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SeamlessM4T

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:

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

This unified model 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).

Note

SeamlessM4T v2 and v1 are also supported in the 🤗 Transformers library, more on it in the dedicated section below.

SeamlessM4T v1

The v1 version of SeamlessM4T is a multitask adaptation of the UnitY architecture (Inaguma et al., 2023). UnitY is a two-pass direct S2ST architecture which first generates textual representations and subsequently predicts discrete acoustic units.

SeamlessM4T v2

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 architectures

SeamlessM4T models

Model Name #params checkpoint metrics
SeamlessM4T-Large v2 2.3B 🤗 Model card - checkpoint metrics
SeamlessM4T-Large (v1) 2.3B 🤗 Model card - checkpoint metrics
SeamlessM4T-Medium (v1) 1.2B 🤗 Model card - checkpoint metrics

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

Using SeamlessM4T models

m4t_predict with CLI:

Inference is run with the CLI, from the root directory of the repository.

The model can be specified with --model_name seamlessM4T_v2_large, seamlessM4T_large or seamlessM4T_medium:

# S2ST:
m4t_predict <path_to_input_audio> --task s2st --tgt_lang <tgt_lang> --output_path <path_to_save_audio> --model_name seamlessM4T_v2_large

# S2T:
m4t_predict <path_to_input_audio> --task s2tt --tgt_lang <tgt_lang> --model_name seamlessM4T_v2_large

# T2TT:
m4t_predict <input_text> --task t2tt --tgt_lang <tgt_lang> --src_lang <src_lang> --model_name seamlessM4T_v2_large

# T2ST:
m4t_predict <input_text> --task t2st --tgt_lang <tgt_lang> --src_lang <src_lang> --output_path <path_to_save_audio> --model_name seamlessM4T_v2_large

# ASR:
m4t_predict <path_to_input_audio> --task asr --tgt_lang <tgt_lang> --model_name seamlessM4T_v2_large

Inference with Translator:

Inference calls for the Translator object instantiated with a multitask UnitY or UnitY2 model with the options:

and a vocoder:

  • vocoder_v2 for seamlessM4T_v2_large.
  • vocoder_36langs for seamlessM4T_large or seamlessM4T_medium.
import torch
from seamless_communication.inference import Translator


# Initialize a Translator object with a multitask model, vocoder on the GPU.
translator = Translator("seamlessM4T_large", "vocoder_36langs", torch.device("cuda:0"), torch.float16)

Now predict() can be used to run inference as many times on any of the supported tasks.

Given an input audio with <path_to_input_audio> or an input text <input_text> in <src_lang>, we first set the text_generation_opts, unit_generation_opts and then translate into <tgt_lang> as follows:

S2ST and T2ST (speech output):

# S2ST
text_output, speech_output = translator.predict(
    input=<path_to_input_audio>,
    task_str="S2ST",
    tgt_lang=<tgt_lang>,
    text_generation_opts=text_generation_opts,
    unit_generation_opts=unit_generation_opts
)

# T2ST
text_output, speech_output = translator.predict(
    input=<input_text>,
    task_str="T2ST",
    tgt_lang=<tgt_lang>,
    src_lang=<src_lang>,
    text_generation_opts=text_generation_opts,
    unit_generation_opts=unit_generation_opts
)

Note that <src_lang> must be specified for T2ST.

The generated units are synthesized and the output audio file is saved with:

# Save the translated audio output:
import torchaudio
torchaudio.save(
    <path_to_save_audio>,
    speech_output.audio_wavs[0][0].cpu(),
    sample_rate=speech_output.sample_rate,
)

S2TT, T2TT and ASR (text output):

# S2TT
text_output, _ = translator.predict(
    input=<path_to_input_audio>,
    task_str="S2TT",
    tgt_lang=<tgt_lang>,
    text_generation_opts=text_generation_opts,
    unit_generation_opts=None
)

# ASR
# This is equivalent to S2TT with `<tgt_lang>=<src_lang>`.
    text_output, _ = translator.predict(
    input=<path_to_input_audio>,
    task_str="ASR",
    tgt_lang=<src_lang>,
    text_generation_opts=text_generation_opts,
    unit_generation_opts=None
)

# T2TT
text_output, _ = translator.predict(
    input=<input_text>,
    task_str="T2TT",
    tgt_lang=<tgt_lang>,
    src_lang=<src_lang>,
    text_generation_opts=text_generation_opts,
    unit_generation_opts=None
)

Note that <src_lang> must be specified for T2TT

To reproduce the seamless papers results (v1 or v2), or to evaluate using the same metrics over your own test sets, please check out the Evaluation README here.

