SONAR

[Paper] [Demo]

We introduce SONAR, a new multilingual and multimodal fixed-size sentence embedding space, with a full suite of speech and text encoders and decoders. It substantially outperforms existing sentence embeddings such as LASER3 and LabSE on the xsim and xsim++ multilingual similarity search tasks.

Speech segments can be embedded in the same SONAR embedding space using language-specific speech encoders trained in a teacher-student setting on speech transcription data. We also provide a single text decoder, which allows us to perform text-to-text and speech-to-text machine translation, including for zero-shot language and modality combinations.

SONAR stands for Sentence-level multimOdal and laNguage-Agnostic Representations

The full list of supported languages (along with download links) can be found here below.

SONAR Architecture:

Text results

Speech results

Installing

You can install SONAR with pip install sonar-space. Note that there is another sonar package on pip that IS NOT this project, make sure to use sonar-space in your dependencies.

If you want to install SONAR manually, you can install it localy. SONAR depends mainly on Fairseq2 and can be installed using (tested with python=3.8)

pip install --upgrade pip
pip install -e .

If fairseq2 does not provide a build for your machine, check the readme of that project to build it locally.

Usage

fairseq2 will automatically download models into your $TORCH_HOME/hub directory upon using the commands below.

Compute text sentence embeddings with SONAR:

from sonar.inference_pipelines.text import TextToEmbeddingModelPipeline
t2vec_model = TextToEmbeddingModelPipeline(encoder="text_sonar_basic_encoder",
                                           tokenizer="text_sonar_basic_encoder")
sentences = ['My name is SONAR.', 'I can embed the sentences into vectorial space.']
embeddings = t2vec_model.predict(sentences, source_lang="eng_Latn")
print(embeddings.shape)
# torch.Size([2, 1024])

Reconstruct text from SONAR embeddings

from sonar.inference_pipelines.text import EmbeddingToTextModelPipeline
vec2text_model = EmbeddingToTextModelPipeline(decoder="text_sonar_basic_decoder",
                                              tokenizer="text_sonar_basic_encoder")
reconstructed = vec2text_model.predict(embeddings, target_lang="eng_Latn", max_seq_len=512)
# max_seq_len is a keyword argument passed to the fairseq2 BeamSearchSeq2SeqGenerator.
print(reconstructed)
# ['My name is SONAR.', 'I can embed the sentences into vector space.']

Translate text with SONAR

from sonar.inference_pipelines.text import TextToTextModelPipeline
t2t_model = TextToTextModelPipeline(encoder="text_sonar_basic_encoder",
                                    decoder="text_sonar_basic_decoder",
                                    tokenizer="text_sonar_basic_encoder")  # tokenizer is attached to both encoder and decoder cards

sentences = ['My name is SONAR.', 'I can embed the sentences into vectorial space.']
t2t_model.predict(sentences, source_lang="eng_Latn", target_lang="fra_Latn")
# ['Mon nom est SONAR.', "Je peux intégrer les phrases dans l'espace vectoriel."]

Compute speech sentence embeddings with SONAR

from sonar.inference_pipelines.speech import SpeechToEmbeddingModelPipeline
s2vec_model = SpeechToEmbeddingModelPipeline(encoder="sonar_speech_encoder_eng")

s2vec_model.predict(["./tests/integration_tests/data/audio_files/audio_1.wav",
                     "./tests/integration_tests/data/audio_files/audio_2.wav"]).shape
# torch.Size([2, 1024])
import torchaudio
inp, sr = torchaudio.load("./tests/integration_tests/data/audio_files/audio_1.wav")
assert sr == 16000, "Sample rate should be 16kHz"

s2vec_model.predict([inp]).shape
# torch.Size([1, 1024])

Speech-to-text translation with SONAR

from sonar.inference_pipelines.speech import SpeechToTextModelPipeline

s2t_model = SpeechToTextModelPipeline(encoder="sonar_speech_encoder_eng",
                                      decoder="text_sonar_basic_decoder",
                                      tokenizer="text_sonar_basic_decoder")

import torchaudio
inp, sr = torchaudio.load("./tests/integration_tests/data/audio_files/audio_1.wav")
assert sr == 16000, "Sample rate should be 16kHz"

# passing loaded audio files
s2t_model.predict([inp], target_lang="eng_Latn")
# ['Television reports show white smoke coming from the plant.']

# passing multiple wav files 
s2t_model.predict(["./tests/integration_tests/data/audio_files/audio_1.wav",
                   "./tests/integration_tests/data/audio_files/audio_2.wav"], target_lang="eng_Latn")
# ['Television reports show white smoke coming from the plant.',
# 'These couples may choose to make an adoption plan for their baby.']

Predicting sentence similarity with BLASER 2.0 models

BLASER 2.0 is a family of models for automatic evaluation of machine translation quality based on SONAR embeddings. They predict cross-lingual semantic similarity between the translation and the source (optionally, also using a reference translation).

from sonar.inference_pipelines.text import TextToEmbeddingModelPipeline
from sonar.models.blaser.loader import load_blaser_model

blaser_ref = load_blaser_model("blaser_2_0_ref").eval()
blaser_qe = load_blaser_model("blaser_2_0_qe").eval()
text_embedder = TextToEmbeddingModelPipeline(encoder="text_sonar_basic_encoder", tokenizer="text_sonar_basic_encoder")

src_embs = text_embedder.predict(["Le chat s'assit sur le tapis."], source_lang="fra_Latn")
ref_embs = text_embedder.predict(["The cat sat on the mat."], source_lang="eng_Latn")
mt_embs = text_embedder.predict(["The cat sat down on the carpet."], source_lang="eng_Latn")

print(blaser_ref(src=src_embs, ref=ref_embs, mt=mt_embs).item())  # 4.688
print(blaser_qe(src=src_embs, mt=mt_embs).item())  # 4.708

Detailed model cards with more examples: facebook/blaser-2.0-ref, facebook/blaser-2.0-qe.

Demo notebooks

See more complete demo notebooks :

• sonar text2text similarity and translation
• sonar speech2text and other data pipeline examples
• sonar bilingual document alignment with sonar text similarity

Supported languages and download links

The SONAR text encoder & decoder supports 200 languages. SONAR speech encoders support 37 languages.

Citation Information

Please cite the paper when referencing the SONAR embedding space, encoders and decoders as:

@misc{Duquenne:2023:sonar_arxiv,
  author = {Paul-Ambroise Duquenne and Holger Schwenk and Benoit Sagot},
  title = {{SONAR:} Sentence-Level Multimodal and Language-Agnostic Representations},
  publisher = {arXiv},
  year = {2023},
  url = {https://arxiv.org/abs/2308.11466},
}

Contributing

See the CONTRIBUTING file for how to help out.

License

SONAR code is released under the MIT license (see CODE_LICENSE).

Some of SONAR models are released with the same MIT license, BUT BEWARE, some of them are released under a non commercial license (see NC_MODEL_LICENSE). Please refer to LICENSE for the details.