metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-large-es-cv11-2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: common_voice_11_0
config: es
split: validation[:1000]
args: es
metrics:
- name: Wer
type: wer
value: 3.7010962486171173
whisper-large-es-cv11-2
This model is a fine-tuned version of openai/whisper-large-v2 on the common_voice_11_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1320
- Wer: 3.7011
- Cer: 1.0555
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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- training_steps: 20000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 0.1837 | 0.32 | 1000 | 0.1669 | 4.2442 | 1.2488 |
| 0.1343 | 0.64 | 2000 | 0.1444 | 4.0833 | 1.2084 |
| 0.1312 | 0.96 | 3000 | 0.1362 | 3.9324 | 1.1933 |
| 0.1206 | 1.28 | 4000 | 0.1333 | 3.8520 | 1.1748 |
| 0.1143 | 1.6 | 5000 | 0.1321 | 3.6508 | 1.0572 |
| 0.1202 | 1.92 | 6000 | 0.1291 | 3.8017 | 1.1311 |
| 0.0856 | 2.24 | 7000 | 0.1325 | 3.7011 | 1.0841 |
| 0.1005 | 2.56 | 8000 | 0.1320 | 3.7011 | 1.0555 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2