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# audio_cls_unispeech-sat-base-100h-libri-ft_minds14_finetune

This model is a fine-tuned version of [microsoft/unispeech-sat-base-100h-libri-ft](https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft) on the minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6384
- Accuracy: 0.0708

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 2    | 2.6385          | 0.0442   |
| No log        | 2.0   | 4    | 2.6385          | 0.0796   |
| No log        | 3.0   | 6    | 2.6386          | 0.0619   |
| No log        | 4.0   | 8    | 2.6375          | 0.0531   |
| 2.64          | 5.0   | 10   | 2.6372          | 0.0619   |
| 2.64          | 6.0   | 12   | 2.6376          | 0.0708   |
| 2.64          | 7.0   | 14   | 2.6379          | 0.0708   |
| 2.64          | 8.0   | 16   | 2.6381          | 0.0708   |
| 2.64          | 9.0   | 18   | 2.6384          | 0.0708   |
| 2.638         | 10.0  | 20   | 2.6384          | 0.0708   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["minds14"], "metrics": ["accuracy"], "model-index": [{"name": "audio_cls_unispeech-sat-base-100h-libri-ft_minds14_finetune", "results": []}]}
transformers pytorch tensorboard unispeech-sat audio-classification generated_from_trainer dataset:minds14 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #unispeech-sat #audio-classification #generated_from_trainer #dataset-minds14 #license-apache-2.0 #endpoints_compatible #region-us 

audio\_cls\_unispeech-sat-base-100h-libri-ft\_minds14\_finetune
===============================================================


This model is a fine-tuned version of microsoft/unispeech-sat-base-100h-libri-ft on the minds14 dataset.
It achieves the following results on the evaluation set:


* Loss: 2.6384
* Accuracy: 0.0708


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: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* 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



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* 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 ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #unispeech-sat #audio-classification #generated_from_trainer #dataset-minds14 #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* 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 ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3