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# wav2vec2-large-xls-r-1b-bemba-fds

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the [BembaSpeech](https://github.com/csikasote/BembaSpeech) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2898
- Wer: 0.3435

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.7986        | 0.34  | 500  | 0.4549          | 0.7292 |
| 0.5358        | 0.67  | 1000 | 0.3325          | 0.4491 |
| 0.4559        | 1.01  | 1500 | 0.3090          | 0.3954 |
| 0.3983        | 1.35  | 2000 | 0.3067          | 0.4105 |
| 0.4067        | 1.68  | 2500 | 0.2838          | 0.3678 |
| 0.3722        | 2.02  | 3000 | 0.2824          | 0.3762 |
| 0.3286        | 2.36  | 3500 | 0.2810          | 0.3670 |
| 0.3239        | 2.69  | 4000 | 0.2643          | 0.3501 |
| 0.3187        | 3.03  | 4500 | 0.2838          | 0.3754 |
| 0.2801        | 3.36  | 5000 | 0.2815          | 0.3507 |
| 0.2806        | 3.7   | 5500 | 0.2725          | 0.3486 |
| 0.2714        | 4.04  | 6000 | 0.2898          | 0.3435 |


### Framework versions

- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0

{"license": "apache-2.0", "tags": ["generated_from_trainer", "bem", "robust-speech-event"], "model-index": [{"name": "wav2vec2-large-xls-r-1b-bemba-fds", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer bem robust-speech-event license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #bem #robust-speech-event #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-large-xls-r-1b-bemba-fds
=================================


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


* Loss: 0.2898
* Wer: 0.3435


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: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #bem #robust-speech-event #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #bem #robust-speech-event #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0