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# wav2vec2-xls-r-300m-th-v7_0

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4099
- Wer: 0.9988
- Cer: 0.7861
- Clean Cer: 0.7617
- Learning Rate: 0.0000

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    | Clean Cer | Rate   |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:---------:|:------:|
| 8.5484        | 0.4   | 500   | 3.6234          | 1.0    | 1.0    | 1.0       | 0.0000 |
| 3.2275        | 0.8   | 1000  | 2.2960          | 0.9998 | 0.7081 | 0.6540    | 0.0000 |
| 0.9955        | 1.2   | 1500  | 1.2224          | 0.9549 | 0.4327 | 0.3756    | 0.0000 |
| 0.66          | 1.61  | 2000  | 0.9559          | 0.9232 | 0.3651 | 0.3040    | 0.0000 |
| 0.546         | 2.01  | 2500  | 0.9207          | 0.9481 | 0.3585 | 0.2826    | 0.0000 |
| 0.4459        | 2.41  | 3000  | 0.7701          | 0.8693 | 0.2940 | 0.2383    | 0.0000 |
| 0.4041        | 2.81  | 3500  | 0.7756          | 0.8224 | 0.2949 | 0.2634    | 0.0000 |
| 0.3637        | 3.21  | 4000  | 0.6015          | 0.7015 | 0.2064 | 0.1807    | 0.0000 |
| 0.334         | 3.61  | 4500  | 0.5615          | 0.6675 | 0.1907 | 0.1638    | 0.0000 |
| 0.3283        | 4.02  | 5000  | 0.6205          | 0.7073 | 0.2092 | 0.1803    | 0.0000 |
| 0.3762        | 4.42  | 5500  | 0.7517          | 0.6366 | 0.1778 | 0.1600    | 0.0000 |
| 0.4954        | 4.82  | 6000  | 0.9374          | 0.7073 | 0.2023 | 0.1735    | 0.0000 |
| 0.5568        | 5.22  | 6500  | 0.8859          | 0.7027 | 0.1982 | 0.1666    | 0.0000 |
| 0.6756        | 5.62  | 7000  | 1.0252          | 0.6802 | 0.1920 | 0.1628    | 0.0000 |
| 0.7752        | 6.02  | 7500  | 1.1259          | 0.7657 | 0.2309 | 0.1908    | 0.0000 |
| 0.8305        | 6.43  | 8000  | 1.3857          | 0.9029 | 0.3252 | 0.2668    | 0.0000 |
| 1.7385        | 6.83  | 8500  | 3.2320          | 0.9998 | 0.9234 | 0.9114    | 0.0000 |
| 2.7839        | 7.23  | 9000  | 3.3238          | 0.9999 | 0.9400 | 0.9306    | 0.0000 |
| 2.8307        | 7.63  | 9500  | 3.2678          | 0.9998 | 0.9167 | 0.9053    | 0.0000 |
| 2.7672        | 8.03  | 10000 | 3.2435          | 0.9995 | 0.8992 | 0.8867    | 0.0000 |
| 2.7426        | 8.43  | 10500 | 3.2396          | 0.9995 | 0.8720 | 0.8561    | 0.0000 |
| 2.7608        | 8.84  | 11000 | 3.2689          | 0.9993 | 0.8399 | 0.8202    | 0.0000 |
| 2.8195        | 9.24  | 11500 | 3.3283          | 0.9989 | 0.8084 | 0.7865    | 0.0000 |
| 2.9044        | 9.64  | 12000 | 3.4099          | 0.9988 | 0.7861 | 0.7617    | 0.0000 |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-xls-r-300m-th-v7_0", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-xls-r-300m-th-v7\_0
============================


This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 3.4099
* Wer: 0.9988
* Cer: 0.7861
* Clean Cer: 0.7617
* Learning Rate: 0.0000


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: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_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: 10
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


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



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_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: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_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: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.0.dev0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_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: 10
* mixed\_precision\_training: Native AMP### Training results### Framework versions


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