
<!-- 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. -->

# wav2vec2-large-xls-r-300m-en-libri-more-steps

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7624
- Wer: 0.8772
- Cer: 0.3762

## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| No log        | 1.94  | 33   | 2.9987          | 1.0    | 1.0    |
| No log        | 3.88  | 66   | 2.8951          | 1.0    | 1.0    |
| No log        | 5.82  | 99   | 2.8732          | 1.0    | 1.0    |
| 3.781         | 7.76  | 132  | 2.6057          | 1.0    | 1.0    |
| 3.781         | 9.71  | 165  | 1.9015          | 1.0154 | 0.5616 |
| 3.781         | 11.65 | 198  | 1.5226          | 0.9263 | 0.4462 |
| 2.2258        | 13.59 | 231  | 1.5116          | 0.8913 | 0.3967 |
| 2.2258        | 15.53 | 264  | 1.5634          | 0.8922 | 0.3842 |
| 2.2258        | 17.47 | 297  | 1.7016          | 0.8876 | 0.3796 |
| 0.7946        | 19.41 | 330  | 1.7624          | 0.8772 | 0.3762 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0+cpu
- Datasets 1.18.3
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-en-libri-more-steps", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer dataset:librispeech_asr license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-large-xls-r-300m-en-libri-more-steps
=============================================


This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:


* Loss: 1.7624
* Wer: 0.8772
* Cer: 0.3762


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: 0.001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cpu
* Datasets 1.18.3
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cpu
* Datasets 1.18.3
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cpu
* Datasets 1.18.3
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.001
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.20.1
* Pytorch 1.11.0+cpu
* Datasets 1.18.3
* Tokenizers 0.12.1