Instructions to use buumba641/nyanja-asr-whisper-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use buumba641/nyanja-asr-whisper-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="buumba641/nyanja-asr-whisper-tiny")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("buumba641/nyanja-asr-whisper-tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("buumba641/nyanja-asr-whisper-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
nyanja-asr-whisper-tiny
This model is a fine-tuned version of buumba641/nyanja-asr-whisper-tiny on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.0349
- Wer: 0.9721
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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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: 50
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.8296 | 2.7027 | 100 | 1.8980 | 1.2775 |
| 1.4551 | 5.4054 | 200 | 1.8875 | 1.0809 |
| 0.6801 | 8.1081 | 300 | 1.9566 | 1.0432 |
| 0.4784 | 10.8108 | 400 | 2.0349 | 0.9721 |
| 0.2899 | 13.5135 | 500 | 2.1027 | 0.9847 |
| 0.1860 | 16.2162 | 600 | 2.1898 | 0.9777 |
| 0.1292 | 18.9189 | 700 | 2.2167 | 1.0126 |
| 0.0690 | 21.6216 | 800 | 2.2689 | 1.0558 |
| 0.0401 | 24.3243 | 900 | 2.2870 | 1.0098 |
| 0.0350 | 27.0270 | 1000 | 2.3181 | 1.0349 |
| 0.0183 | 29.7297 | 1100 | 2.3307 | 1.2706 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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