Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Nyankole
whisper
whisper-event
Generated from Trainer
Instructions to use KitoEver/runyakore_whisper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KitoEver/runyakore_whisper with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="KitoEver/runyakore_whisper")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("KitoEver/runyakore_whisper") model = AutoModelForSpeechSeq2Seq.from_pretrained("KitoEver/runyakore_whisper") - 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:
- eval_loss: 0.9862
- eval_wer: 44.6947
- eval_runtime: 52.8005
- eval_samples_per_second: 4.261
- eval_steps_per_second: 0.549
- epoch: 42.55
- step: 5000
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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- training_steps: 10000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
- Downloads last month
- 26
Model tree for KitoEver/runyakore_whisper
Base model
openai/whisper-small