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facebook/
seamless-m4t-unity-small

Name: seamless-m4t-unity-small
Availability: InStock

fairseq2

SeamlessM4T

cc-by-nc-4.0

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SeamlessM4T - On-Device

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

SeamlessM4T covers:

📥 101 languages for speech input
⌨️ 96 Languages for text input/output
🗣️ 35 languages for speech output.

Apart from SeamlessM4T-LARGE (2.3B) and SeamlessM4T-MEDIUM (1.2B) models, we are also developing a small model (281M) targeting for on-device inference.

This README contains an example to run an exported small model covering most tasks (ASR/S2TT/S2ST). The model could be executed on popular mobile devices with Pytorch Mobile (https://pytorch.org/mobile/home/).

Overview

Model	Checkpoint	Num Params	Disk Size	Supported Tasks	Supported Languages
UnitY-Small	🤗 Model card - checkpoint	281M	862MB	S2ST, S2TT, ASR	eng, fra, hin, por, spa
UnitY-Small-S2T	🤗 Model card - checkpoint	235M	637MB	S2TT, ASR	eng, fra,hin, por, spa

UnitY-Small-S2T is a pruned version of UnitY-Small without 2nd pass unit decoding.

Inference

To use exported model, users don't need seamless_communication or fairseq2 dependency.

python

import torchaudio
import torch

audio_input, _ = torchaudio.load(TEST_AUDIO_PATH) # Load waveform using torchaudio

s2st_model = torch.jit.load("unity_on_device.ptl")

with torch.no_grad():
    text, units, waveform = s2st_model(audio_input, tgt_lang=TGT_LANG) # S2ST model also returns waveform

print(text)
torchaudio.save(f"{OUTPUT_FOLDER}/result.wav", waveform.unsqueeze(0), sample_rate=16000) # Save output waveform to local file

Also running the exported model doesn't need python runtime. For example, you could load this model in C++ following this tutorial, or building your own on-device applications similar to this example

Citation

If you use SeamlessM4T in your work or any models/datasets/artifacts published in SeamlessM4T, please cite:

bibtex

@article{seamlessm4t2023,
  title={SeamlessM4T—Massively Multilingual \& Multimodal Machine Translation},
  author={{Seamless Communication}, Lo\"{i}c Barrault, Yu-An Chung, Mariano Cora Meglioli, David Dale, Ning Dong, Paul-Ambroise Duquenne, Hady Elsahar, Hongyu Gong, Kevin Heffernan, John Hoffman, Christopher Klaiber, Pengwei Li, Daniel Licht, Jean Maillard, Alice Rakotoarison, Kaushik Ram Sadagopan, Guillaume Wenzek, Ethan Ye,  Bapi Akula, Peng-Jen Chen, Naji El Hachem, Brian Ellis, Gabriel Mejia Gonzalez, Justin Haaheim, Prangthip Hansanti, Russ Howes, Bernie Huang, Min-Jae Hwang, Hirofumi Inaguma, Somya Jain, Elahe Kalbassi, Amanda Kallet, Ilia Kulikov, Janice Lam, Daniel Li, Xutai Ma, Ruslan Mavlyutov, Benjamin Peloquin, Mohamed Ramadan, Abinesh Ramakrishnan, Anna Sun, Kevin Tran, Tuan Tran, Igor Tufanov, Vish Vogeti, Carleigh Wood, Yilin Yang, Bokai Yu, Pierre Andrews, Can Balioglu, Marta R. Costa-juss\`{a} \footnotemark[3], Onur \,{C}elebi,Maha Elbayad,Cynthia Gao, Francisco Guzm\'an, Justine Kao, Ann Lee, Alexandre Mourachko, Juan Pino, Sravya Popuri, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Paden Tomasello, Changhan Wang, Jeff Wang, Skyler Wang},
  journal={ArXiv},
  year={2023}
}

License

seamless_communication is CC-BY-NC 4.0 licensed

facebook/seamless-m4t-unity-small