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# wav2vec2-large-xls-r-1b-common_voice-tr-ft

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:

- Loss: 0.3015
- Wer: 0.2149
- Cer: 0.0503

## 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: 0.00005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP

### Training results

Check [Training metrics](https://huggingface.co/patrickvonplaten/wav2vec2-xls-r-1b-common_voice-tr-ft/tensorboard).


### Framework versions

- Transformers 4.13.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.15.2.dev0
- Tokenizers 0.10.3

{"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer", "xls_r_repro_common_voice_tr"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-1b-common_voice-tr-ft", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition common_voice generated_from_trainer xls_r_repro_common_voice_tr tr dataset:common_voice license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #xls_r_repro_common_voice_tr #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 




# wav2vec2-large-xls-r-1b-common_voice-tr-ft

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:

- Loss: 0.3015
- Wer: 0.2149
- Cer: 0.0503

## 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: 0.00005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP

### Training results

Check Training metrics.


### Framework versions

- Transformers 4.13.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.15.2.dev0
- Tokenizers 0.10.3

# wav2vec2-large-xls-r-1b-common_voice-tr-ft

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:

- Loss: 0.3015
- Wer: 0.2149
- Cer: 0.0503 ## 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: 0.00005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP ### Training results

Check Training metrics. ### Framework versions

- Transformers 4.13.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.15.2.dev0
- Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #xls_r_repro_common_voice_tr #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 
 # wav2vec2-large-xls-r-1b-common_voice-tr-ft

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:

- Loss: 0.3015
- Wer: 0.2149
- Cer: 0.0503 ## 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: 0.00005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP ### Training results

Check Training metrics. ### Framework versions

- Transformers 4.13.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.15.2.dev0
- Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #xls_r_repro_common_voice_tr #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 
# wav2vec2-large-xls-r-1b-common_voice-tr-ft

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:

- Loss: 0.3015
- Wer: 0.2149
- Cer: 0.0503## 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: 0.00005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100.0
- mixed_precision_training: Native AMP### Training results

Check Training metrics.### Framework versions

- Transformers 4.13.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.15.2.dev0
- Tokenizers 0.10.3