Inference with 🤗 Transformers

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:
import torchaudio
from transformers import AutoProcessor, SeamlessM4Tv2Model

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().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().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. torchaudio:

torchaudio.save(
    <path_to_save_audio>,
    audio_array_from_audio,  # or audio_array_from_text
    sample_rate=model.config.sampling_rate,
)
  1. (bis) To run inference for text generating tasks (T2TT, ASR or S2TT), it is recommended to use dedicated models. With that, only the required sub-modules will be loaded. For exmaple, text-to-text translation from English to Bulgarian, is performed as follows:
from transformers import AutoProcessor, SeamlessM4Tv2ForTextToText
processor = AutoProcessor.from_pretrained("facebook/seamless-m4t-v2-large")
model = SeamlessM4Tv2ForTextToText.from_pretrained("facebook/seamless-m4t-v2-large")

src_lang, tgt_lang = "eng", "bul"
text_inputs = processor(text='Hello, my dog is cute', src_lang=src_lang, return_tensors="pt")
decoder_input_ids = model.generate(**text_inputs, tgt_lang=tgt_lang)[0].tolist()
translated_text = processor.decode(decoder_input_ids, skip_special_tokens=True)
print(f"{tgt_lang}: {translated_text}")

Note

For more details on using the SeamlessM4T model for inference using the 🤗 Transformers library, refer to the SeamlessM4T v2 docs, the SeamlessM4T v1 docs or to this hands-on Google Colab.

Finetuning SeamlessM4T models

Please check out the Finetuning README here.

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).

