# Whisper small by ehzawad

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1104
- Wer: 31.3274

## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 8000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2424        | 0.27  | 500  | 0.2407          | 63.1783 |
| 0.1559        | 0.53  | 1000 | 0.1633          | 48.0380 |
| 0.1255        | 0.8   | 1500 | 0.1394          | 42.6625 |
| 0.0899        | 1.07  | 2000 | 0.1231          | 38.6982 |
| 0.0872        | 1.34  | 2500 | 0.1172          | 37.3415 |
| 0.0755        | 1.6   | 3000 | 0.1091          | 35.4971 |
| 0.0786        | 1.87  | 3500 | 0.1042          | 34.6567 |
| 0.0499        | 2.14  | 4000 | 0.1047          | 33.2752 |
| 0.0468        | 2.4   | 4500 | 0.1027          | 32.7874 |
| 0.0436        | 2.67  | 5000 | 0.1019          | 32.2877 |
| 0.0379        | 2.94  | 5500 | 0.1000          | 31.7168 |
| 0.025         | 3.2   | 6000 | 0.1062          | 31.6455 |
| 0.0282        | 3.47  | 6500 | 0.1050          | 31.4699 |
| 0.0249        | 3.74  | 7000 | 0.1060          | 31.3737 |
| 0.0231        | 4.01  | 7500 | 0.1049          | 31.1969 |
| 0.0183        | 4.27  | 8000 | 0.1104          | 31.3274 |


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3

{"language": ["bn"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_13_0"], "metrics": ["wer"], "model-index": [{"name": "Whisper small by ehzawad", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 13.0", "type": "mozilla-foundation/common_voice_13_0", "config": "bn", "split": "test", "args": "config: lt, split: test"}, "metrics": [{"type": "wer", "value": 31.32744623273038, "name": "Wer"}]}]}]}
transformers pytorch tensorboard whisper automatic-speech-recognition hf-asr-leaderboard generated_from_trainer bn dataset:mozilla-foundation/common_voice_13_0 license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #bn #dataset-mozilla-foundation/common_voice_13_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us 

Whisper small by ehzawad
========================


This model is a fine-tuned version of openai/whisper-small on the Common Voice 13.0 dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1104
* Wer: 31.3274


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: 4
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 8000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 4
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 8000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #bn #dataset-mozilla-foundation/common_voice_13_0 #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: 4
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 8000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.30.0.dev0
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3