File size: 2,602 Bytes
e752aa6 dcbc327 e752aa6 dcbc327 e752aa6 dcbc327 e752aa6 |
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 84 85 86 87 88 89 90 |
---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
model-index:
- name: model_KWS
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: audiofolder
type: audiofolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9825
---
<!-- 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. -->
# model_KWS
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3346
- Accuracy: 0.9825
## Model description
Finetuned on custom commands: "ambient", "light", "off", "on", "scene1", "scene2", "scene3", "void"
## Intended uses & limitations
Intended for keyword spotting applications.
## Training and evaluation data
3200 training samples, 800 testing samples in total.
Originally was recorded 20 samples of every class.
Each sample was randomly augmented with random methods: pitch-shifting, time-stretching, volume-change, gaussian noise.
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0119 | 1.0 | 25 | 1.9832 | 0.375 |
| 1.4505 | 2.0 | 50 | 1.3361 | 0.8337 |
| 1.0767 | 3.0 | 75 | 0.8700 | 0.955 |
| 0.7448 | 4.0 | 100 | 0.6919 | 0.9513 |
| 0.6143 | 5.0 | 125 | 0.5333 | 0.9625 |
| 0.4924 | 6.0 | 150 | 0.4387 | 0.98 |
| 0.4544 | 7.0 | 175 | 0.3844 | 0.985 |
| 0.3888 | 8.0 | 200 | 0.3668 | 0.9812 |
| 0.3734 | 9.0 | 225 | 0.3436 | 0.9825 |
| 0.3522 | 10.0 | 250 | 0.3346 | 0.9825 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.14.0
- Tokenizers 0.13.3
|