metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- fsicoli/cv16-fleurs
metrics:
- wer
model-index:
- name: whisper-medium-pt-cv16-fleurs2-lr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: fsicoli/cv16-fleurs default
type: fsicoli/cv16-fleurs
args: default
metrics:
- name: Wer
type: wer
value: 0.0932
whisper-medium-pt-cv16-fleurs2-lr
This model is a fine-tuned version of openai/whisper-medium on the fsicoli/cv16-fleurs default dataset. It achieves the following results on the evaluation set:
- Loss: 0.2088
- Wer: 0.0932
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: 6.25e-06
- 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: 5000
- training_steps: 25000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0856 | 2.3343 | 5000 | 0.1601 | 0.1030 |
| 0.0156 | 4.6685 | 10000 | 0.1831 | 0.1003 |
| 0.0189 | 7.0028 | 15000 | 0.1996 | 0.0980 |
| 0.0052 | 9.3371 | 20000 | 0.2079 | 0.0956 |
| 0.0035 | 11.6713 | 25000 | 0.2088 | 0.0932 |
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.1
- Datasets 2.21.0
- Tokenizers 0.19.1