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# Whisper Small Khmer - Seanghay Yath

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Google FLEURS & OpenSLR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4484
- Wer: 1.0704

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6.25e-06
- 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: 800
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2052        | 3.33  | 1000 | 0.3582          | 1.0233 |
| 0.0465        | 6.67  | 2000 | 0.3129          | 1.0105 |
| 0.0089        | 10.0  | 3000 | 0.3977          | 1.0214 |
| 0.0016        | 13.33 | 4000 | 0.4484          | 1.0704 |


### Framework versions

- Transformers 4.28.0.dev0
- Pytorch 1.12.1
- Datasets 2.11.1.dev0
- Tokenizers 0.13.3

{"language": ["km"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["openslr", "google/fleurs"], "metrics": ["wer"], "model-index": [{"name": "Whisper Small Khmer - Seanghay Yath", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Google FLEURS", "type": "google/fleurs", "config": "km_kh", "split": "all"}, "metrics": [{"type": "wer", "value": 1.0704381586245146, "name": "Wer"}]}]}]}
transformers pytorch tensorboard whisper automatic-speech-recognition hf-asr-leaderboard generated_from_trainer km dataset:openslr dataset:google/fleurs license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #km #dataset-openslr #dataset-google/fleurs #license-apache-2.0 #model-index #endpoints_compatible #region-us 

Whisper Small Khmer - Seanghay Yath
===================================


This model is a fine-tuned version of openai/whisper-small on the Google FLEURS & OpenSLR dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4484
* Wer: 1.0704


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6.25e-06
* 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: 800
* training\_steps: 4000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.12.1
* Datasets 2.11.1.dev0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6.25e-06
* 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: 800
* training\_steps: 4000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.12.1
* Datasets 2.11.1.dev0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #km #dataset-openslr #dataset-google/fleurs #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6.25e-06
* 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: 800
* training\_steps: 4000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.12.1
* Datasets 2.11.1.dev0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #km #dataset-openslr #dataset-google/fleurs #license-apache-2.0 #model-index #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 6.25e-06
* 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: 800
* training\_steps: 4000
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.28.0.dev0
* Pytorch 1.12.1
* Datasets 2.11.1.dev0
* Tokenizers 0.13.3