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# wav2vec2-xls-r-1b-portuguese-CORAA-3

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on [CORAA dataset](https://github.com/nilc-nlp/CORAA).
It achieves the following results on the evaluation set:
- Loss: 1.0029
- Wer: 0.6020

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- training_steps: 30000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.0169        | 0.21  | 5000  | 1.9582          | 0.9283 |
| 1.8561        | 0.42  | 10000 | 1.6144          | 0.8554 |
| 1.6823        | 0.63  | 15000 | 1.4165          | 0.7710 |
| 1.52          | 0.84  | 20000 | 1.2441          | 0.7289 |
| 1.3757        | 1.05  | 25000 | 1.1061          | 0.6491 |
| 1.2377        | 1.26  | 30000 | 1.0029          | 0.6020 |


### Framework versions

- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3.dev0
- Tokenizers 0.11.0

{"language": ["pt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "pt", "robust-speech-event", "hf-asr-leaderboard"], "model-index": [{"name": "wav2vec2-xls-r-1b-portuguese-CORAA-3", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "pt"}, "metrics": [{"type": "wer", "value": 71.67, "name": "Test WER"}, {"type": "cer", "value": 30.64, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "pt"}, "metrics": [{"type": "wer", "value": 68.18, "name": "Test WER"}, {"type": "cer", "value": 28.34, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "sv"}, "metrics": [{"type": "wer", "value": 56.76, "name": "Test WER"}, {"type": "cer", "value": 23.7, "name": "Test CER"}]}]}]}
transformers pytorch wav2vec2 automatic-speech-recognition generated_from_trainer pt robust-speech-event hf-asr-leaderboard license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #pt #robust-speech-event #hf-asr-leaderboard #license-apache-2.0 #model-index #endpoints_compatible #region-us 

wav2vec2-xls-r-1b-portuguese-CORAA-3
====================================


This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on CORAA dataset.
It achieves the following results on the evaluation set:


* Loss: 1.0029
* Wer: 0.6020


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: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 5000
* training\_steps: 30000
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3.dev0
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 5000
* training\_steps: 30000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3.dev0
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #pt #robust-speech-event #hf-asr-leaderboard #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 5000
* training\_steps: 30000
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3.dev0
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #pt #robust-speech-event #hf-asr-leaderboard #license-apache-2.0 #model-index #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 5000
* training\_steps: 30000
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3.dev0
* Tokenizers 0.11.0