Automatic Speech Recognition
Transformers
Safetensors
Hausa
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use EYEDOL/whisper-tiny-hausa2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EYEDOL/whisper-tiny-hausa2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="EYEDOL/whisper-tiny-hausa2")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("EYEDOL/whisper-tiny-hausa2") model = AutoModelForSpeechSeq2Seq.from_pretrained("EYEDOL/whisper-tiny-hausa2") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| language: | |
| - ha | |
| license: apache-2.0 | |
| base_model: EYEDOL/whisper-tiny-hausa1 | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - EYEDOL/naija-voices-hausa-split_0-5 | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: EYEDOL/whisper-tiny-hausa2 | |
| results: | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: EYEDOL/naija-voices-hausa-split_0-5 | |
| type: EYEDOL/naija-voices-hausa-split_0-5 | |
| metrics: | |
| - name: Wer | |
| type: wer | |
| value: 0.48092983669344114 | |
| <!-- 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-hausa2 | |
| This model is a fine-tuned version of [EYEDOL/whisper-tiny-hausa1](https://huggingface.co/EYEDOL/whisper-tiny-hausa1) on the EYEDOL/naija-voices-hausa-split_0-5 dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.6946 | |
| - Wer Ortho: 0.5454 | |
| - Wer: 0.4809 | |
| ## 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: 10 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| | |
| | 1.2967 | 1.0 | 665 | 0.6307 | 0.5191 | 0.4636 | | |
| | 1.1962 | 2.0 | 1330 | 0.6195 | 0.5019 | 0.4473 | | |
| | 1.0667 | 3.0 | 1995 | 0.6200 | 0.5036 | 0.4456 | | |
| | 0.9621 | 4.0 | 2660 | 0.6227 | 0.5083 | 0.4455 | | |
| | 0.8693 | 5.0 | 3325 | 0.6323 | 0.5126 | 0.4540 | | |
| | 0.7838 | 6.0 | 3990 | 0.6426 | 0.5192 | 0.4556 | | |
| | 0.7056 | 7.0 | 4655 | 0.6494 | 0.5218 | 0.4650 | | |
| | 0.6303 | 8.0 | 5320 | 0.6652 | 0.5369 | 0.4758 | | |
| | 0.5595 | 9.0 | 5985 | 0.6766 | 0.5332 | 0.4736 | | |
| | 0.4927 | 10.0 | 6650 | 0.6946 | 0.5454 | 0.4809 | | |
| ### Framework versions | |
| - Transformers 5.0.0 | |
| - Pytorch 2.10.0+cu128 | |
| - Datasets 4.8.3 | |
| - Tokenizers 0.22.2 | |