Instructions to use dmatekenya/whisper-small-chichewa-1h with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmatekenya/whisper-small-chichewa-1h with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="dmatekenya/whisper-small-chichewa-1h")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("dmatekenya/whisper-small-chichewa-1h") model = AutoModelForSpeechSeq2Seq.from_pretrained("dmatekenya/whisper-small-chichewa-1h") - Notebooks
- Google Colab
- Kaggle
whisper-small-chichewa-1h
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: 3.4894
- Wer: 156.3333
- Cer: 118.0469
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: 2
- 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: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 3.4192 | 0.0820 | 10 | 3.6337 | 207.1667 | 156.2051 |
| 3.1910 | 0.1639 | 20 | 3.4894 | 156.3333 | 118.0469 |
Framework versions
- Transformers 5.4.0
- Pytorch 2.11.0
- Datasets 4.8.5
- Tokenizers 0.22.2
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Model tree for dmatekenya/whisper-small-chichewa-1h
Base model
openai/whisper-small