
<!-- 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. -->

# 

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-2b](https://huggingface.co/facebook/wav2vec2-xls-r-2b) on the OPENSLR_SLR66 - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4253
- Wer: 0.5109


### Evaluation metrics

| Metric | Split  | Decode with LM | Value     |
|:------:|:------:|:--------------:|:---------:|
| WER    | Train  | No             |      |
| CER    | Train  | No             |      |
| WER    | Test   | No             |      |
| CER    | Test   | No             |      |
| WER    | Train  | Yes            |      |
| CER    | Train  | Yes            |      |
| WER    | Test   | Yes            |      |
| CER    | Test   | Yes            |      |


## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 12
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- learning_rate: 3e-6
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 150.0
- hidden_dropout: 0.15
- mixed_precision_training: Native AMP

### Training results




### Framework versions

- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0

{"language": ["te"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "openslr_SLR66", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["openslr", "SLR66"], "metrics": ["wer"], "model-index": [{"name": "xls-r-1B-te", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Open SLR", "type": "openslr", "args": "SLR66"}, "metrics": [{"type": "wer", "value": 0.51, "name": "Test WER"}, {"type": "cer", "value": 0.097, "name": "Test CER"}]}]}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition openslr_SLR66 generated_from_trainer robust-speech-event hf-asr-leaderboard te dataset:openslr dataset:SLR66 license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #openslr_SLR66 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #te #dataset-openslr #dataset-SLR66 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 


This model is a fine-tuned version of facebook/wav2vec2-xls-r-2b on the OPENSLR\_SLR66 - NA dataset.
It achieves the following results on the evaluation set:


* Loss: 0.4253
* Wer: 0.5109


### Evaluation metrics



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: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 12
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* learning\_rate: 3e-6
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 150.0
* hidden\_dropout: 0.15
* mixed\_precision\_training: Native AMP


### Training results


### Framework versions


* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.17.1.dev0
* Tokenizers 0.11.0



### Evaluation metrics



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: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 12
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* learning\_rate: 3e-6
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 150.0
* hidden\_dropout: 0.15
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.17.1.dev0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #openslr_SLR66 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #te #dataset-openslr #dataset-SLR66 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
 ### Evaluation metrics



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: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 12
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* learning\_rate: 3e-6
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 150.0
* hidden\_dropout: 0.15
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.17.1.dev0
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #openslr_SLR66 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #te #dataset-openslr #dataset-SLR66 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
### Evaluation metrics



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: 2e-05
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* gradient\_accumulation\_steps: 12
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* learning\_rate: 3e-6
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 150.0
* hidden\_dropout: 0.15
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.17.1.dev0
* Tokenizers 0.11.0