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---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: finetune_v15
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. -->
# finetune_v15
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7837
- Wer: 193.6017
## 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: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 80
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log | 6.1538 | 10 | 0.7300 | 34.1589 |
| No log | 12.3077 | 20 | 0.7090 | 39.9381 |
| No log | 18.4615 | 30 | 0.7617 | 33.2559 |
| No log | 24.6154 | 40 | 0.7676 | 33.4107 |
| 0.223 | 30.7692 | 50 | 0.7749 | 199.6646 |
| 0.223 | 36.9231 | 60 | 0.7764 | 164.3189 |
| 0.223 | 43.0769 | 70 | 0.7827 | 202.6574 |
| 0.223 | 49.2308 | 80 | 0.7837 | 193.6017 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.2.0
- Datasets 2.20.0
- Tokenizers 0.19.1
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