metadata
base_model: openai/whisper-base
datasets:
- fleurs
language:
- es
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Base Spanish Punctuation 4k - Chee Li
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Google Fleurs
type: fleurs
config: es_419
split: None
args: 'config: es split: test'
metrics:
- type: wer
value: 35.04427822199295
name: Wer
Whisper Base Spanish Punctuation 4k - Chee Li
This model is a fine-tuned version of openai/whisper-base on the Google Fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.4406
- Wer: 35.0443
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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 |
|---|---|---|---|---|
| 0.4698 | 4.9751 | 1000 | 0.3323 | 20.7377 |
| 0.1943 | 9.9502 | 2000 | 0.3957 | 31.6740 |
| 0.0952 | 14.9254 | 3000 | 0.4278 | 33.7030 |
| 0.0577 | 19.9005 | 4000 | 0.4406 | 35.0443 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.20.3