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
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 4.8923 | 0.77 | 100 | 0.7710 | 0.7413 |
| 0.7507 | 1.54 | 200 | 0.7249 | 0.7585 |
| 0.7033 | 2.31 | 300 | 0.7105 | 0.7247 |
| 0.6888 | 3.08 | 400 | 0.6829 | 0.7229 |
| 0.6471 | 3.85 | 500 | 0.6682 | 0.6802 |
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0