wav2vec2-large-mms-1b-yoruba-test

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wav2vec2-large-mms-1b-yoruba-test

This model is a fine-tuned version of facebook/mms-1b-all on the common_voice_16_1 dataset. It achieves the following results on the evaluation set:

• Loss: 0.6682
• Wer: 0.6802

Finetuned by Daniel Ogbuigwe

Model description

This checkpoint is a model fine-tuned for multi-lingual ASR using Facebook's Massive Multilingual Speech project. This checkpoint is based on the Wav2Vec2 architecture and makes use of adapter models to transcribe 1000+ languages. The checkpoint consists of 1 billion parameters and has been fine-tuned from facebook/mms-1b on Yoruba.

Intended uses & limitations

More information needed

Training and evaluation data

Common Voice 16.1 Yoruba data

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.001
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• num_epochs: 4
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.38.0.dev0
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0