
<!-- 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. -->

# w2v2-ks-jpqd-quant-FE-finetuned-student

This model is a fine-tuned version of [anton-l/wav2vec2-base-ft-keyword-spotting](https://huggingface.co/anton-l/wav2vec2-base-ft-keyword-spotting) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0869
- Accuracy: 0.9794

## 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: 7e-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: 0.5
- num_epochs: 12.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3477        | 1.0   | 399  | 0.1516          | 0.9637   |
| 5.5957        | 2.0   | 798  | 5.4798          | 0.9545   |
| 8.7806        | 3.0   | 1197 | 8.6491          | 0.9634   |
| 10.4524       | 4.0   | 1596 | 10.2701         | 0.9554   |
| 10.8964       | 5.0   | 1995 | 10.7809         | 0.9647   |
| 10.9322       | 6.0   | 2394 | 10.7806         | 0.9619   |
| 0.2389        | 7.0   | 2793 | 0.1148          | 0.9738   |
| 0.2522        | 8.0   | 3192 | 0.1013          | 0.9747   |
| 0.2213        | 9.0   | 3591 | 0.0983          | 0.9754   |
| 0.2053        | 10.0  | 3990 | 0.0934          | 0.9768   |
| 0.1543        | 11.0  | 4389 | 0.0875          | 0.9779   |
| 0.1836        | 12.0  | 4788 | 0.0869          | 0.9794   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["superb"], "metrics": ["accuracy"], "model-index": [{"name": "w2v2-ks-jpqd-quant-FE-finetuned-student", "results": []}]}
transformers pytorch openvino wav2vec2 audio-classification generated_from_trainer dataset:superb license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #openvino #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us 

w2v2-ks-jpqd-quant-FE-finetuned-student
=======================================


This model is a fine-tuned version of anton-l/wav2vec2-base-ft-keyword-spotting on the superb dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0869
* Accuracy: 0.9794


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: 7e-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: 0.5
* num\_epochs: 12.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7e-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: 0.5
* num\_epochs: 12.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #openvino #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7e-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: 0.5
* num\_epochs: 12.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #openvino #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 7e-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: 0.5
* num\_epochs: 12.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2