File size: 2,087 Bytes
5b017ec |
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 |
---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- balbus-classifier
metrics:
- accuracy
model-index:
- name: miosipof/whisper-small-ft-balbus-sep28k-v1
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: 0.7953596287703016
---
<!-- 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
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: 0.5668
- Accuracy: 0.7954
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 404 | 0.4751 | 0.7748 |
| 0.494 | 2.0 | 808 | 0.4533 | 0.7901 |
| 0.3256 | 3.0 | 1212 | 0.5668 | 0.7954 |
### Framework versions
- Transformers 4.48.1
- Pytorch 2.2.0
- Datasets 3.2.0
- Tokenizers 0.21.0
|