, Alex Mourachko, Benjamin Peloquin, Juan Pino, Sravya Popuri, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Anna Sun, Paden Tomasello, Changhan Wang, Jeff Wang, Skyler Wang, Mary Williamson",
  journal={ArXiv},
  year={2023}
}