MMT-JEPA

A multimodal machine translation model for English ↔ Twi using a JEPA (Joint Embedding Predictive Architecture) objective.

What it does

Learns a shared latent space across text and audio in both languages by training a predictor to anticipate target representations from context — no reconstruction loss, no cascaded pipeline.

Three training objectives:

• A — Audio → Text (both languages)
• B — Text → Text (translation)
• C — Text → Audio (both languages)

Files

File	Purpose
model.py	MMT_JEPA model + EMA target encoder
dataset.py	ObjA, ObjB, ObjC dataset classes
tokenizer.py	Trains a joint BPE tokenizer on all text data
train.py	Training loop (all objectives)
train_b.py	Training loop (Objective B only)

Setup

pip install torch librosa soundfile sentencepiece datasets

Usage

1. Train the tokenizer

python tokenizer.py
# outputs: tokenizer.model, tokenizer.vocab

2. Train the model

python train.py

Checkpoints saved to checkpoints/epoch{N}.pt after each epoch.

Data

Objective	Dataset
A + C (English audio)	LibriSpeech train-clean-100
A + C (Twi audio)	twi-speech-text-multispeaker-16k
B (translation)	twi-english-paragraph-dataset_news · english-twi-sentences-non-nouns · english-twi-nouns-v2

All datasets load automatically via HuggingFace on first run.

Model config

Edit ModelConfig in model.py to change capacity:

d_model      = 512    # embedding dimension
trunk_layers = 6      # shared transformer depth
vocab_size   = 16_000
n_mels       = 80
sample_rate  = 16_000

Training notes

• First 5 epochs run text-only (ObjB) to warm up representations before audio is introduced
• L2 normalization applied to both sides before MSE loss to keep scale stable across modalities
• EMA target encoder uses cosine-annealed decay (0.990 → 0.996)
• Collapse logged as COLLAPSE when std < 0.01 or cos_sim > 0.99