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# wav2vec2-base-stac-msa-local

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: 2.0671
- Wer: 0.7924
- Cer: 0.3289

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data
Training: STAC + Tunisian MSA
Test: CS DATA

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|
| 1.4697        | 1.0   | 3773   | 1.8242          | 0.9395 | 0.5135 |
| 1.1644        | 2.0   | 7546   | 1.6306          | 0.8731 | 0.4446 |
| 0.9517        | 3.0   | 11319  | 1.4122          | 0.8587 | 0.4059 |
| 0.8563        | 4.0   | 15092  | 1.5409          | 0.8386 | 0.4034 |
| 0.7556        | 5.0   | 18865  | 1.4103          | 0.8247 | 0.3724 |
| 0.6841        | 6.0   | 22638  | 1.4608          | 0.8166 | 0.3735 |
| 0.5834        | 7.0   | 26411  | 1.5139          | 0.8113 | 0.3646 |
| 0.5607        | 8.0   | 30184  | 1.5303          | 0.8263 | 0.3797 |
| 0.5442        | 9.0   | 33957  | 1.3824          | 0.8198 | 0.3476 |
| 0.4584        | 10.0  | 37730  | 1.6412          | 0.8160 | 0.3576 |
| 0.4257        | 11.0  | 41503  | 1.5575          | 0.8003 | 0.3514 |
| 0.3631        | 12.0  | 45276  | 1.5776          | 0.8141 | 0.3454 |
| 0.3272        | 13.0  | 49049  | 1.5124          | 0.8127 | 0.3399 |
| 0.3348        | 14.0  | 52822  | 1.6733          | 0.7946 | 0.3398 |
| 0.3231        | 15.0  | 56595  | 1.5154          | 0.7987 | 0.3324 |
| 0.2556        | 16.0  | 60368  | 1.6161          | 0.7993 | 0.3402 |
| 0.238         | 17.0  | 64141  | 1.6126          | 0.7974 | 0.3329 |
| 0.2228        | 18.0  | 67914  | 1.7419          | 0.8014 | 0.3291 |
| 0.2129        | 19.0  | 71687  | 1.8394          | 0.8015 | 0.3374 |
| 0.1975        | 20.0  | 75460  | 1.9307          | 0.7928 | 0.3451 |
| 0.1981        | 21.0  | 79233  | 1.8700          | 0.8080 | 0.3375 |
| 0.1628        | 22.0  | 83006  | 1.9776          | 0.8061 | 0.3408 |
| 0.1462        | 23.0  | 86779  | 1.9090          | 0.8031 | 0.3306 |
| 0.1555        | 24.0  | 90552  | 1.9063          | 0.7878 | 0.3294 |
| 0.1515        | 25.0  | 94325  | 1.9632          | 0.7963 | 0.3278 |
| 0.1194        | 26.0  | 98098  | 1.9280          | 0.7991 | 0.3301 |
| 0.1219        | 27.0  | 101871 | 2.0248          | 0.7927 | 0.3329 |
| 0.1184        | 28.0  | 105644 | 2.0447          | 0.7903 | 0.3314 |
| 0.074         | 29.0  | 109417 | 2.0513          | 0.7910 | 0.3287 |
| 0.0836        | 30.0  | 113190 | 2.0671          | 0.7924 | 0.3289 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.8.1+cu102
- Datasets 1.18.3
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-stac-msa-local", "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-stac-msa-local
============================


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: 2.0671
* Wer: 0.7924
* Cer: 0.3289


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


Training and evaluation data
----------------------------


Training: STAC + Tunisian MSA
Test: CS DATA


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 2
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.8.1+cu102
* Datasets 1.18.3
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 1
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 2
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.8.1+cu102
* Datasets 1.18.3
* Tokenizers 0.12.1
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: 1
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 2
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30 ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.8.1+cu102
* Datasets 1.18.3
* Tokenizers 0.12.1
passage: 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: 1
* eval\_batch\_size: 1
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 2
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.8.1+cu102
* Datasets 1.18.3
* Tokenizers 0.12.1