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# wav2vec2-large-xlsr-common1000asli-demo-colab-dd

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 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0671
- Wer: 0.5268

## 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: 128
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 10.4469       | 10.53  | 400   | 2.9884          | 0.9788 |
| 1.6358        | 21.05  | 800   | 0.5968          | 0.6578 |
| 0.3404        | 31.58  | 1200  | 0.5348          | 0.5757 |
| 0.2218        | 42.11  | 1600  | 0.5945          | 0.5659 |
| 0.1676        | 52.63  | 2000  | 0.6318          | 0.5753 |
| 0.1442        | 63.16  | 2400  | 0.6395          | 0.5559 |
| 0.1237        | 73.68  | 2800  | 0.6981          | 0.5522 |
| 0.1132        | 84.21  | 3200  | 0.7174          | 0.5428 |
| 0.1056        | 94.74  | 3600  | 0.7120          | 0.5531 |
| 0.0942        | 105.26 | 4000  | 0.7865          | 0.5529 |
| 0.0967        | 115.79 | 4400  | 0.7796          | 0.5546 |
| 0.0854        | 126.32 | 4800  | 0.7392          | 0.5507 |
| 0.0816        | 136.84 | 5200  | 0.8173          | 0.5575 |
| 0.0748        | 147.37 | 5600  | 0.8164          | 0.5550 |
| 0.0691        | 157.89 | 6000  | 0.8061          | 0.5444 |
| 0.0654        | 168.42 | 6400  | 0.8098          | 0.5524 |
| 0.0627        | 178.95 | 6800  | 0.8527          | 0.5655 |
| 0.0653        | 189.47 | 7200  | 0.8210          | 0.5412 |
| 0.0639        | 200.0  | 7600  | 0.8619          | 0.5602 |
| 0.0614        | 210.53 | 8000  | 0.8453          | 0.5606 |
| 0.0647        | 221.05 | 8400  | 0.8248          | 0.5564 |
| 0.0611        | 231.58 | 8800  | 0.8323          | 0.5637 |
| 0.0606        | 242.11 | 9200  | 0.8754          | 0.5654 |
| 0.0587        | 252.63 | 9600  | 0.8684          | 0.5528 |
| 0.0524        | 263.16 | 10000 | 0.8798          | 0.5556 |
| 0.0499        | 273.68 | 10400 | 0.8593          | 0.5553 |
| 0.0466        | 284.21 | 10800 | 0.9079          | 0.5520 |
| 0.0505        | 294.74 | 11200 | 0.8680          | 0.5607 |
| 0.0517        | 305.26 | 11600 | 0.8709          | 0.5557 |
| 0.0522        | 315.79 | 12000 | 0.8687          | 0.5570 |
| 0.0453        | 326.32 | 12400 | 0.8585          | 0.5614 |
| 0.047         | 336.84 | 12800 | 0.9249          | 0.5581 |
| 0.0431        | 347.37 | 13200 | 0.8934          | 0.5543 |
| 0.0454        | 357.89 | 13600 | 0.8837          | 0.5583 |
| 0.0472        | 368.42 | 14000 | 0.9070          | 0.5565 |
| 0.0431        | 378.95 | 14400 | 0.9202          | 0.5526 |
| 0.0404        | 389.47 | 14800 | 0.9234          | 0.5543 |
| 0.0386        | 400.0  | 15200 | 0.9056          | 0.5549 |
| 0.0372        | 410.53 | 15600 | 0.9901          | 0.5493 |
| 0.0376        | 421.05 | 16000 | 0.9109          | 0.5460 |
| 0.0365        | 431.58 | 16400 | 0.9313          | 0.5487 |
| 0.0347        | 442.11 | 16800 | 0.9027          | 0.5496 |
| 0.0361        | 452.63 | 17200 | 0.9614          | 0.5457 |
| 0.0323        | 463.16 | 17600 | 0.9782          | 0.5558 |
| 0.0325        | 473.68 | 18000 | 0.9549          | 0.5481 |
| 0.032         | 484.21 | 18400 | 0.9781          | 0.5431 |
| 0.0289        | 494.74 | 18800 | 0.9840          | 0.5463 |
| 0.0292        | 505.26 | 19200 | 0.9397          | 0.5357 |
| 0.0276        | 515.79 | 19600 | 0.9228          | 0.5467 |
| 0.0283        | 526.32 | 20000 | 0.9683          | 0.5394 |
| 0.0281        | 536.84 | 20400 | 0.9783          | 0.5479 |
| 0.026         | 547.37 | 20800 | 0.9663          | 0.5472 |
| 0.0288        | 557.89 | 21200 | 0.9424          | 0.5426 |
