File size: 2,848 Bytes
4824497 31669fc 4824497 31669fc 4824497 31669fc 4824497 31669fc 4824497 31669fc 4824497 31669fc 4824497 31669fc 4824497 31669fc 4824497 31669fc 4824497 31669fc 4824497 |
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 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 |
---
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-v1.6
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: 0.8100908806016922
- name: Precision
type: precision
value: 0.8183656957928802
- name: Recall
type: recall
value: 0.7261306532663316
- name: F1
type: f1
value: 0.7694941042221377
---
<!-- 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-v1.6
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: 0.1091
- Accuracy: 0.8101
- Precision: 0.8184
- Recall: 0.7261
- F1: 0.7695
- Roc-auc: 0.8006
## 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: 2e-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: 1200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc-auc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.1683 | 0.2506 | 200 | 0.1682 | 0.5730 | 0.7364 | 0.0341 | 0.0652 | 0.5123 |
| 0.1494 | 0.5013 | 400 | 0.1446 | 0.7084 | 0.6603 | 0.6838 | 0.6718 | 0.7056 |
| 0.1212 | 0.7519 | 600 | 0.1236 | 0.7629 | 0.6917 | 0.8245 | 0.7523 | 0.7699 |
| 0.1088 | 1.0025 | 800 | 0.1107 | 0.8062 | 0.8337 | 0.6945 | 0.7578 | 0.7936 |
| 0.0955 | 1.2531 | 1000 | 0.1106 | 0.8081 | 0.8036 | 0.7416 | 0.7713 | 0.8006 |
| 0.0997 | 1.5038 | 1200 | 0.1091 | 0.8101 | 0.8184 | 0.7261 | 0.7695 | 0.8006 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.2.0
- Datasets 3.2.0
- Tokenizers 0.20.3
|