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# wav2vec2-xls-r-100m-common_voice-tr-ft

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-100m](https://huggingface.co/facebook/wav2vec2-xls-r-100m) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4113
- Wer: 1.0
- Cer: 1.0

## 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: 0.0005
- 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: 50.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:---:|:---:|
| 3.1315        | 9.09  | 500  | 3.3832          | 1.0 | 1.0 |
| 3.1163        | 18.18 | 1000 | 3.4252          | 1.0 | 1.0 |
| 3.121         | 27.27 | 1500 | 3.4051          | 1.0 | 1.0 |
| 3.1273        | 36.36 | 2000 | 3.4345          | 1.0 | 1.0 |
| 3.2257        | 45.45 | 2500 | 3.4097          | 1.0 | 1.0 |


### 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-xls-r-100m-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-xls-r-100m-common\_voice-tr-ft
=======================================


This model is a fine-tuned version of facebook/wav2vec2-xls-r-100m on the COMMON\_VOICE - TR dataset.
It achieves the following results on the evaluation set:


* Loss: 3.4113
* Wer: 1.0
* Cer: 1.0


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: 0.0005
* 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: 50.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


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



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* 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: 50.0
* mixed\_precision\_training: Native AMP ### Training results ### 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 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* 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: 50.0
* mixed\_precision\_training: Native AMP ### Training results ### 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 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* 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: 50.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


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