| 0.0275        | 568.42 | 21600 | 0.9788          | 0.5435 |
| 0.0264        | 578.95 | 22000 | 0.9703          | 0.5473 |
| 0.0259        | 589.47 | 22400 | 0.9994          | 0.5446 |
| 0.0243        | 600.0  | 22800 | 0.9637          | 0.5590 |
| 0.0251        | 610.53 | 23200 | 0.9577          | 0.5457 |
| 0.0222        | 621.05 | 23600 | 0.9780          | 0.5419 |
| 0.0227        | 631.58 | 24000 | 0.9582          | 0.5417 |
| 0.0222        | 642.11 | 24400 | 0.9847          | 0.5432 |
| 0.0214        | 652.63 | 24800 | 1.0171          | 0.5449 |
| 0.022         | 663.16 | 25200 | 0.9819          | 0.5430 |
| 0.0202        | 673.68 | 25600 | 0.9737          | 0.5413 |
| 0.0187        | 684.21 | 26000 | 0.9977          | 0.5440 |
| 0.0213        | 694.74 | 26400 | 0.9919          | 0.5464 |
| 0.0197        | 705.26 | 26800 | 0.9769          | 0.5357 |
| 0.0183        | 715.79 | 27200 | 0.9964          | 0.5377 |
| 0.0187        | 726.32 | 27600 | 0.9973          | 0.5341 |
| 0.0191        | 736.84 | 28000 | 0.9970          | 0.5399 |
| 0.0183        | 747.37 | 28400 | 1.0179          | 0.5371 |
| 0.0176        | 757.89 | 28800 | 1.0020          | 0.5440 |
| 0.018         | 768.42 | 29200 | 0.9992          | 0.5394 |
| 0.0157        | 778.95 | 29600 | 1.0502          | 0.5397 |
| 0.0165        | 789.47 | 30000 | 1.0463          | 0.5397 |
| 0.0147        | 800.0  | 30400 | 1.0363          | 0.5430 |
| 0.0153        | 810.53 | 30800 | 0.9890          | 0.5407 |
| 0.0145        | 821.05 | 31200 | 1.0139          | 0.5369 |
| 0.0143        | 831.58 | 31600 | 1.0260          | 0.5346 |
| 0.0141        | 842.11 | 32000 | 1.0277          | 0.5361 |
| 0.0139        | 852.63 | 32400 | 1.0639          | 0.5335 |
| 0.0132        | 863.16 | 32800 | 1.0661          | 0.5314 |
| 0.013         | 873.68 | 33200 | 1.0537          | 0.5335 |
| 0.0126        | 884.21 | 33600 | 1.0433          | 0.5347 |
| 0.0121        | 894.74 | 34000 | 1.0275          | 0.5326 |
| 0.0128        | 905.26 | 34400 | 1.0405          | 0.5327 |
| 0.0112        | 915.79 | 34800 | 1.0626          | 0.5296 |
| 0.0115        | 926.32 | 35200 | 1.0583          | 0.5284 |
| 0.0109        | 936.84 | 35600 | 1.0494          | 0.5287 |
| 0.0113        | 947.37 | 36000 | 1.0655          | 0.5294 |
| 0.0104        | 957.89 | 36400 | 1.0723          | 0.5269 |
| 0.0108        | 968.42 | 36800 | 1.0680          | 0.5267 |
| 0.0104        | 978.95 | 37200 | 1.0707          | 0.5261 |
| 0.0108        | 989.47 | 37600 | 1.0649          | 0.5268 |
| 0.0103        | 1000.0 | 38000 | 1.0671          | 0.5268 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-common1000asli-demo-colab-dd", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer dataset:common_voice license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-large-xlsr-common1000asli-demo-colab-dd
================================================


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


* Loss: 1.0671
* Wer: 0.5268


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: 128
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu102
* Datasets 1.13.3
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 128
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu102
* Datasets 1.13.3
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 128
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu102
* Datasets 1.13.3
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 128
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1000
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu102
* Datasets 1.13.3
* Tokenizers 0.10.3