metadata
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- speech_commands
metrics:
- accuracy
model-index:
- name: wav2vec_final_output
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: speech_commands
type: speech_commands
config: v0.02
split: test
args: v0.02
metrics:
- name: Accuracy
type: accuracy
value: 0.901840490797546
wav2vec_final_output
This model is a fine-tuned version of facebook/wav2vec2-base on the speech_commands dataset. It achieves the following results on the evaluation set:
- Loss: 0.4410
- Accuracy: 0.9018
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 |
|---|---|---|---|---|
| 1.4588 | 1.0 | 663 | 1.2309 | 0.8763 |
| 0.6109 | 2.0 | 1326 | 0.5745 | 0.8920 |
| 0.4153 | 3.0 | 1989 | 0.4884 | 0.8953 |
| 0.3227 | 4.0 | 2652 | 0.4574 | 0.8980 |
| 0.2806 | 5.0 | 3315 | 0.4412 | 0.8994 |
| 0.207 | 6.0 | 3978 | 0.4403 | 0.9014 |
| 0.2226 | 7.0 | 4641 | 0.4479 | 0.8998 |
| 0.2577 | 8.0 | 5304 | 0.4421 | 0.9014 |
| 0.2188 | 9.0 | 5967 | 0.4408 | 0.9016 |
| 0.2082 | 10.0 | 6630 | 0.4410 | 0.9018 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1