LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech

TL;DR

LeBenchmark 2.0 is a comprehensive, open-source framework for evaluating and developing French speech SSL models, featuring large datasets, multiple pre-trained models, and diverse downstream tasks, highlighting performance and energy considerations.

Contribution

It introduces a standardized, replicable framework with extensive datasets, multiple pre-trained models, and evaluation protocols for French speech SSL, advancing research and benchmarking.

Findings

Pre-trained models outperform previous models and multilingual benchmarks.

Models trained on 14,000 hours of speech show improved performance.

Training larger models increases energy consumption up to fourfold.

Abstract

Self-supervised learning (SSL) is at the origin of unprecedented improvements in many different domains including computer vision and natural language processing. Speech processing drastically benefitted from SSL as most of the current domain-related tasks are now being approached with pre-trained models. This work introduces LeBenchmark 2.0 an open-source framework for assessing and building SSL-equipped French speech technologies. It includes documented, large-scale and heterogeneous corpora with up to 14,000 hours of heterogeneous speech, ten pre-trained SSL wav2vec 2.0 models containing from 26 million to one billion learnable parameters shared with the community, and an evaluation protocol made of six downstream tasks to complement existing benchmarks. LeBenchmark 2.0 also presents unique perspectives on pre-trained SSL models for speech with the investigation of frozen versus fine-tuned downstream models, task-agnostic versus task-specific pre-trained models as well as a discussion on the carbon footprint of large-scale model training. Overall, the newly introduced models trained on 14,000 hours of French speech outperform multilingual and previous LeBenchmark SSL models across the benchmark but also required up to four times more energy for pre-training.