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---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- balbus-classifier
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: miosipof/whisper-small-ft-balbus-sep28k-v1.5
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: Apple dataset
type: balbus-classifier
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value:
accuracy: 0.8111877154497023
- name: Precision
type: precision
value:
precision: 0.8133174791914387
- name: Recall
type: recall
value:
recall: 0.7365398420674802
- name: F1
type: f1
value:
f1: 0.7730269353927294
---
<!-- 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. -->
# miosipof/whisper-small-ft-balbus-sep28k-v1.5
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Apple dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1083
- Accuracy: {'accuracy': 0.8111877154497023}
- Precision: {'precision': 0.8133174791914387}
- Recall: {'recall': 0.7365398420674802}
- F1: {'f1': 0.7730269353927294}
## 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: 3e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.5
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------------------------------:|:---------------------------------:|:-------------------------------:|:----------------------------:|
| 0.1718 | 0.1253 | 100 | 0.1705 | {'accuracy': 0.564243183954873} | {'precision': 0.6190476190476191} | {'recall': 0.00466618808327351} | {'f1': 0.009262557890986818} |
| 0.1683 | 0.2506 | 200 | 0.1653 | {'accuracy': 0.6118771544970228} | {'precision': 0.7677642980935875} | {'recall': 0.15900933237616655} | {'f1': 0.26345524829021705} |
| 0.1595 | 0.3759 | 300 | 0.1494 | {'accuracy': 0.6847383265434033} | {'precision': 0.6486175115207373} | {'recall': 0.6062455132806892} | {'f1': 0.6267161410018552} |
| 0.1299 | 0.5013 | 400 | 0.1266 | {'accuracy': 0.7608900031338138} | {'precision': 0.7008928571428571} | {'recall': 0.7889447236180904} | {'f1': 0.7423167848699763} |
| 0.1174 | 0.6266 | 500 | 0.1140 | {'accuracy': 0.7977123158884363} | {'precision': 0.7800674409891345} | {'recall': 0.747307968413496} | {'f1': 0.7633363886342804} |
| 0.1117 | 0.7519 | 600 | 0.1155 | {'accuracy': 0.7919147602632404} | {'precision': 0.7362281270252754} | {'recall': 0.8155061019382628} | {'f1': 0.773841961852861} |
| 0.1072 | 0.8772 | 700 | 0.1074 | {'accuracy': 0.8096208085239737} | {'precision': 0.8282490597576264} | {'recall': 0.7114142139267767} | {'f1': 0.765398725622707} |
| 0.106 | 1.0025 | 800 | 0.1078 | {'accuracy': 0.8077405202130994} | {'precision': 0.8175152749490835} | {'recall': 0.7203876525484566} | {'f1': 0.7658843732112193} |
| 0.1001 | 1.1278 | 900 | 0.1079 | {'accuracy': 0.810404261986838} | {'precision': 0.8174858984689767} | {'recall': 0.7282842785355348} | {'f1': 0.7703113135914958} |
| 0.092 | 1.2531 | 1000 | 0.1083 | {'accuracy': 0.8111877154497023} | {'precision': 0.8133174791914387} | {'recall': 0.7365398420674802} | {'f1': 0.7730269353927294} |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.2.0
- Datasets 3.2.0
- Tokenizers 0.20.3
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