metadata
library_name: transformers
language:
- sw
widget:
- example_title: speech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: speech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Small SW-eolang
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17
type: mozilla-foundation/common_voice_17_0
config: sw
split: test
args: sw
metrics:
- name: Wer
type: wer
value: 27.951115548558043
Whisper Small SW-eolang
This model is a fine-tuned version of openai/whisper-small on the Common Voice 17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5136
- Wer Ortho: 36.8520
- Wer: 27.9511
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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.4894 | 0.1721 | 500 | 0.7495 | 47.1590 | 39.6183 |
| 0.4068 | 0.3441 | 1000 | 0.6356 | 44.4535 | 36.3763 |
| 0.4137 | 0.5162 | 1500 | 0.5934 | 41.9094 | 33.4866 |
| 0.3759 | 0.6882 | 2000 | 0.5590 | 41.4031 | 33.1765 |
| 0.38 | 0.8603 | 2500 | 0.5293 | 37.2958 | 28.8699 |
| 0.2027 | 1.0323 | 3000 | 0.5235 | 37.4755 | 29.0340 |
| 0.2089 | 1.2044 | 3500 | 0.5149 | 35.8239 | 27.4845 |
| 0.2282 | 1.3765 | 4000 | 0.5136 | 36.8520 | 27.9511 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.1.0
- Datasets 2.21.0
- Tokenizers 0.19.1