pytorch tensorboard wav2vec2 automatic-speech-recognition dataset:mir_st500 transformers /workspace/datasets/datasets/MIR_ST500/MIR_ST500.py generated_from_trainer license:apache-2.0 model-index
---
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-large-xlsr-53-MIR_ST500_ASR
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-large-xlsr-53-MIR_ST500_ASR

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the /WORKSPACE/DATASETS/DATASETS/MIR_ST500/MIR_ST500.PY - ASR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5180
- Wer: 0.5824

## 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: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 8
- 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: 15.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 56.764        | 0.13  | 100   | 24.4254         | 0.9990 |
| 7.5081        | 0.27  | 200   | 2.9111          | 1.0    |
| 3.4899        | 0.4   | 300   | 2.1361          | 1.0    |
| 2.4094        | 0.53  | 400   | 1.9088          | 1.0    |
| 2.6764        | 0.67  | 500   | 1.8543          | 1.0    |
| 3.3107        | 0.8   | 600   | 1.7979          | 1.0    |
| 2.2856        | 0.93  | 700   | 1.7571          | 1.0    |
| 1.856         | 1.07  | 800   | 1.7351          | 0.9648 |
| 1.8882        | 1.2   | 900   | 1.7181          | 0.9654 |
| 2.1731        | 1.33  | 1000  | 1.6736          | 0.9637 |
| 1.8252        | 1.46  | 1100  | 1.3468          | 0.9647 |
| 1.9092        | 1.6   | 1200  | 1.3302          | 0.9627 |
| 1.9435        | 1.73  | 1300  | 1.2428          | 0.9634 |
| 1.3027        | 1.86  | 1400  | 1.2133          | 0.9644 |
| 1.3438        | 2.0   | 1500  | 1.2002          | 0.9635 |
| 1.2161        | 2.13  | 1600  | 1.1901          | 0.9636 |
| 1.203         | 2.26  | 1700  | 1.1620          | 0.9616 |
| 1.1159        | 2.4   | 1800  | 1.1660          | 0.9598 |
| 1.1466        | 2.53  | 1900  | 1.2089          | 0.9605 |
| 1.0563        | 2.66  | 2000  | 1.1732          | 0.9603 |
| 1.1019        | 2.8   | 2100  | 1.1468          | 0.9612 |
| 1.029         | 2.93  | 2200  | 1.1188          | 0.9622 |
| 1.0079        | 3.06  | 2300  | 1.0604          | 0.9617 |
| 1.0483        | 3.2   | 2400  | 1.0499          | 0.9612 |
| 0.9892        | 3.33  | 2500  | 1.0292          | 0.9606 |
| 0.9556        | 3.46  | 2600  | 1.0228          | 0.9604 |
| 0.9626        | 3.6   | 2700  | 1.0028          | 0.9617 |
| 1.0537        | 3.73  | 2800  | 1.0051          | 0.9608 |
| 1.0648        | 3.86  | 2900  | 0.9723          | 0.9604 |
| 0.8657        | 3.99  | 3000  | 0.9620          | 0.9605 |
| 0.8964        | 4.13  | 3100  | 1.0432          | 0.9612 |
| 0.9639        | 4.26  | 3200  | 0.9322          | 0.9589 |
| 0.8965        | 4.39  | 3300  | 0.9091          | 0.9559 |
| 0.8257        | 4.53  | 3400  | 0.8999          | 0.9499 |
| 0.8002        | 4.66  | 3500  | 0.8754          | 0.9554 |
| 0.7335        | 4.79  | 3600  | 0.8608          | 0.9572 |
| 0.936         | 4.93  | 3700  | 0.8564          | 0.9510 |
| 0.8185        | 5.06  | 3800  | 0.8890          | 0.9517 |
| 0.7422        | 5.19  | 3900  | 0.8262          | 0.9392 |
| 0.7784        | 5.33  | 4000  | 0.8292          | 0.9259 |
| 0.8123        | 5.46  | 4100  | 0.8180          | 0.9374 |
| 0.7256        | 5.59  | 4200  | 0.8158          | 0.9077 |
| 0.7638        | 5.73  | 4300  | 0.8423          | 0.9170 |
| 0.6737        | 5.86  | 4400  | 0.7818          | 0.9182 |
| 0.7644        | 5.99  | 4500  | 0.7692          | 0.8934 |
| 0.7911        | 6.13  | 4600  | 0.7627          | 0.8978 |
| 0.6922        | 6.26  | 4700  | 0.7627          | 0.8906 |
| 0.7369        | 6.39  | 4800  | 0.7570          | 0.8838 |
| 0.6642        | 6.52  | 4900  | 0.9476          | 0.8953 |
| 0.7502        | 6.66  | 5000  | 0.7336          | 0.8955 |
