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---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- balbus-classifier
metrics:
- accuracy
model-index:
- name: whisper-medium-ft-balbus
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: Balbus dataset
type: balbus-classifier
metrics:
- name: Accuracy
type: accuracy
value: 0.465
---
<!-- 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-medium-ft-balbus
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Balbus dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7451
- Accuracy: 0.465
## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 450 | 0.6979 | 0.535 |
| 4.2819 | 2.0 | 900 | 1.1246 | 0.465 |
| 0.8774 | 3.0 | 1350 | 0.7451 | 0.465 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.19.1
|