metadata
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
model-index:
- name: whisper-medium-pt-cv16-fleurs
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_16_1 pt
type: mozilla-foundation/common_voice_16_1
config: pt
split: test
args: pt
metrics:
- name: Wer
type: wer
value: 0.11905377038591959
language:
- pt
whisper-medium-pt-cv16-fleurs
This model is a fine-tuned version of openai/whisper-medium on the mozilla-foundation/common_voice_16_1 pt dataset. It achieves the following results on the evaluation set:
- Loss: 0.1975
- Wer: 0.1191
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-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 2
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2614 | 0.06 | 1000 | 0.2986 | 0.1466 |
| 0.2632 | 0.13 | 2000 | 0.2244 | 0.1316 |
| 0.1694 | 0.19 | 3000 | 0.2086 | 0.1234 |
| 0.1658 | 0.26 | 4000 | 0.1987 | 0.1205 |
| 0.1391 | 0.32 | 5000 | 0.1975 | 0.1191 |
Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1
- Datasets 2.16.1
- Tokenizers 0.15.2