A 17.31M parameter multilingual linear projector version 2 trained for automatic speech recognition (ASR) using the
SLAM-ASR speechLLM framework.
Within this framework, only the linear projector was trained alongside a frozen speech encoder (
Whisper-large-v3-turbo)
and frozen LLM (
Villanova 2B).
This model is trained for Automatic Speech Recognition (ASR) and is meant to be the version 2 of the mEUltilingual speechLLM projectors collection.
This linear projector checkpoint can be downloaded and utilised for further finetuning or decoding using the shell scripts provided in the
SLAM-ASR codebase. Kindly refer to the instructions there for further details.
Whisper-large-v3-turbo and Villanova 2B must be downloaded before using this linear projector.
The linear projector was trained with a multilingual dataset covering 5 European languages, that relys on widely used speech datasets:
Common Voice 17.0,
Fleurs, and
Vox-Populi. As the distribution of data across languages is highli imbalanced, we applied a cap of 100K audio samples per language per dataset, discarding any additional samples beyond this threshold. This strategy allowed us to reduce data skew while keeping training computationally feasible. To assess the generalizability and robustness of our models on out-of-domain speech, we used the official evaluation set of the
INTERSPEECH 2025 MLC-SLM Challenge.
For more details on the mEUltilingual speechLLM project and the methodology behind this model, please refer to the following publication:
