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# whisper-tiny-full-data-v1-testing-maxlength

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5294

## 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: 0.0015
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- num_devices: 8
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1024
- training_steps: 500

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3556        | 14.29 | 100  | 1.5690          |
| 0.284         | 28.57 | 200  | 2.2530          |
| 0.3211        | 42.86 | 300  | 2.7694          |
| 0.3587        | 57.14 | 400  | 3.1295          |
| 0.3842        | 71.43 | 500  | 3.5294          |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.1.0a0+gitcc01568
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["whisper-event", "generated_from_trainer"], "model-index": [{"name": "whisper-tiny-full-data-v1-testing-maxlength", "results": []}]}
transformers pytorch whisper automatic-speech-recognition whisper-event generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

whisper-tiny-full-data-v1-testing-maxlength
===========================================


This model is a fine-tuned version of openai/whisper-tiny on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 3.5294


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: 0.0015
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: tpu
* num\_devices: 8
* total\_train\_batch\_size: 256
* total\_eval\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1024
* training\_steps: 500


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.1.0a0+gitcc01568
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0015
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: tpu
* num\_devices: 8
* total\_train\_batch\_size: 256
* total\_eval\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1024
* training\_steps: 500 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.1.0a0+gitcc01568
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0015
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: tpu
* num\_devices: 8
* total\_train\_batch\_size: 256
* total\_eval\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1024
* training\_steps: 500 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.1.0a0+gitcc01568
* Datasets 2.12.0
* Tokenizers 0.13.3