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# jpqd-wav2vec2-base-ft-keyword-spotting

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the
superb dataset, using [superb/wav2vec2-base-superb-ks](https://huggingface.co/superb/wav2vec2-base-superb-ks) as a teacher model

It was compressed using [NNCF](https://github.com/openvinotoolkit/nncf) with [Optimum Intel](https://github.com/huggingface/optimum-intel#openvino) following the 
JPQD image classification example.


It achieves the following results on the evaluation set:
- Loss: 0.5632
- Accuracy: 0.9756

## 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: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2245        | 1.0   | 399  | 2.2351          | 0.6209   |
| 6.9856        | 2.0   | 798  | 7.0597          | 0.7354   |
| 10.013        | 3.0   | 1197 | 9.8779          | 0.8069   |
| 11.3484       | 4.0   | 1596 | 11.1949         | 0.8719   |
| 11.6849       | 5.0   | 1995 | 11.5479         | 0.9014   |
| 11.5921       | 6.0   | 2394 | 11.4193         | 0.9495   |
| 0.8911        | 7.0   | 2793 | 0.7334          | 0.9500   |
| 0.8965        | 8.0   | 3192 | 0.6553          | 0.9685   |
| 0.7198        | 9.0   | 3591 | 0.6213          | 0.9669   |
| 0.7372        | 10.0  | 3990 | 0.5929          | 0.9675   |
| 0.7004        | 11.0  | 4389 | 0.5720          | 0.9721   |
| 0.6195        | 12.0  | 4788 | 0.5632          | 0.9756   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- 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": "jpqd-wav2vec2-base-ft-keyword-spotting", "results": []}]}
transformers openvino wav2vec2 audio-classification generated_from_trainer dataset:superb license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #openvino #wav2vec2 #audio-classification #generated_from_trainer #dataset-superb #license-apache-2.0 #endpoints_compatible #region-us 

jpqd-wav2vec2-base-ft-keyword-spotting
======================================


This model is a fine-tuned version of facebook/wav2vec2-base on the
superb dataset, using superb/wav2vec2-base-superb-ks as a teacher model


It was compressed using NNCF with Optimum Intel following the
JPQD image classification example.


It achieves the following results on the evaluation set:


* Loss: 0.5632
* Accuracy: 0.9756


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: 64
* 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 4.26.1
* Pytorch 1.13.1+cu117
* 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: 64
* 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 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #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: 64
* 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 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #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: 64
* 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 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.8.0
* Tokenizers 0.13.2