
<!-- 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-base-ms-with-lm

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1256
- Wer: 0.3991

## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.0672        | 1.85  | 500   | 2.9666          | 1.0    |
| 1.6566        | 3.7   | 1000  | 0.7862          | 0.6177 |
| 0.6424        | 5.56  | 1500  | 0.6600          | 0.5478 |
| 0.4438        | 7.41  | 2000  | 0.6148          | 0.4835 |
| 0.3133        | 9.26  | 2500  | 0.6949          | 0.4516 |
| 0.2822        | 11.11 | 3000  | 0.6844          | 0.4533 |
| 0.2338        | 12.96 | 3500  | 0.7682          | 0.4634 |
| 0.2246        | 14.81 | 4000  | 0.8370          | 0.4925 |
| 0.1957        | 16.67 | 4500  | 0.8554          | 0.4377 |
| 0.1809        | 18.52 | 5000  | 0.7211          | 0.4505 |
| 0.1496        | 20.37 | 5500  | 0.8081          | 0.4354 |
| 0.1369        | 22.22 | 6000  | 0.9099          | 0.4360 |
| 0.1285        | 24.07 | 6500  | 0.8051          | 0.4231 |
| 0.1174        | 25.93 | 7000  | 0.9041          | 0.4505 |
| 0.1074        | 27.78 | 7500  | 0.8096          | 0.4310 |
| 0.0904        | 29.63 | 8000  | 0.8589          | 0.4237 |
| 0.0975        | 31.48 | 8500  | 0.9019          | 0.4142 |
| 0.0806        | 33.33 | 9000  | 0.8966          | 0.4382 |
| 0.0772        | 35.19 | 9500  | 1.0612          | 0.4181 |
| 0.0738        | 37.04 | 10000 | 0.8979          | 0.4215 |
| 0.0678        | 38.89 | 10500 | 0.9342          | 0.4103 |
| 0.0626        | 40.74 | 11000 | 0.9992          | 0.4187 |
| 0.0641        | 42.59 | 11500 | 1.0101          | 0.4120 |
| 0.0565        | 44.44 | 12000 | 0.9841          | 0.4287 |
| 0.0535        | 46.3  | 12500 | 1.0049          | 0.4097 |
| 0.0512        | 48.15 | 13000 | 0.9569          | 0.4080 |
| 0.0466        | 50.0  | 13500 | 0.9863          | 0.4276 |
| 0.0514        | 51.85 | 14000 | 0.9594          | 0.4120 |
| 0.0397        | 53.7  | 14500 | 0.9775          | 0.4103 |
| 0.0394        | 55.56 | 15000 | 1.0077          | 0.4131 |
| 0.0393        | 57.41 | 15500 | 0.9763          | 0.4108 |
| 0.0355        | 59.26 | 16000 | 1.1432          | 0.4226 |
| 0.0323        | 61.11 | 16500 | 1.1553          | 0.4192 |
| 0.0344        | 62.96 | 17000 | 1.0437          | 0.4203 |
| 0.0296        | 64.81 | 17500 | 1.0227          | 0.4209 |
| 0.0267        | 66.67 | 18000 | 1.0408          | 0.4287 |
| 0.0283        | 68.52 | 18500 | 1.0811          | 0.4192 |
| 0.0243        | 70.37 | 19000 | 1.0697          | 0.4075 |
| 0.0238        | 72.22 | 19500 | 1.1047          | 0.4164 |
| 0.0234        | 74.07 | 20000 | 1.1585          | 0.4142 |
| 0.02          | 75.93 | 20500 | 1.1183          | 0.4187 |
| 0.0231        | 77.78 | 21000 | 1.0923          | 0.4080 |
| 0.018         | 79.63 | 21500 | 1.1299          | 0.4058 |
| 0.0179        | 81.48 | 22000 | 1.0963          | 0.4008 |
| 0.0152        | 83.33 | 22500 | 1.0676          | 0.4002 |
| 0.0142        | 85.19 | 23000 | 1.1026          | 0.4047 |
| 0.0159        | 87.04 | 23500 | 1.0876          | 0.4058 |
| 0.0144        | 88.89 | 24000 | 1.0943          | 0.3963 |
| 0.0152        | 90.74 | 24500 | 1.0827          | 0.4075 |
| 0.0155        | 92.59 | 25000 | 1.0982          | 0.4019 |
| 0.0124        | 94.44 | 25500 | 1.1284          | 0.3985 |
| 0.0132        | 96.3  | 26000 | 1.1233          | 0.3991 |
| 0.0109        | 98.15 | 26500 | 1.1196          | 0.3980 |
| 0.0102        | 100.0 | 27000 | 1.1256          | 0.3991 |


### Framework versions

- Transformers 4.24.0
- Pytorch 2.0.0+cu118
- Datasets 1.18.3
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-base-ms-with-lm", "results": []}]}
transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-base-ms-with-lm
========================


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


* Loss: 1.1256
* Wer: 0.3991


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: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 100
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.24.0
* Pytorch 2.0.0+cu118
* Datasets 1.18.3
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 100
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 2.0.0+cu118
* Datasets 1.18.3
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #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: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 100
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.24.0
* Pytorch 2.0.0+cu118
* Datasets 1.18.3
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #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: 0.0001
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 100
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.24.0
* Pytorch 2.0.0+cu118
* Datasets 1.18.3
* Tokenizers 0.13.3