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# wav2vec2-base-mirst500-ac

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the /workspace/datasets/datasets/MIR_ST500/MIR_ST500.py dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7566
- Accuracy: 0.7570

## 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: 16
- eval_batch_size: 1
- seed: 0
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3718        | 1.0   | 1304  | 1.4422          | 0.4255   |
| 1.1285        | 2.0   | 2608  | 1.1061          | 0.5869   |
| 1.0275        | 3.0   | 3912  | 0.8825          | 0.6724   |
| 0.9982        | 4.0   | 5216  | 0.9181          | 0.6713   |
| 0.9482        | 5.0   | 6520  | 0.8717          | 0.6971   |
| 0.8687        | 6.0   | 7824  | 0.8041          | 0.7164   |
| 0.8841        | 7.0   | 9128  | 0.8869          | 0.7034   |
| 0.8094        | 8.0   | 10432 | 0.8216          | 0.7172   |
| 0.7733        | 9.0   | 11736 | 0.8018          | 0.7298   |
| 0.7892        | 10.0  | 13040 | 0.7517          | 0.7426   |
| 0.8736        | 11.0  | 14344 | 0.7482          | 0.7482   |
| 0.7035        | 12.0  | 15648 | 0.7730          | 0.7488   |
| 0.7361        | 13.0  | 16952 | 0.7677          | 0.7510   |
| 0.7808        | 14.0  | 18256 | 0.7765          | 0.7512   |
| 0.7359        | 15.0  | 19560 | 0.7566          | 0.7570   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.9.1+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["mir_st500"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-base-mirst500-ac", "results": []}]}
transformers pytorch tensorboard wav2vec2 audio-classification generated_from_trainer dataset:mir_st500 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-mir_st500 #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-base-mirst500-ac
=========================


This model is a fine-tuned version of facebook/wav2vec2-base on the /workspace/datasets/datasets/MIR\_ST500/MIR\_ST500.py dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7566
* Accuracy: 0.7570


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: 16
* eval\_batch\_size: 1
* seed: 0
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* total\_eval\_batch\_size: 2
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 15.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.9.1+cu102
* Datasets 2.0.0
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 1
* seed: 0
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* total\_eval\_batch\_size: 2
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 15.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.9.1+cu102
* Datasets 2.0.0
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-mir_st500 #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 1
* seed: 0
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* total\_eval\_batch\_size: 2
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 15.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.9.1+cu102
* Datasets 2.0.0
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #dataset-mir_st500 #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 1
* seed: 0
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 128
* total\_eval\_batch\_size: 2
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 15.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.9.1+cu102
* Datasets 2.0.0
* Tokenizers 0.11.6