File size: 1,700 Bytes
83ca424 a3ea447 83ca424 a3ea447 83ca424 a3ea447 83ca424 a3ea447 83ca424 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 |
---
library_name: transformers
language:
- es
license: apache-2.0
base_model: openai/whisper-large
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper openai-whisper-large
results: []
---
<!-- 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 openai-whisper-large
This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the llamadas ecu911 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1954
- Wer: 40.5791
## 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: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.3338 | 2.6596 | 500 | 0.7921 | 42.5161 |
| 0.0335 | 5.3191 | 1000 | 0.9873 | 40.2465 |
| 0.0083 | 7.9787 | 1500 | 1.1470 | 40.3639 |
| 0.0007 | 10.6383 | 2000 | 1.1954 | 40.5791 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|