metadata
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- minds14
metrics:
- accuracy
model-index:
- name: audio_classification
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: minds14
type: minds14
config: en-US
split: train
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.02654867256637168
audio_classification (default from Skill Academy, I just learn and run the program provided)
This model is a fine-tuned version of facebook/wav2vec2-base on the minds14 dataset. It achieves the following results on the evaluation set:
- Loss: 2.6736
- Accuracy: 0.0265
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-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.8 | 3 | 2.6313 | 0.1062 |
| No log | 1.8667 | 7 | 2.6508 | 0.0708 |
| 2.6379 | 2.9333 | 11 | 2.6587 | 0.0531 |
| 2.6379 | 4.0 | 15 | 2.6631 | 0.0442 |
| 2.6379 | 4.8 | 18 | 2.6712 | 0.0354 |
| 2.6277 | 5.8667 | 22 | 2.6724 | 0.0354 |
| 2.6277 | 6.9333 | 26 | 2.6745 | 0.0177 |
| 2.6257 | 8.0 | 30 | 2.6736 | 0.0265 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1