Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Nyankole
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use Tobius/runyakore with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tobius/runyakore with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Tobius/runyakore")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Tobius/runyakore") model = AutoModelForSpeechSeq2Seq.from_pretrained("Tobius/runyakore") - Notebooks
- Google Colab
- Kaggle
Whisper Small Runyankore
This model is a fine-tuned version of openai/whisper-small on the Yogera data dataset. It achieves the following results on the evaluation set:
- Loss: 1.6289
- Wer: 96.9176
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- training_steps: 200
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.9225 | 0.5 | 100 | 2.3983 | 126.6153 |
| 1.8681 | 1.25 | 200 | 1.6289 | 96.9176 |
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for Tobius/runyakore
Base model
openai/whisper-smallEvaluation results
- Wer on Yogera datatest set self-reported96.918