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---
license: apache-2.0
base_model: guilhermebastos96/whisper-large-v2-finetuning
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: whisper-large-v2-finetuning-2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: pt
split: None
args: pt
metrics:
- name: Wer
type: wer
value: 11.81143898462227
---
<!-- 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-large-v2-finetuning-2
This model is a fine-tuned version of [guilhermebastos96/whisper-large-v2-finetuning](https://huggingface.co/guilhermebastos96/whisper-large-v2-finetuning) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2251
- Wer: 11.8114
## 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: 16
- eval_batch_size: 8
- 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: 6000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0724 | 0.5089 | 1000 | 0.2000 | 15.6703 |
| 0.0322 | 1.0178 | 2000 | 0.2156 | 12.0592 |
| 0.0398 | 1.5267 | 3000 | 0.2065 | 9.9843 |
| 0.0167 | 2.0356 | 4000 | 0.2091 | 10.5134 |
| 0.0107 | 2.5445 | 5000 | 0.2181 | 13.2453 |
| 0.0035 | 3.0534 | 6000 | 0.2251 | 11.8114 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.2.1
- Datasets 2.20.0
- Tokenizers 0.19.1
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