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# wav2vec2-common_voice-tr-demo

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3943
- Wer: 0.3340

## 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: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 0.23  | 100  | 3.6296          | 1.0    |
| No log        | 0.46  | 200  | 3.1588          | 0.9999 |
| No log        | 0.69  | 300  | 2.3111          | 1.0083 |
| No log        | 0.92  | 400  | 0.9852          | 0.7981 |
| 3.6643        | 1.15  | 500  | 0.7056          | 0.7363 |
| 3.6643        | 1.38  | 600  | 0.6146          | 0.6287 |
| 3.6643        | 1.61  | 700  | 0.5583          | 0.6195 |
| 3.6643        | 1.84  | 800  | 0.5529          | 0.5678 |
| 3.6643        | 2.07  | 900  | 0.5280          | 0.5373 |
| 0.5896        | 2.3   | 1000 | 0.5253          | 0.5349 |
| 0.5896        | 2.53  | 1100 | 0.4803          | 0.5057 |
| 0.5896        | 2.76  | 1200 | 0.4562          | 0.5132 |
| 0.5896        | 2.99  | 1300 | 0.4252          | 0.4873 |
| 0.5896        | 3.22  | 1400 | 0.4428          | 0.4831 |
| 0.368         | 3.45  | 1500 | 0.4510          | 0.4779 |
| 0.368         | 3.68  | 1600 | 0.4404          | 0.4946 |
| 0.368         | 3.91  | 1700 | 0.4330          | 0.4785 |
| 0.368         | 4.14  | 1800 | 0.4358          | 0.4558 |
| 0.368         | 4.37  | 1900 | 0.4126          | 0.4643 |
| 0.2629        | 4.6   | 2000 | 0.4197          | 0.4529 |
| 0.2629        | 4.83  | 2100 | 0.4064          | 0.4409 |
| 0.2629        | 5.06  | 2200 | 0.4285          | 0.4514 |
| 0.2629        | 5.29  | 2300 | 0.4193          | 0.4204 |
| 0.2629        | 5.52  | 2400 | 0.4301          | 0.4219 |
| 0.2072        | 5.75  | 2500 | 0.4222          | 0.4335 |
| 0.2072        | 5.98  | 2600 | 0.4077          | 0.4231 |
| 0.2072        | 6.21  | 2700 | 0.4132          | 0.4121 |
| 0.2072        | 6.44  | 2800 | 0.4113          | 0.4220 |
| 0.2072        | 6.67  | 2900 | 0.4101          | 0.4175 |
| 0.1731        | 6.9   | 3000 | 0.4240          | 0.4122 |
| 0.1731        | 7.13  | 3100 | 0.4309          | 0.4023 |
| 0.1731        | 7.36  | 3200 | 0.4275          | 0.3987 |
| 0.1731        | 7.59  | 3300 | 0.4289          | 0.4063 |
| 0.1731        | 7.82  | 3400 | 0.4181          | 0.4025 |
| 0.1397        | 8.05  | 3500 | 0.4490          | 0.3885 |
| 0.1397        | 8.28  | 3600 | 0.4198          | 0.3872 |
| 0.1397        | 8.51  | 3700 | 0.3980          | 0.3842 |
| 0.1397        | 8.74  | 3800 | 0.4051          | 0.3876 |
| 0.1397        | 8.97  | 3900 | 0.4080          | 0.3912 |
| 0.1224        | 9.2   | 4000 | 0.4180          | 0.3774 |
| 0.1224        | 9.43  | 4100 | 0.4102          | 0.3820 |
| 0.1224        | 9.66  | 4200 | 0.3978          | 0.3880 |
| 0.1224        | 9.89  | 4300 | 0.4157          | 0.3731 |
| 0.1224        | 10.11 | 4400 | 0.4175          | 0.3741 |
| 0.1012        | 10.34 | 4500 | 0.3887          | 0.3705 |
| 0.1012        | 10.57 | 4600 | 0.4064          | 0.3774 |
| 0.1012        | 10.8  | 4700 | 0.3961          | 0.3622 |
| 0.1012        | 11.03 | 4800 | 0.3912          | 0.3574 |
| 0.1012        | 11.26 | 4900 | 0.4020          | 0.3638 |
| 0.088         | 11.49 | 5000 | 0.4117          | 0.3560 |
| 0.088         | 11.72 | 5100 | 0.3916          | 0.3524 |
| 0.088         | 11.95 | 5200 | 0.4012          | 0.3533 |
| 0.088         | 12.18 | 5300 | 0.4085          | 0.3584 |
| 0.088         | 12.41 | 5400 | 0.4000          | 0.3547 |
| 0.0775        | 12.64 | 5500 | 0.4137          | 0.3525 |
| 0.0775        | 12.87 | 5600 | 0.4005          | 0.3466 |
| 0.0775        | 13.1  | 5700 | 0.3986          | 0.3479 |
| 0.0775        | 13.33 | 5800 | 0.3983          | 0.3470 |
| 0.0775        | 13.56 | 5900 | 0.3940          | 0.3429 |
| 0.0716        | 13.79 | 6000 | 0.3872          | 0.3383 |
| 0.0716        | 14.02 | 6100 | 0.4005          | 0.3384 |
| 0.0716        | 14.25 | 6200 | 0.4005          | 0.3363 |
| 0.0716        | 14.48 | 6300 | 0.3973          | 0.3357 |
| 0.0716        | 14.71 | 6400 | 0.3957          | 0.3347 |
| 0.0639        | 14.94 | 6500 | 0.3942          | 0.3340 |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.11.0

{"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-common_voice-tr-demo", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "COMMON_VOICE - TR", "type": "common_voice", "config": "tr", "split": "test", "args": "Config: tr, Training split: train+validation, Eval split: test"}, "metrics": [{"type": "wer", "value": 0.33398018588499645, "name": "Wer"}]}]}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition common_voice generated_from_trainer tr dataset:common_voice license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 

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


This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON\_VOICE - TR dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3943
* Wer: 0.3340


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: 0.0003
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.11.0