metadata
language:
- pt
license: apache-2.0
tags:
- generated_from_trainer
base_model: openai/whisper-small
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Small pt
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
config: pt
split: test
args: pt
metrics:
- type: wer
value: 11.034559928386457
name: Wer
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: pt_br
split: test
metrics:
- type: wer
value: 10.68
name: WER
Whisper Small pt
This model is a fine-tuned version of openai/whisper-small on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3836
- Wer: 11.0346
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: 64
- 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: 5000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1139 | 2.0325 | 1000 | 0.2474 | 10.7516 |
| 0.024 | 4.0650 | 2000 | 0.2882 | 10.7692 |
| 0.0065 | 6.0976 | 3000 | 0.3367 | 11.0889 |
| 0.0028 | 8.1301 | 4000 | 0.3731 | 11.0362 |
| 0.0023 | 10.1626 | 5000 | 0.3836 | 11.0346 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1