code language script Source Target
afr Afrikaans Latn Sp, Tx Tx
amh Amharic Ethi Sp, Tx Tx
arb Modern Standard Arabic Arab Sp, Tx Sp, Tx
ary Moroccan Arabic Arab Sp, Tx Tx
arz Egyptian Arabic Arab Sp, Tx Tx
asm Assamese Beng Sp, Tx Tx
ast Asturian Latn Sp --
azj North Azerbaijani Latn Sp, Tx Tx
bel Belarusian Cyrl Sp, Tx Tx
ben Bengali Beng Sp, Tx Sp, Tx
bos Bosnian Latn Sp, Tx Tx
bul Bulgarian Cyrl Sp, Tx Tx
cat Catalan Latn Sp, Tx Sp, Tx
ceb Cebuano Latn Sp, Tx Tx
ces Czech Latn Sp, Tx Sp, Tx
ckb Central Kurdish Arab Sp, Tx Tx
cmn Mandarin Chinese Hans Sp, Tx Sp, Tx
cmn_Hant Mandarin Chinese Hant Sp, Tx Sp, Tx
cym Welsh Latn Sp, Tx Sp, Tx
dan Danish Latn Sp, Tx Sp, Tx
deu German Latn Sp, Tx Sp, Tx
ell Greek Grek Sp, Tx Tx
eng English Latn Sp, Tx Sp, Tx
est Estonian Latn Sp, Tx Sp, Tx
eus Basque Latn Sp, Tx Tx
fin Finnish Latn Sp, Tx Sp, Tx
fra French Latn Sp, Tx Sp, Tx
fuv Nigerian Fulfulde Latn Sp, Tx Tx
gaz West Central Oromo Latn Sp, Tx Tx
gle Irish Latn Sp, Tx Tx
glg Galician Latn Sp, Tx Tx
guj Gujarati Gujr Sp, Tx Tx
heb Hebrew Hebr Sp, Tx Tx
hin Hindi Deva Sp, Tx Sp, Tx
hrv Croatian Latn Sp, Tx Tx
hun Hungarian Latn Sp, Tx Tx
hye Armenian Armn Sp, Tx Tx
ibo Igbo Latn Sp, Tx Tx
ind Indonesian Latn Sp, Tx Sp, Tx
isl Icelandic Latn Sp, Tx Tx
ita Italian Latn Sp, Tx Sp, Tx
jav Javanese Latn Sp, Tx Tx
jpn Japanese Jpan Sp, Tx Sp, Tx
kam Kamba Latn Sp --
kan Kannada Knda Sp, Tx Tx
kat Georgian Geor Sp, Tx Tx
kaz Kazakh Cyrl Sp, Tx Tx
kea Kabuverdianu Latn Sp --
khk Halh Mongolian Cyrl Sp, Tx Tx
khm Khmer Khmr Sp, Tx Tx
kir Kyrgyz Cyrl Sp, Tx Tx
kor Korean Kore Sp, Tx Sp, Tx
lao Lao Laoo Sp, Tx Tx
lit Lithuanian Latn Sp, Tx Tx
ltz Luxembourgish Latn Sp --
lug Ganda Latn Sp, Tx Tx
luo Luo Latn Sp, Tx Tx
lvs Standard Latvian Latn Sp, Tx Tx
mai Maithili Deva Sp, Tx Tx
mal Malayalam Mlym Sp, Tx Tx
mar Marathi Deva Sp, Tx Tx
mkd Macedonian Cyrl Sp, Tx Tx
mlt Maltese Latn Sp, Tx Sp, Tx
mni Meitei Beng Sp, Tx Tx
mya Burmese Mymr Sp, Tx Tx
nld Dutch Latn Sp, Tx Sp, Tx
nno Norwegian Nynorsk Latn Sp, Tx Tx
nob Norwegian Bokmål Latn Sp, Tx Tx
npi Nepali Deva Sp, Tx Tx
nya Nyanja Latn Sp, Tx Tx
oci Occitan Latn Sp --
ory Odia Orya Sp, Tx Tx
pan Punjabi Guru Sp, Tx Tx
pbt Southern Pashto Arab Sp, Tx Tx
pes Western Persian Arab Sp, Tx Sp, Tx
pol Polish Latn Sp, Tx Sp, Tx
por Portuguese Latn Sp, Tx Sp, Tx
ron Romanian Latn Sp, Tx Sp, Tx
rus Russian Cyrl Sp, Tx Sp, Tx
slk Slovak Latn Sp, Tx Sp, Tx
slv Slovenian Latn Sp, Tx Tx
sna Shona Latn Sp, Tx Tx
snd Sindhi Arab Sp, Tx Tx
som Somali Latn Sp, Tx Tx
spa Spanish Latn Sp, Tx Sp, Tx
srp Serbian Cyrl Sp, Tx Tx
swe Swedish Latn Sp, Tx Sp, Tx
swh Swahili Latn Sp, Tx Sp, Tx
tam Tamil Taml Sp, Tx Tx
tel Telugu Telu Sp, Tx Sp, Tx
tgk Tajik Cyrl Sp, Tx Tx
tgl Tagalog Latn Sp, Tx Sp, Tx
tha Thai Thai Sp, Tx Sp, Tx
tur Turkish Latn Sp, Tx Sp, Tx
ukr Ukrainian Cyrl Sp, Tx Sp, Tx
urd Urdu Arab Sp, Tx Sp, Tx
uzn Northern Uzbek Latn Sp, Tx Sp, Tx
vie Vietnamese Latn Sp, Tx Sp, Tx
xho Xhosa Latn Sp --
yor Yoruba Latn Sp, Tx Tx
yue Cantonese Hant Sp, Tx Tx
zlm Colloquial Malay Latn Sp --
zsm Standard Malay Latn Tx Tx
zul Zulu Latn Sp, Tx 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 UnitY, please cite :

@inproceedings{inaguma-etal-2023-unity,
    title="{U}nit{Y}: Two-pass Direct Speech-to-speech Translation with Discrete Units",
    author="Inaguma, Hirofumi  and Popuri, Sravya  and Kulikov, Ilia  and Chen, Peng-Jen  and Wang, Changhan  and Chung, Yu-An  and Tang, Yun  and Lee, Ann  and Watanabe, Shinji  and Pino, Juan",
    booktitle="Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    year="2023",
    url="https://aclanthology.org/2023.acl-long.872",
}

For SeamlessM4T v1, please cite :

@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}
}

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}
}