File size: 2,350 Bytes
5ab3e14 df976c1 5ab3e14 df976c1 5ab3e14 91cf013 df976c1 5ab3e14 91cf013 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
---
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_15_0
metrics:
- wer
model-index:
- name: Whisper Small Luganda
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 15.0
type: mozilla-foundation/common_voice_15_0
config: lg
split: validation
args: 'config: lu, split: test'
metrics:
- name: Wer
type: wer
value: 40.49254109791658
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Luganda
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 15.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3827
- Wer: 40.4925
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.6714 | 0.1129 | 500 | 0.7163 | 66.8396 |
| 0.4722 | 0.2258 | 1000 | 0.5435 | 54.3887 |
| 0.4207 | 0.3388 | 1500 | 0.4766 | 49.0312 |
| 0.3891 | 0.4517 | 2000 | 0.4403 | 45.2288 |
| 0.3737 | 0.5646 | 2500 | 0.4167 | 44.0403 |
| 0.3386 | 0.6775 | 3000 | 0.3994 | 41.2405 |
| 0.3402 | 0.7904 | 3500 | 0.3887 | 41.2300 |
| 0.3089 | 0.9033 | 4000 | 0.3827 | 40.4925 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
|