# Load model directly
from transformers import AutoProcessor, AutoModelForAudioClassification
processor = AutoProcessor.from_pretrained("MatsRooth/wav2vec2-base_down_on")
model = AutoModelForAudioClassification.from_pretrained("MatsRooth/wav2vec2-base_down_on")Quick Links
wav2vec2-base_down_on
This model is a fine-tuned version of facebook/wav2vec2-base on the MatsRooth/down_on dataset. It achieves the following results on the evaluation set:
- Loss: 0.1385
- Accuracy: 0.9962
Model description
Binary classifier using facebook/wav2vec2/base for the words "down" and "on".
Intended uses & limitations
This is a demo of binary audio classification that illustrates data layout, training and evaluation using python and slurm.
Training and evaluation data
The data are utterances of "down" and "on" in superb ks. See down_on_copy.py for the subsetting. This puts wav files in locations
like down_on/data/train/on and down_on/data/train/down
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 0
- 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: 5.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6089 | 1.0 | 29 | 0.1385 | 0.9962 |
| 0.1289 | 2.0 | 58 | 0.0510 | 0.9962 |
| 0.0835 | 3.0 | 87 | 0.0433 | 0.9885 |
| 0.0605 | 4.0 | 116 | 0.0330 | 0.9923 |
| 0.0479 | 5.0 | 145 | 0.0273 | 0.9904 |
Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
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Model tree for MatsRooth/wav2vec2-base_down_on
Base model
facebook/wav2vec2-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="MatsRooth/wav2vec2-base_down_on")