metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- fsicoli/cv18-fleurs
metrics:
- wer
model-index:
- name: whisper-medium-pt-cv18-fleurs2-lr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: fsicoli/cv18-fleurs default
type: fsicoli/cv18-fleurs
args: default
metrics:
- name: Wer
type: wer
value: 0.10594170403587444
whisper-medium-pt-cv18-fleurs2-lr
This model is a fine-tuned version of openai/whisper-medium on the fsicoli/cv18-fleurs default dataset. It achieves the following results on the evaluation set:
- Loss: 0.1662
- Wer: 0.1059
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 |
|---|---|---|---|---|
| 0.0876 | 2.3004 | 5000 | 0.1662 | 0.1059 |
| 0.0371 | 4.6009 | 10000 | 0.1839 | 0.0999 |
| 0.0246 | 6.9013 | 15000 | 0.2027 | 0.0997 |
| 0.0072 | 9.2017 | 20000 | 0.2152 | 0.0967 |
| 0.0074 | 11.5022 | 25000 | 0.2163 | 0.0929 |
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.1
- Datasets 2.21.0
- Tokenizers 0.19.1