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# wav2vec2-100m-mls-german-ft

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-100m](https://huggingface.co/facebook/wav2vec2-xls-r-100m) on the MULTILINGUAL_LIBRISPEECH - GERMAN dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9325
- Wer: 1.0

## 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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 100.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 8.2135        | 14.29 | 500  | 8.4258          | 1.0 |
| 3.0031        | 28.57 | 1000 | 2.9839          | 1.0 |
| 2.9661        | 42.86 | 1500 | 2.9402          | 1.0 |
| 2.9584        | 57.14 | 2000 | 2.9354          | 1.0 |
| 2.936         | 71.43 | 2500 | 2.9341          | 1.0 |
| 2.9344        | 85.71 | 3000 | 2.9323          | 1.0 |
| 2.9674        | 100.0 | 3500 | 2.9325          | 1.0 |


### Framework versions

- Transformers 4.13.0.dev0
- Pytorch 1.9.0+cu111
- Datasets 1.15.2.dev0
- Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["automatic-speech-recognition", "multilingual_librispeech", "generated_from_trainer"], "datasets": ["multilingual_librispeech"], "model-index": [{"name": "wav2vec2-100m-mls-german-ft", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition multilingual_librispeech generated_from_trainer dataset:multilingual_librispeech license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #multilingual_librispeech #generated_from_trainer #dataset-multilingual_librispeech #license-apache-2.0 #endpoints_compatible #region-us 

wav2vec2-100m-mls-german-ft
===========================


This model is a fine-tuned version of facebook/wav2vec2-xls-r-100m on the MULTILINGUAL\_LIBRISPEECH - GERMAN dataset.
It achieves the following results on the evaluation set:


* Loss: 2.9325
* Wer: 1.0


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: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.13.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 1.15.2.dev0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.13.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 1.15.2.dev0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #multilingual_librispeech #generated_from_trainer #dataset-multilingual_librispeech #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.13.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 1.15.2.dev0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #multilingual_librispeech #generated_from_trainer #dataset-multilingual_librispeech #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* total\_train\_batch\_size: 64
* total\_eval\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.13.0.dev0
* Pytorch 1.9.0+cu111
* Datasets 1.15.2.dev0
* Tokenizers 0.10.3