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# whisper-small-ft-common-language-id

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_language dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6409
- Accuracy: 0.8860

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 0
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1767        | 1.0   | 694  | 1.1063          | 0.7514   |
| 0.582         | 2.0   | 1388 | 0.6595          | 0.8327   |
| 0.3172        | 3.0   | 2082 | 0.5887          | 0.8529   |
| 0.196         | 4.0   | 2776 | 0.5332          | 0.8701   |
| 0.0858        | 5.0   | 3470 | 0.5705          | 0.8733   |
| 0.0477        | 6.0   | 4164 | 0.6311          | 0.8779   |
| 0.0353        | 7.0   | 4858 | 0.6011          | 0.8825   |
| 0.0033        | 8.0   | 5552 | 0.6186          | 0.8843   |
| 0.0071        | 9.0   | 6246 | 0.6409          | 0.8860   |
| 0.0074        | 10.0  | 6940 | 0.6334          | 0.8860   |


### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["common_language"], "metrics": ["accuracy"], "base_model": "openai/whisper-small", "model-index": [{"name": "whisper-small-ft-common-language-id", "results": []}]}
transformers pytorch tensorboard safetensors whisper audio-classification generated_from_trainer dataset:common_language base_model:openai/whisper-small license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #whisper #audio-classification #generated_from_trainer #dataset-common_language #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us 

whisper-small-ft-common-language-id
===================================


This model is a fine-tuned version of openai/whisper-small on the common\_language dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6409
* Accuracy: 0.8860


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: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 0
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 0
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #whisper #audio-classification #generated_from_trainer #dataset-common_language #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 0
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #whisper #audio-classification #generated_from_trainer #dataset-common_language #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 0
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1
* Datasets 2.9.0
* Tokenizers 0.13.2