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# Whisper Small Ta - Bharat Ramanathan (Kudos to him for developing it)
# This is a copy of his model for academic purpose.

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1803
- Wer: 17.1456

## 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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.3374        | 0.1   | 500  | 0.2579          | 23.3804 |
| 0.29          | 0.2   | 1000 | 0.2260          | 20.9937 |
| 0.2522        | 0.3   | 1500 | 0.2139          | 20.0682 |
| 0.2338        | 0.4   | 2000 | 0.2025          | 19.6785 |
| 0.223         | 0.5   | 2500 | 0.1979          | 18.3147 |
| 0.211         | 0.6   | 3000 | 0.1927          | 17.8276 |
| 0.2032        | 0.7   | 3500 | 0.1865          | 17.3892 |
| 0.1978        | 0.8   | 4000 | 0.1839          | 17.5353 |
| 0.1972        | 0.9   | 4500 | 0.1812          | 17.0969 |
| 0.1894        | 1.0   | 5000 | 0.1803          | 17.1456 |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2

{"language": ["ta"], "license": "apache-2.0", "tags": ["whisper-event", "generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "Whisper Small Tamil", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "google/fleurs", "type": "google/fleurs", "config": "ta_in", "split": "test"}, "metrics": [{"type": "wer", "value": 15.8, "name": "WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "mozilla-foundation/common_voice_11_0", "type": "mozilla-foundation/common_voice_11_0", "config": "ta", "split": "test"}, "metrics": [{"type": "wer", "value": 11.15, "name": "WER"}]}]}]}
transformers pytorch whisper automatic-speech-recognition whisper-event generated_from_trainer ta license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #ta #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 

Whisper Small Ta - Bharat Ramanathan (Kudos to him for developing it)
=====================================================================


This is a copy of his model for academic purpose.
=================================================


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


* Loss: 0.1803
* Wer: 17.1456


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: 32
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 5000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 5000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #ta #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 5000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #ta #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 5000
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.0.dev0
* Pytorch 1.13.0+cu117
* Datasets 2.7.1
* Tokenizers 0.13.2