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# Whisper Medium Catalan

This model is a fine-tuned version of [openai/whisper-Medium](https://huggingface.co/openai/whisper-Medium) on the 10 hrs of Catalan Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 4.9217
- eval_wer: 132.1947
- eval_runtime: 3848.0596
- eval_samples_per_second: 0.78
- eval_steps_per_second: 0.78
- epoch: 1.14
- step: 2000

## 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: 0.001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"language": ["hi"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_11_0"], "model-index": [{"name": "Whisper Medium Catalan", "results": []}]}
transformers pytorch tensorboard whisper automatic-speech-recognition hf-asr-leaderboard generated_from_trainer hi dataset:mozilla-foundation/common_voice_11_0 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hi #dataset-mozilla-foundation/common_voice_11_0 #license-apache-2.0 #endpoints_compatible #region-us 




# Whisper Medium Catalan

This model is a fine-tuned version of openai/whisper-Medium on the 10 hrs of Catalan Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 4.9217
- eval_wer: 132.1947
- eval_runtime: 3848.0596
- eval_samples_per_second: 0.78
- eval_steps_per_second: 0.78
- epoch: 1.14
- step: 2000

## 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: 0.001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

# Whisper Medium Catalan

This model is a fine-tuned version of openai/whisper-Medium on the 10 hrs of Catalan Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 4.9217
- eval_wer: 132.1947
- eval_runtime: 3848.0596
- eval_samples_per_second: 0.78
- eval_steps_per_second: 0.78
- epoch: 1.14
- step: 2000 ## 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: 0.001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hi #dataset-mozilla-foundation/common_voice_11_0 #license-apache-2.0 #endpoints_compatible #region-us 
 # Whisper Medium Catalan

This model is a fine-tuned version of openai/whisper-Medium on the 10 hrs of Catalan Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 4.9217
- eval_wer: 132.1947
- eval_runtime: 3848.0596
- eval_samples_per_second: 0.78
- eval_steps_per_second: 0.78
- epoch: 1.14
- step: 2000 ## 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: 0.001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hi #dataset-mozilla-foundation/common_voice_11_0 #license-apache-2.0 #endpoints_compatible #region-us 
# Whisper Medium Catalan

This model is a fine-tuned version of openai/whisper-Medium on the 10 hrs of Catalan Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 4.9217
- eval_wer: 132.1947
- eval_runtime: 3848.0596
- eval_samples_per_second: 0.78
- eval_steps_per_second: 0.78
- epoch: 1.14
- step: 2000## 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: 0.001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2