| 0.6243        | 6.79  | 5100  | 0.7583          | 0.8896 |
| 0.6912        | 6.92  | 5200  | 0.7764          | 0.8761 |
| 0.7744        | 7.06  | 5300  | 0.7615          | 0.8790 |
| 0.6195        | 7.19  | 5400  | 0.7114          | 0.8712 |
| 0.7418        | 7.32  | 5500  | 0.8314          | 0.8864 |
| 0.7658        | 7.46  | 5600  | 0.8531          | 0.8718 |
| 0.6821        | 7.59  | 5700  | 0.9068          | 0.8786 |
| 0.6931        | 7.72  | 5800  | 0.7549          | 0.8645 |
| 0.6771        | 7.86  | 5900  | 0.7138          | 0.8442 |
| 0.6735        | 7.99  | 6000  | 0.6947          | 0.8493 |
| 0.6427        | 8.12  | 6100  | 0.6997          | 0.8475 |
| 0.6988        | 8.26  | 6200  | 0.6814          | 0.8098 |
| 0.6726        | 8.39  | 6300  | 0.6656          | 0.8259 |
| 0.6247        | 8.52  | 6400  | 0.6438          | 0.8314 |
| 0.5101        | 8.66  | 6500  | 0.6323          | 0.8446 |
| 0.5325        | 8.79  | 6600  | 0.6305          | 0.8413 |
| 0.5918        | 8.92  | 6700  | 0.6353          | 0.8076 |
| 0.617         | 9.05  | 6800  | 0.6544          | 0.8118 |
| 0.4861        | 9.19  | 6900  | 0.6174          | 0.8429 |
| 0.6396        | 9.32  | 7000  | 0.6140          | 0.8117 |
| 0.436         | 9.45  | 7100  | 0.6148          | 0.7887 |
| 0.6141        | 9.59  | 7200  | 0.6133          | 0.8007 |
| 0.5781        | 9.72  | 7300  | 0.6135          | 0.8211 |
| 0.52          | 9.85  | 7400  | 0.6155          | 0.8042 |
| 0.6681        | 9.99  | 7500  | 0.6074          | 0.7843 |
| 0.5004        | 10.12 | 7600  | 0.5950          | 0.8035 |
| 0.4993        | 10.25 | 7700  | 0.5888          | 0.7710 |
| 0.4768        | 10.39 | 7800  | 0.5922          | 0.7633 |
| 0.4535        | 10.52 | 7900  | 0.5906          | 0.8030 |
| 0.517         | 10.65 | 8000  | 0.5875          | 0.7823 |
| 0.5894        | 10.79 | 8100  | 0.5882          | 0.7932 |
| 0.6005        | 10.92 | 8200  | 0.5798          | 0.7922 |
| 0.4284        | 11.05 | 8300  | 0.5775          | 0.7701 |
| 0.5163        | 11.19 | 8400  | 0.5715          | 0.7592 |
| 0.4701        | 11.32 | 8500  | 0.5955          | 0.7485 |
| 0.5152        | 11.45 | 8600  | 0.6041          | 0.6914 |
| 0.4442        | 11.58 | 8700  | 0.5614          | 0.7439 |
| 0.4451        | 11.72 | 8800  | 0.5619          | 0.7033 |
| 0.4433        | 11.85 | 8900  | 0.5562          | 0.7246 |
| 0.4799        | 11.98 | 9000  | 0.5834          | 0.7040 |
| 0.4832        | 12.12 | 9100  | 0.5902          | 0.7349 |
| 0.523         | 12.25 | 9200  | 0.5562          | 0.7326 |
| 0.4419        | 12.38 | 9300  | 0.5472          | 0.7326 |
| 0.437         | 12.52 | 9400  | 0.5466          | 0.7100 |
| 0.4797        | 12.65 | 9500  | 0.5470          | 0.6698 |
| 0.3971        | 12.78 | 9600  | 0.5437          | 0.6835 |
| 0.5254        | 12.92 | 9700  | 0.5385          | 0.6747 |
| 0.5046        | 13.05 | 9800  | 0.5330          | 0.6554 |
| 0.4692        | 13.18 | 9900  | 0.5305          | 0.6527 |
| 0.4305        | 13.32 | 10000 | 0.5292          | 0.6314 |
| 0.6132        | 13.45 | 10100 | 0.5405          | 0.6028 |
| 0.4741        | 13.58 | 10200 | 0.5311          | 0.6207 |
| 0.398         | 13.72 | 10300 | 0.5320          | 0.6261 |
| 0.458         | 13.85 | 10400 | 0.5240          | 0.6242 |
| 0.4154        | 13.98 | 10500 | 0.5262          | 0.6215 |
| 0.3702        | 14.11 | 10600 | 0.5206          | 0.6136 |
| 0.427         | 14.25 | 10700 | 0.5231          | 0.6289 |
| 0.4307        | 14.38 | 10800 | 0.5210          | 0.5908 |
| 0.4738        | 14.51 | 10900 | 0.5211          | 0.5826 |
| 0.5522        | 14.65 | 11000 | 0.5193          | 0.5886 |
| 0.4717        | 14.78 | 11100 | 0.5194          | 0.5907 |
| 0.4819        | 14.91 | 11200 | 0.5178          | 0.5870 |


### Framework versions

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