
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# wav2vec2-xlarge-...-common_voice-tr-demo

This model is a fine-tuned version of [facebook/wav2vec2-xlarge-xlsr-...](https://huggingface.co/facebook/wav2vec2-xlarge-xlsr-...) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2701
- Wer: 0.2309
- Cer: 0.0527

## 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: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4388        | 3.7   | 400  | 1.366          | 0.9701 |
| 0.3766        | 7.4   | 800  | 0.4914          | 0.5374 |
| 0.2295        | 11.11 | 1200 | 0.3934          | 0.4125 |
| 0.1121        | 14.81 | 1600 | 0.3264          | 0.2904 |
| 0.1473        | 18.51 | 2000 | 0.3103          | 0.2671 |
| 0.1013        | 22.22 | 2400 | 0.2589          | 0.2324 |
| 0.0704        | 25.92 | 2800 | 0.2826          | 0.2339 |
| 0.0537        | 29.63 | 3200 | 0.2704          | 0.2309 |


### Framework versions

- Transformers 4.12.0.dev0
- Pytorch 1.8.1
- Datasets 1.14.1.dev0
- Tokenizers 0.10.3

{"language": ["tr"], "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xlarge-...-common_voice-tr-demo", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition common_voice generated_from_trainer tr dataset:common_voice endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #endpoints_compatible #region-us 

wav2vec2-xlarge-...-common\_voice-tr-demo
=========================================


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


* Loss: 0.2701
* Wer: 0.2309
* Cer: 0.0527


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: 2
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.12.0.dev0
* Pytorch 1.8.1
* Datasets 1.14.1.dev0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00005
* train\_batch\_size: 2
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.12.0.dev0
* Pytorch 1.8.1
* Datasets 1.14.1.dev0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00005
* train\_batch\_size: 2
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.12.0.dev0
* Pytorch 1.8.1
* Datasets 1.14.1.dev0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.00005
* train\_batch\_size: 2
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.12.0.dev0
* Pytorch 1.8.1
* Datasets 1.14.1.dev0
* Tokenizers 0.10.3