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# whisper-tiny-lg

This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the common_voice_6_0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.0726
- eval_wer: 228.8298
- eval_runtime: 246.146
- eval_samples_per_second: 2.84
- eval_steps_per_second: 0.179
- epoch: 41.67
- step: 1000

## 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: 1000
- training_steps: 10000
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.11.0
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_6_0"], "model-index": [{"name": "whisper-tiny-lg", "results": []}]}
transformers pytorch whisper automatic-speech-recognition generated_from_trainer dataset:common_voice_6_0 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_6_0 #license-apache-2.0 #endpoints_compatible #region-us 




# whisper-tiny-lg

This model is a fine-tuned version of openai/whisper-tiny on the common_voice_6_0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.0726
- eval_wer: 228.8298
- eval_runtime: 246.146
- eval_samples_per_second: 2.84
- eval_steps_per_second: 0.179
- epoch: 41.67
- step: 1000

## 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: 1000
- training_steps: 10000
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.11.0
- Datasets 2.9.0
- Tokenizers 0.13.2

# whisper-tiny-lg

This model is a fine-tuned version of openai/whisper-tiny on the common_voice_6_0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.0726
- eval_wer: 228.8298
- eval_runtime: 246.146
- eval_samples_per_second: 2.84
- eval_steps_per_second: 0.179
- epoch: 41.67
- step: 1000 ## 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: 1000
- training_steps: 10000
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.11.0
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_6_0 #license-apache-2.0 #endpoints_compatible #region-us 
 # whisper-tiny-lg

This model is a fine-tuned version of openai/whisper-tiny on the common_voice_6_0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.0726
- eval_wer: 228.8298
- eval_runtime: 246.146
- eval_samples_per_second: 2.84
- eval_steps_per_second: 0.179
- epoch: 41.67
- step: 1000 ## 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: 1000
- training_steps: 10000
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.11.0
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_6_0 #license-apache-2.0 #endpoints_compatible #region-us 
# whisper-tiny-lg

This model is a fine-tuned version of openai/whisper-tiny on the common_voice_6_0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.0726
- eval_wer: 228.8298
- eval_runtime: 246.146
- eval_samples_per_second: 2.84
- eval_steps_per_second: 0.179
- epoch: 41.67
- step: 1000## 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: 1000
- training_steps: 10000
- mixed_precision_training: Native AMP### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.11.0
- Datasets 2.9.0
- Tokenizers 0.13.2