t2p-nllb-200-distilled-600M-commonvoice

t2p-nllb-200-distilled-600M-commonvoice is a text-to-pictograms translation model built by fine-tuning the nllb-200-distilled-600M 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

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

A full list of the parameters is available in the config.json file. This is the arguments in the training pipeline :

training_args = Seq2SeqTrainingArguments(
    output_dir="checkpoints_commonvoice/",
    evaluation_strategy="epoch",
    save_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=32,
    per_device_eval_batch_size=32,
    weight_decay=0.01,
    save_total_limit=3,
    num_train_epochs=40,
    predict_with_generate=True,
    fp16=True,
    load_best_model_at_end=True
)

Evaluation

The model was evaluated with sacreBLEU, where we compared the reference pictogram translation with the model hypothesis.

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

Fine-tuning was performed using a single Nvidia V100 GPU with 32 GB of memory which took around 30 hours in total.

Using t2p-nllb-200-distilled-600M-commonvoice model with HuggingFace transformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

source_lang = "fr"
target_lang = "frp"
max_input_length = 128
max_target_length = 128

tokenizer = AutoTokenizer.from_pretrained("Propicto/t2p-nllb-200-distilled-600M-commonvoice")
model = AutoModelForSeq2SeqLM.from_pretrained("Propicto/t2p-nllb-200-distilled-600M-commonvoice")

inputs = tokenizer("Je mange une pomme", return_tensors="pt").input_ids
outputs = model.generate(inputs.to("cuda:0"), max_new_tokens=40, do_sample=True, top_k=30, top_p=0.95)
pred = tokenizer.decode(outputs[0], skip_special_tokens=True)

Linking the predicted sequence of tokens to the corresponding ARASAAC pictograms

import pandas as pd

def process_output_trad(pred):
    return pred.split()

def read_lexicon(lexicon):
    df = pd.read_csv(lexicon, sep='\t')
    df['keyword_no_cat'] = df['lemma'].str.split(' #').str[0].str.strip().str.replace(' ', '_')
    return df

def get_id_picto_from_predicted_lemma(df_lexicon, lemma):
    id_picto = df_lexicon.loc[df_lexicon['keyword_no_cat'] == lemma, 'id_picto'].tolist()
    return (id_picto[0], lemma) if id_picto else (0, lemma)

lexicon = read_lexicon("lexicon.csv")
sentence_to_map = process_output_trad(pred)
pictogram_ids = [get_id_picto_from_predicted_lemma(lexicon, lemma) for lemma in sentence_to_map]

Viewing the predicted sequence of ARASAAC pictograms in a HTML file

def generate_html(ids):
    html_content = '<html><body>'
    for picto_id, lemma in ids:
        if picto_id != 0:  # ignore invalid IDs
            img_url = f"https://static.arasaac.org/pictograms/{picto_id}/{picto_id}_500.png"
            html_content += f'''
            <figure style="display:inline-block; margin:1px;">
                <img src="{img_url}" alt="{lemma}" width="200" height="200" />
                <figcaption>{lemma}</figcaption>
            </figure>
            '''
    html_content += '</body></html>'
    return html_content
    
html = generate_html(pictogram_ids)
with open("pictograms.html", "w") as file:
    file.write(html)

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}
}