metadata
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-ks-ob
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: audiofolder
type: audiofolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9999694563225412
wav2vec2-base-finetuned-ks-ob
This model is a fine-tuned version of facebook/wav2vec2-base on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0015
- Accuracy: 1.0000
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
- distributed_type: tpu
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0528 | 1.0 | 256 | 0.0275 | 0.9994 |
| 0.0122 | 2.0 | 512 | 0.0054 | 0.9998 |
| 0.0048 | 3.0 | 768 | 0.0041 | 0.9995 |
| 0.0029 | 4.0 | 1024 | 0.0020 | 0.9999 |
| 0.0019 | 5.0 | 1280 | 0.0015 | 1.0000 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.0.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.1