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seamless-m4t-v2-large

Automatic Speech Recognition
Transformers
Safetensors
seamless_m4t_v2
feature-extraction
audio-to-audio
text-to-speech
seamless_communication
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metadata
license: cc-by-nc-4.0
language:
  - af
  - am
  - ar
  - as
  - az
  - be
  - bn
  - bs
  - bg
  - ca
  - cs
  - zh
  - cy
  - da
  - de
  - el
  - en
  - et
  - fi
  - fr
  - or
  - om
  - ga
  - gl
  - gu
  - ha
  - he
  - hi
  - hr
  - hu
  - hy
  - ig
  - id
  - is
  - it
  - jv
  - ja
  - kn
  - ka
  - kk
  - mn
  - km
  - ky
  - ko
  - lo
  - ln
  - lt
  - lb
  - lg
  - lv
  - ml
  - mr
  - mk
  - mt
  - mi
  - my
  - nl
  - nb
  - ne
  - ny
  - oc
  - pa
  - ps
  - fa
  - pl
  - pt
  - ro
  - ru
  - sk
  - sl
  - sn
  - sd
  - so
  - es
  - sr
  - sv
  - sw
  - ta
  - te
  - tg
  - tl
  - th
  - tr
  - uk
  - ur
  - uz
  - vi
  - wo
  - xh
  - yo
  - ms
  - zu
  - ary
  - arz
  - yue
  - kea
metrics:
  - bleu
  - wer
  - chrf
inference: false
pipeline_tag: automatic-speech-recognition
tags:
  - audio-to-audio
  - text-to-speech
  - seamless_communication
library_name: transformers
widget:
  - src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
    example_title: Librispeech sample 1
    output:
      text: >-
        going along slushy country roads and speaking to damp audiences in
        draughty schoolrooms day after day for a fortnight he'll have to put in
        an appearance at some place of worship on sunday morning and he can come
        to us immediately afterwards
  - src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
    example_title: Librispeech sample 2
    output:
      text: >-
        before he had time to answer a much-encumbered vera burst into the room
        with the question i say can i leave these here these were a small black
        pig and a lusty specimen of black-red game-cock

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 architectures

SeamlessM4T models

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

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).

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 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}
}