license: apache-2.0
language:
- fr
library_name: transformers
tags:
- NMT
- commonvoice
- pytorch
- pictograms
- translation
metrics:
- sacrebleu
inference: false
t2p-nmt-commonvoice
t2p-nmt-commonvoice is a text-to-pictograms translation model built by training from scratch the NMT model on a dataset of pairs of transcriptions / pictogram token sequence (each token is linked to a pictogram image from ARASAAC). The model is used only for inference.
Training details
The model was trained with Fairseq.
Datasets
The Propicto-commonvoice dataset is used, which was created from the CommmonVoice v.15.0 corpus. This dataset was built with the method presented in the research paper titled "A Multimodal French Corpus of Aligned Speech, Text, and Pictogram Sequences for Speech-to-Pictogram Machine Translation" at LREC-Coling 2024. The dataset was split into training, validation, and test sets.
| Split | Number of utterances |
|---|---|
| train | 527,390 |
| valid | 16,124 |
| test | 16,120 |
Parameters
This is the arguments in the training pipeline :
fairseq-train \
data-bin/commonvoice.tokenized.fr-frp \
--arch transformer_iwslt_de_en --share-decoder-input-output-embed \
--optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \
--lr 5e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000 \
--dropout 0.3 --weight-decay 0.0001 \
--save-dir exp_commonvoice/checkpoints/nmt_fr_frp_commonvoice \
--criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
--max-tokens 4096 \
--eval-bleu \
--eval-bleu-args '{"beam": 5, "max_len_a": 1.2, "max_len_b": 10}' \
--eval-bleu-detok moses \
--eval-bleu-remove-bpe \
--eval-bleu-print-samples \
--best-checkpoint-metric bleu --maximize-best-checkpoint-metric \
--max-epoch 40 \
--keep-best-checkpoints 5 \
--keep-last-epochs 5
Evaluation
The model was evaluated with sacreBLEU, where we compared the reference pictogram translation with the model hypothesis.
fairseq-generate exp_commonvoice/data-bin/commonvoice.tokenized.fr-frp \
--path exp_commonvoice/checkpoints/nmt_fr_frp_commonvoice/checkpoint.best_bleu_86.0600.pt \
--batch-size 128 --beam 5 --remove-bpe > gen_cv.out
The output file prints the following information :
S-2724 la planète terre
T-2724 le planète_terre
H-2724 -0.08702446520328522 le planète_terre
D-2724 -0.08702446520328522 le planète_terre
P-2724 -0.1058 -0.0340 -0.1213
Generate test with beam=5: BLEU4 = 82.60, 92.5/85.5/79.5/74.1 (BP=1.000, ratio=1.027, syslen=138507, reflen=134811)
Results
Comparison to other translation models :
| Model | validation | test |
|---|---|---|
| t2p-t5-large-commonvoice | 86.3 | 86.5 |
| t2p-nmt-commonvoice | 86.0 | 82.6 |
| t2p-mbart-large-cc25-commonvoice | 72.3 | 72.3 |
| t2p-nllb-200-distilled-600M-commonvoice | 87.4 | 87.6 |
Environmental Impact
Training was performed using a single Nvidia V100 GPU with 32 GB of memory which took around 2 hours in total.
Using t2p-nmt-commonvoice model
The scripts to use the t2p-nmt-commonvoice model are located in the speech-to-pictograms GitHub repository.
Information
- Language(s): French
- License: Apache-2.0
- Developed by: Cécile Macaire
- Funded by
- GENCI-IDRIS (Grant 2023-AD011013625R1)
- PROPICTO ANR-20-CE93-0005
- Authors
- Cécile Macaire
- Chloé Dion
- Emmanuelle Esperança-Rodier
- Benjamin Lecouteux
- Didier Schwab
Citation
If you use this model for your own research work, please cite as follows:
@inproceedings{macaire_jeptaln2024,
title = {{Approches cascade et de bout-en-bout pour la traduction automatique de la parole en pictogrammes}},
author = {Macaire, C{\'e}cile and Dion, Chlo{\'e} and Schwab, Didier and Lecouteux, Benjamin and Esperan{\c c}a-Rodier, Emmanuelle},
url = {https://inria.hal.science/hal-04623007},
booktitle = {{35{\`e}mes Journ{\'e}es d'{\'E}tudes sur la Parole (JEP 2024) 31{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN 2024) 26{\`e}me Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RECITAL 2024)}},
address = {Toulouse, France},
publisher = {{ATALA \& AFPC}},
volume = {1 : articles longs et prises de position},
pages = {22-35},
year = {2024}
}