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# wav2vec2-base-MIR_ST500_ASR_109

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the /WORKSPACE/DATASETS/DATASETS/MIR_ST500/MIR_ST500.PY - ASR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6452
- Wer: 0.3732

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 16
- 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 12.5751       | 0.27  | 100   | 6.0291          | 1.0    |
| 4.343         | 0.53  | 200   | 2.8709          | 1.0    |
| 4.1911        | 0.8   | 300   | 2.5472          | 1.0    |
| 2.4535        | 1.06  | 400   | 2.4323          | 1.0    |
| 2.6157        | 1.33  | 500   | 2.2799          | 1.0    |
| 2.4839        | 1.6   | 600   | 2.2722          | 1.0    |
| 2.2787        | 1.86  | 700   | 2.2269          | 1.0    |
| 2.1981        | 2.13  | 800   | 2.2221          | 1.0    |
| 2.159         | 2.39  | 900   | 2.1657          | 1.0    |
| 2.1421        | 2.66  | 1000  | 2.1769          | 1.0    |
| 2.0841        | 2.93  | 1100  | 2.1688          | 1.0    |
| 2.0599        | 3.19  | 1200  | 2.1141          | 1.0    |
| 2.0257        | 3.46  | 1300  | 2.0445          | 1.0    |
| 1.979         | 3.72  | 1400  | 2.0180          | 1.0    |
| 1.9366        | 3.99  | 1500  | 1.9419          | 1.0    |
| 1.8547        | 4.26  | 1600  | 1.8765          | 1.0    |
| 1.3988        | 4.52  | 1700  | 1.4151          | 0.7999 |
| 1.1881        | 4.79  | 1800  | 1.1158          | 0.7347 |
| 0.9557        | 5.05  | 1900  | 1.0095          | 0.6485 |
| 0.9087        | 5.32  | 2000  | 0.9644          | 0.6848 |
| 0.8086        | 5.59  | 2100  | 0.8960          | 0.6119 |
| 0.9106        | 5.85  | 2200  | 0.8892          | 0.5941 |
| 0.8252        | 6.12  | 2300  | 0.8333          | 0.5756 |
| 0.8299        | 6.38  | 2400  | 0.8559          | 0.5838 |
| 0.8021        | 6.65  | 2500  | 0.8201          | 0.5883 |
| 0.7979        | 6.91  | 2600  | 0.8349          | 0.575  |
| 0.7223        | 7.18  | 2700  | 0.7883          | 0.5563 |
| 0.6754        | 7.45  | 2800  | 0.7590          | 0.5393 |
| 0.6454        | 7.71  | 2900  | 0.7411          | 0.5291 |
| 0.6228        | 7.98  | 3000  | 0.7464          | 0.5300 |
| 0.6475        | 8.24  | 3100  | 0.7478          | 0.5295 |
| 0.6452        | 8.51  | 3200  | 0.7555          | 0.5360 |
| 0.5636        | 8.78  | 3300  | 0.7369          | 0.5232 |
| 0.564         | 9.04  | 3400  | 0.7331          | 0.5076 |
| 0.6173        | 9.31  | 3500  | 0.7199          | 0.5034 |
| 0.625         | 9.57  | 3600  | 0.7243          | 0.5193 |
| 0.8122        | 9.84  | 3700  | 0.7436          | 0.5242 |
| 0.5455        | 10.11 | 3800  | 0.7111          | 0.4920 |
| 0.7928        | 10.37 | 3900  | 0.7137          | 0.4858 |
| 0.5446        | 10.64 | 4000  | 0.6874          | 0.4828 |
| 0.4772        | 10.9  | 4100  | 0.6760          | 0.4801 |
| 0.6447        | 11.17 | 4200  | 0.6893          | 0.4886 |
| 0.5818        | 11.44 | 4300  | 0.6789          | 0.4740 |
| 0.4952        | 11.7  | 4400  | 0.7043          | 0.4811 |
| 0.5722        | 11.97 | 4500  | 0.6794          | 0.4766 |
| 0.58          | 12.23 | 4600  | 0.6629          | 0.4580 |
| 0.5432        | 12.5  | 4700  | 0.6907          | 0.4906 |
| 0.4786        | 12.77 | 4800  | 0.6925          | 0.4854 |
| 0.5177        | 13.03 | 4900  | 0.6666          | 0.4532 |
| 0.5448        | 13.3  | 5000  | 0.6744          | 0.4542 |
| 0.5732        | 13.56 | 5100  | 0.6930          | 0.4986 |
| 0.5065        | 13.83 | 5200  | 0.6647          | 0.4351 |
| 0.4005        | 14.1  | 5300  | 0.6659          | 0.4508 |
| 0.4256        | 14.36 | 5400  | 0.6682          | 0.4533 |
| 0.4459        | 14.63 | 5500  | 0.6594          | 0.4326 |
| 0.4645        | 14.89 | 5600  | 0.6615          | 0.4287 |
| 0.4275        | 15.16 | 5700  | 0.6423          | 0.4299 |
| 0.4026        | 15.43 | 5800  | 0.6539          | 0.4217 |
| 0.3507        | 15.69 | 5900  | 0.6555          | 0.4299 |
| 0.3998        | 15.96 | 6000  | 0.6526          | 0.4213 |
| 0.4462        | 16.22 | 6100  | 0.6469          | 0.4230 |
| 0.4095        | 16.49 | 6200  | 0.6516          | 0.4210 |
| 0.4452        | 16.76 | 6300  | 0.6373          | 0.4133 |
| 0.3997        | 17.02 | 6400  | 0.6456          | 0.4211 |
| 0.3826        | 17.29 | 6500  | 0.6278          | 0.4042 |
| 0.3867        | 17.55 | 6600  | 0.6459          | 0.4112 |
| 0.4367        | 17.82 | 6700  | 0.6464          | 0.4131 |
| 0.3887        | 18.09 | 6800  | 0.6567          | 0.4150 |
| 0.3481        | 18.35 | 6900  | 0.6548          | 0.4145 |
