metadata
library_name: transformers
language:
- es
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- fixie-ai/common_voice_17_0
metrics:
- wer
model-index:
- name: >-
Whisper Medium CV17 Es 5000 steps with the same training configuration and
processing of text as 500-steps_proc3-def3 -with filtering 30sec,
customised optimizer, and processing of text using method 3- ; but with
the values for some training_args scaled in proportion to 5000 - María
Marrón
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: fixie-ai/common_voice_17_0
args: 'config: es, split:test'
metrics:
- name: Wer
type: wer
value: 5.149797262084832
Whisper Medium CV17 Es 5000 steps with the same training configuration and processing of text as 500-steps_proc3-def3 -with filtering 30sec, customised optimizer, and processing of text using method 3- ; but with the values for some training_args scaled in proportion to 5000 - María Marrón
This model is a fine-tuned version of openai/whisper-medium on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1402
- Wer Ortho: 9.1690
- Wer: 5.1498
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-05
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Wer Ortho |
|---|---|---|---|---|---|
| 0.1806 | 0.5 | 1000 | 0.1703 | 6.2760 | 10.5261 |
| 0.1607 | 1.0 | 2000 | 0.1536 | 5.4669 | 9.6349 |
| 0.1599 | 0.6 | 3000 | 0.1525 | 5.3907 | 9.4987 |
| 0.1468 | 0.8 | 4000 | 0.1448 | 5.1333 | 9.2110 |
| 0.1508 | 1.0 | 5000 | 0.1402 | 5.1498 | 9.1690 |
Framework versions
- Transformers 4.53.2
- Pytorch 2.9.1+cu128
- Datasets 2.14.4
- Tokenizers 0.21.4