Automatic Speech Recognition
Transformers
Safetensors
Yoruba
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use EYEDOL/whisper-tiny-yoruba3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EYEDOL/whisper-tiny-yoruba3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="EYEDOL/whisper-tiny-yoruba3")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("EYEDOL/whisper-tiny-yoruba3") model = AutoModelForSpeechSeq2Seq.from_pretrained("EYEDOL/whisper-tiny-yoruba3") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| language: | |
| - yo | |
| license: apache-2.0 | |
| base_model: EYEDOL/whisper-tiny-yoruba2 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - EYEDOL/naija-voices-yoruba-split_0-6 | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: EYEDOL/whisper-tiny-yoruba3 | |
| results: | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: EYEDOL/naija-voices-yoruba-split_0-6 | |
| type: EYEDOL/naija-voices-yoruba-split_0-6 | |
| metrics: | |
| - name: Wer | |
| type: wer | |
| value: 0.6293283063796011 | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # EYEDOL/whisper-tiny-yoruba3 | |
| This model is a fine-tuned version of [EYEDOL/whisper-tiny-yoruba2](https://huggingface.co/EYEDOL/whisper-tiny-yoruba2) on the EYEDOL/naija-voices-yoruba-split_0-6 dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.7111 | |
| - Wer Ortho: 0.7096 | |
| - Wer: 0.6293 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 1e-05 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: constant_with_warmup | |
| - lr_scheduler_warmup_steps: 0.1 | |
| - num_epochs: 5 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| | |
| | 1.4417 | 1.0 | 583 | 0.7132 | 0.7172 | 0.6352 | | |
| | 1.3179 | 2.0 | 1166 | 0.7039 | 0.6964 | 0.6110 | | |
| | 1.2151 | 3.0 | 1749 | 0.7038 | 0.7101 | 0.6275 | | |
| | 1.1268 | 4.0 | 2332 | 0.7058 | 0.7037 | 0.6166 | | |
| | 1.0504 | 5.0 | 2915 | 0.7111 | 0.7096 | 0.6293 | | |
| ### Framework versions | |
| - Transformers 5.0.0 | |
| - Pytorch 2.10.0+cu128 | |
| - Datasets 4.8.3 | |
| - Tokenizers 0.22.2 | |