Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Yoruba
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Danieljava/whisper-small-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Danieljava/whisper-small-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Danieljava/whisper-small-dv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Danieljava/whisper-small-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("Danieljava/whisper-small-dv", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| language: | |
| - yo | |
| license: apache-2.0 | |
| base_model: openai/whisper-small | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - hf-internal-testing/librispeech_asr_dummy | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: Whisper Small yo - fine_tune | |
| results: | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: librispeech_asr_dataset | |
| type: hf-internal-testing/librispeech_asr_dummy | |
| metrics: | |
| - name: Wer | |
| type: wer | |
| value: 6.587473002159827 | |
| <!-- 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. --> | |
| # Whisper Small yo - fine_tune | |
| This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the librispeech_asr_dataset dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1471 | |
| - Wer Ortho: 6.6134 | |
| - Wer: 6.5875 | |
| ## 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: 16 | |
| - 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: 50 | |
| - training_steps: 500 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | | |
| |:-------------:|:------:|:----:|:---------------:|:---------:|:------:| | |
| | 0.0123 | 3.2895 | 500 | 0.1471 | 6.6134 | 6.5875 | | |
| ### Framework versions | |
| - Transformers 5.0.0 | |
| - Pytorch 2.10.0+cu128 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.22.2 | |