---
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](https://github.com/facebookresearch/fairseq/blob/main/examples/translation/README.md) model on a dataset of pairs of transcriptions / pictogram token sequence (each token is linked to a pictogram image from [ARASAAC](https://arasaac.org/)).
The model is used only for **inference**. 

## Training details

The model was trained with [Fairseq](https://github.com/facebookresearch/fairseq/blob/main/examples/translation/README.md).

### Datasets

The [Propicto-commonvoice dataset](https://www.ortolang.fr/market/corpora/propicto) 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](https://aclanthology.org/2024.lrec-main.76/)" 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 :

```bash
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.

```bash
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 :
```txt
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](https://github.com/macairececile/speech-to-pictograms).

## 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:

```bibtex
@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}
}
```