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### Note: Files are missing. Probably, didn't get (git)pushed properly.  :(

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1679
- Wer: 0.5761

## 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: 0.000111
- 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: 300
- num_epochs: 200
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 8.3852        | 10.51  | 200  | 3.6402          | 1.0    |
| 3.5374        | 21.05  | 400  | 3.3894          | 1.0    |
| 2.8645        | 31.56  | 600  | 1.3143          | 0.8303 |
| 1.1784        | 42.1   | 800  | 0.9417          | 0.6661 |
| 0.7805        | 52.62  | 1000 | 0.9292          | 0.6237 |
| 0.5973        | 63.15  | 1200 | 0.9489          | 0.6014 |
| 0.4784        | 73.67  | 1400 | 0.9916          | 0.5962 |
| 0.4138        | 84.21  | 1600 | 1.0272          | 0.6121 |
| 0.3491        | 94.72  | 1800 | 1.0412          | 0.5984 |
| 0.3062        | 105.26 | 2000 | 1.0769          | 0.6005 |
| 0.2707        | 115.77 | 2200 | 1.0708          | 0.5752 |
| 0.2459        | 126.31 | 2400 | 1.1285          | 0.6009 |
| 0.2234        | 136.82 | 2600 | 1.1209          | 0.5949 |
| 0.2035        | 147.36 | 2800 | 1.1348          | 0.5842 |
| 0.1876        | 157.87 | 3000 | 1.1480          | 0.5872 |
| 0.1669        | 168.41 | 3200 | 1.1496          | 0.5838 |
| 0.1595        | 178.92 | 3400 | 1.1721          | 0.5778 |
| 0.1505        | 189.46 | 3600 | 1.1654          | 0.5744 |
| 0.1486        | 199.97 | 3800 | 1.1679          | 0.5761 |


### Framework versions

- Transformers 4.16.1
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0

{"language": ["as"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "as", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-as-with-LM-v2", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "hsb"}, "metrics": [{"type": "wer", "value": [], "name": "Test WER"}, {"type": "cer", "value": [], "name": "Test CER"}]}]}]}
automatic-speech-recognition mozilla-foundation/common_voice_8_0 generated_from_trainer as robust-speech-event model_for_talk hf-asr-leaderboard dataset:common_voice license:apache-2.0 model-index region:us
TAGS
#automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #as #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-common_voice #license-apache-2.0 #model-index #region-us 

### Note: Files are missing. Probably, didn't get (git)pushed properly. :(


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


* Loss: 1.1679
* Wer: 0.5761


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: 0.000111
* 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: 300
* num\_epochs: 200
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.16.1
* Pytorch 1.10.0+cu111
* Datasets 1.18.2
* Tokenizers 0.11.0



### Note: Files are missing. Probably, didn't get (git)pushed properly. :(


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


* Loss: 1.1679
* Wer: 0.5761


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: 0.000111
* 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: 300
* num\_epochs: 200
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.1
* Pytorch 1.10.0+cu111
* Datasets 1.18.2
* Tokenizers 0.11.0
TAGS
#automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #as #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-common_voice #license-apache-2.0 #model-index #region-us 
 ### Note: Files are missing. Probably, didn't get (git)pushed properly. :(


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


* Loss: 1.1679
* Wer: 0.5761


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: 0.000111
* 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: 300
* num\_epochs: 200
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.16.1
* Pytorch 1.10.0+cu111
* Datasets 1.18.2
* Tokenizers 0.11.0
passage: TAGS
#automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #as #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-common_voice #license-apache-2.0 #model-index #region-us 
### Note: Files are missing. Probably, didn't get (git)pushed properly. :(


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


* Loss: 1.1679
* Wer: 0.5761


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: 0.000111
* 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: 300
* num\_epochs: 200
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.16.1
* Pytorch 1.10.0+cu111
* Datasets 1.18.2
* Tokenizers 0.11.0