CoVoST

Download CoVoST corpus and Common Voice data.

Use the provided scripts to generate data splits.

Train an end-to-end ST model using Fairseq.

Evaluate model performance on the Tatoeba evaluation set.

Download the CoVoST and Tatoeba datasets.

Preprocess the data using the provided scripts.

Run the model on the evaluation set.

Analyze the translation quality using metrics like BLEU.

Train an end-to-end ST model on the CoVoST corpus.

Optimize the model for low latency.

Integrate the model into a real-time translation pipeline.

Test and refine the system with user feedback.

Select a low-resource language pair within CoVoST.

Apply transfer learning techniques from high-resource languages.

Fine-tune the model on the low-resource data.

Evaluate the performance and analyze the limitations.

Leverage extended Common Voice splits to maximize data utilization.

Implement data augmentation techniques to increase data variability.

Train a model using the enhanced dataset.

Compare the performance with models trained on standard splits.