metadata
language:
- sw
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Small Swahili - Badili
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 13.0
type: mozilla-foundation/common_voice_13_0
config: sw
split: test
args: 'config: sw, split: test'
metrics:
- name: Wer
type: wer
value: 98.40119332745073
Whisper Small Swahili - Badili
This model is a fine-tuned version of openai/whisper-medium on the Common Voice 13.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4329
- Wer: 98.4012
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 12000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.3563 | 0.35 | 1000 | 0.4938 | 100.5715 |
| 0.2853 | 0.69 | 2000 | 0.4143 | 100.7007 |
| 0.1612 | 1.04 | 3000 | 0.3910 | 100.9748 |
| 0.1399 | 1.38 | 4000 | 0.3762 | 98.4989 |
| 0.1657 | 1.73 | 5000 | 0.3700 | 90.3357 |
| 0.0818 | 2.08 | 6000 | 0.3775 | 98.0493 |
| 0.0749 | 2.42 | 7000 | 0.3768 | 97.9936 |
| 0.0637 | 2.77 | 8000 | 0.3822 | 92.9440 |
| 0.0355 | 3.11 | 9000 | 0.4036 | 93.8979 |
| 0.0299 | 3.46 | 10000 | 0.4141 | 97.9695 |
| 0.0277 | 3.8 | 11000 | 0.4175 | 98.2961 |
| 0.0147 | 4.15 | 12000 | 0.4329 | 98.4012 |
Framework versions
- Transformers 4.29.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3