|
--- |
|
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: 0.0929 |
|
--- |
|
|
|
<!-- 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-medium-pt-cv18-fleurs2-lr |
|
|
|
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the fsicoli/cv18-fleurs default dataset for Portuguese. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2163 |
|
- Wer: 0.0929 |
|
|
|
## 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 |
|
|
