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# Whisper Base Noisy

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Noisy Common Voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4454
- Wer: 59.3212

## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.3125        | 3.19  | 1000 | 1.0918          | 56.8476 |
| 0.0585        | 6.39  | 2000 | 1.2650          | 58.9703 |
| 0.0153        | 9.58  | 3000 | 1.3946          | 58.3412 |
| 0.0066        | 12.78 | 4000 | 1.4454          | 59.3212 |


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["alxfng/noisycommonvoice"], "metrics": ["wer"], "model-index": [{"name": "Whisper Base Noisy", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Noisy Common Voice", "type": "alxfng/noisycommonvoice", "config": "en", "split": "None"}, "metrics": [{"type": "wer", "value": 59.32123598390824, "name": "Wer"}]}]}]}
transformers pytorch tensorboard whisper automatic-speech-recognition generated_from_trainer en dataset:alxfng/noisycommonvoice license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #en #dataset-alxfng/noisycommonvoice #license-apache-2.0 #model-index #endpoints_compatible #region-us 

Whisper Base Noisy
==================


This model is a fine-tuned version of openai/whisper-base on the Noisy Common Voice dataset.
It achieves the following results on the evaluation set:


* Loss: 1.4454
* Wer: 59.3212


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: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 4000


### Training results



### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 4000 ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #generated_from_trainer #en #dataset-alxfng/noisycommonvoice #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 4000 ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.1+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3