| 0.4241        | 18.62 | 7000  | 0.6490          | 0.4123 |
| 0.3742        | 18.88 | 7100  | 0.6561          | 0.4135 |
| 0.423         | 19.15 | 7200  | 0.6498          | 0.4051 |
| 0.3803        | 19.41 | 7300  | 0.6475          | 0.3903 |
| 0.3084        | 19.68 | 7400  | 0.6403          | 0.4042 |
| 0.3012        | 19.95 | 7500  | 0.6460          | 0.4004 |
| 0.3306        | 20.21 | 7600  | 0.6491          | 0.3837 |
| 0.3612        | 20.48 | 7700  | 0.6752          | 0.3884 |
| 0.3572        | 20.74 | 7800  | 0.6383          | 0.3793 |
| 0.3638        | 21.01 | 7900  | 0.6349          | 0.3838 |
| 0.3658        | 21.28 | 8000  | 0.6544          | 0.3793 |
| 0.3726        | 21.54 | 8100  | 0.6567          | 0.3756 |
| 0.3618        | 21.81 | 8200  | 0.6390          | 0.3795 |
| 0.3212        | 22.07 | 8300  | 0.6359          | 0.3768 |
| 0.3561        | 22.34 | 8400  | 0.6452          | 0.3732 |
| 0.3231        | 22.61 | 8500  | 0.6416          | 0.3731 |
| 0.3764        | 22.87 | 8600  | 0.6428          | 0.3697 |
| 0.4142        | 23.14 | 8700  | 0.6415          | 0.3665 |
| 0.2713        | 23.4  | 8800  | 0.6541          | 0.3676 |
| 0.2277        | 23.67 | 8900  | 0.6492          | 0.3684 |
| 0.3849        | 23.94 | 9000  | 0.6448          | 0.3651 |
| 0.266         | 24.2  | 9100  | 0.6602          | 0.3643 |
| 0.3464        | 24.47 | 9200  | 0.6673          | 0.3607 |
| 0.2919        | 24.73 | 9300  | 0.6557          | 0.3677 |
| 0.2878        | 25.0  | 9400  | 0.6377          | 0.3653 |
| 0.1603        | 25.27 | 9500  | 0.6598          | 0.3700 |
| 0.2055        | 25.53 | 9600  | 0.6558          | 0.3614 |
| 0.1508        | 25.8  | 9700  | 0.6543          | 0.3605 |
| 0.3162        | 26.06 | 9800  | 0.6570          | 0.3576 |
| 0.2613        | 26.33 | 9900  | 0.6604          | 0.3584 |
| 0.2244        | 26.6  | 10000 | 0.6618          | 0.3634 |
| 0.1585        | 26.86 | 10100 | 0.6698          | 0.3634 |
| 0.2959        | 27.13 | 10200 | 0.6709          | 0.3593 |
| 0.2778        | 27.39 | 10300 | 0.6638          | 0.3537 |
| 0.2354        | 27.66 | 10400 | 0.6770          | 0.3585 |
| 0.2992        | 27.93 | 10500 | 0.6698          | 0.3506 |
| 0.2664        | 28.19 | 10600 | 0.6725          | 0.3533 |
| 0.2582        | 28.46 | 10700 | 0.6689          | 0.3542 |
| 0.2096        | 28.72 | 10800 | 0.6731          | 0.3527 |
| 0.4169        | 28.99 | 10900 | 0.6691          | 0.3521 |
| 0.2716        | 29.26 | 11000 | 0.6712          | 0.3517 |
| 0.2944        | 29.52 | 11100 | 0.6708          | 0.3509 |
| 0.2737        | 29.79 | 11200 | 0.6699          | 0.3491 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.9.1+cu102
- Datasets 2.0.0
- Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["automatic-speech-recognition", "/workspace/datasets/datasets/MIR_ST500/MIR_ST500.py", "generated_from_trainer"], "datasets": ["mir_st500"], "model-index": [{"name": "wav2vec2-base-MIR_ST500_ASR_109", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition /workspace/datasets/datasets/MIR_ST500/MIR_ST500.py generated_from_trainer dataset:mir_st500 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #/workspace/datasets/datasets/MIR_ST500/MIR_ST500.py #generated_from_trainer #dataset-mir_st500 #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-base-MIR\_ST500\_ASR\_109
==================================


This model is a fine-tuned version of facebook/wav2vec2-base on the /WORKSPACE/DATASETS/DATASETS/MIR\_ST500/MIR\_ST500.PY - ASR dataset.
It achieves the following results on the evaluation set:


* Loss: 0.6452
* Wer: 0.3732


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: 3e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 16
* 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 4.18.0
* Pytorch 1.9.1+cu102
* Datasets 2.0.0
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 16
* 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 4.18.0
* Pytorch 1.9.1+cu102
* Datasets 2.0.0
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #/workspace/datasets/datasets/MIR_ST500/MIR_ST500.py #generated_from_trainer #dataset-mir_st500 #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 16
* 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 4.18.0
* Pytorch 1.9.1+cu102
* Datasets 2.0.0
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #/workspace/datasets/datasets/MIR_ST500/MIR_ST500.py #generated_from_trainer #dataset-mir_st500 #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 2
* total\_train\_batch\_size: 16
* 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 4.18.0
* Pytorch 1.9.1+cu102
* Datasets 2.0.0
* Tokenizers 0.11.6