Instructions to use Bedru/whisper-medium-amharic-smoke with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bedru/whisper-medium-amharic-smoke with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Bedru/whisper-medium-amharic-smoke")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Bedru/whisper-medium-amharic-smoke") model = AutoModelForSpeechSeq2Seq.from_pretrained("Bedru/whisper-medium-amharic-smoke", device_map="auto") - Notebooks
- Google Colab
- Kaggle
whisper-medium-amharic-smoke
This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.7905
- Wer: 1.7508
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: 8
- 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: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.0842 | 1.6667 | 15 | 1.9306 | 1.6197 |
| 1.7936 | 3.3333 | 30 | 1.7905 | 1.7508 |
Framework versions
- Transformers 5.12.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for Bedru/whisper-medium-amharic-smoke
Base model
openai/whisper-small