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# whisper-large-v2-kangri

This model is a fine-tuned version of [vasista22/whisper-hindi-large-v2](https://huggingface.co/vasista22/whisper-hindi-large-v2) on the [bridgeconn/snow-mountain](https://huggingface.co/datasets/bridgeconn/snow-mountain)  dataset for the low resource Indian language- Kangri.
It achieves the following results on the evaluation set:
- Loss: 0.2967
- Wer: 0.1740

## Usage

In order to evaluate this model on an entire dataset, the evaluation codes available in the whisper-finetune repository can be used.

The same repository also provides the scripts for faster inference using whisper-jax.


## Training and evaluation data

Training Data: 
  - [Snow Mountain Dataset for Kangri Language](https://huggingface.co/datasets/bridgeconn/snow-mountain)
  
Evaluation Data: 
  - [Snow Mountain Dataset for Kangri Language](https://huggingface.co/datasets/bridgeconn/snow-mountain)
  - [Kangri Translators Dataset ](https://drive.google.com/drive/folders/16BdOieekGRAo2bFOQDd4YhE2LpgiRnqQ?usp=share_link)

## Training procedure

We implemented Cross-Lingual Phoneme Recognition - a process that leverages patterns in resource-rich languages such as Hindi to recognize utterances in resource-poor languages
such as Kangri. By fine-tuning a pre-trained model of the Whisper-Hindi-Large-V2 on a customised dataset - we have achieved SoTa accuracy.
A customised dataset - consisting of the brigdeconn/snow-mountain and sentences collected from Kangri translators was created. This was then split using the 80/20 
split rule. The results were evaluated with 5000 steps. The model decreases the word error rate by 0.6% after the initial 1000 steps. The Validation Loss increases due to
more data being introduced.


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- total_eval_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: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0001        | 40.0  | 1000 | 0.2442          | 0.1800 |
| 0.0           | 80.0  | 2000 | 0.2752          | 0.1764 |
| 0.0           | 120.0 | 3000 | 0.2870          | 0.1747 |
| 0.0           | 160.0 | 4000 | 0.2940          | 0.1745 |
| 0.0           | 200.0 | 5000 | 0.2967          | 0.1740 |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "whisper-large-v2-kangri", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "snow-moutain-Kangri", "type": "bridgeconn/snow-mountain", "config": "Kangri", "split": "train_500"}, "metrics": [{"type": "wer", "value": 17.4, "name": "WER"}]}]}]}
transformers pytorch whisper automatic-speech-recognition generated_from_trainer doi:10.57967/hf/1591 license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #generated_from_trainer #doi-10.57967/hf/1591 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 

whisper-large-v2-kangri
=======================


This model is a fine-tuned version of vasista22/whisper-hindi-large-v2 on the bridgeconn/snow-mountain dataset for the low resource Indian language- Kangri.
It achieves the following results on the evaluation set:


* Loss: 0.2967
* Wer: 0.1740


Usage
-----


In order to evaluate this model on an entire dataset, the evaluation codes available in the whisper-finetune repository can be used.


The same repository also provides the scripts for faster inference using whisper-jax.


Training and evaluation data
----------------------------


Training Data:


* Snow Mountain Dataset for Kangri Language


Evaluation Data:


* Snow Mountain Dataset for Kangri Language
* Kangri Translators Dataset


Training procedure
------------------


We implemented Cross-Lingual Phoneme Recognition - a process that leverages patterns in resource-rich languages such as Hindi to recognize utterances in resource-poor languages
such as Kangri. By fine-tuning a pre-trained model of the Whisper-Hindi-Large-V2 on a customised dataset - we have achieved SoTa accuracy.
A customised dataset - consisting of the brigdeconn/snow-mountain and sentences collected from Kangri translators was created. This was then split using the 80/20
split rule. The results were evaluated with 5000 steps. The model decreases the word error rate by 0.6% after the initial 1000 steps. The Validation Loss increases due to
more data being introduced.


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 16
* total\_eval\_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: 5000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 16
* total\_eval\_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: 5000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #generated_from_trainer #doi-10.57967/hf/1591 #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: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 16
* total\_eval\_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: 5000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #generated_from_trainer #doi-10.57967/hf/1591 #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: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 16
* total\_eval\_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: 5000
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3