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# wav2vec2-xls-r-300m-Russian-small

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: 0.3514
- Wer: 0.4838

## 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: 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: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.512         | 1.32  | 400  | 3.2207          | 1.0    |
| 3.1562        | 2.65  | 800  | 3.0166          | 1.0    |
| 1.5211        | 3.97  | 1200 | 0.7134          | 0.8275 |
| 0.6724        | 5.3   | 1600 | 0.4713          | 0.6402 |
| 0.4693        | 6.62  | 2000 | 0.3904          | 0.5668 |
| 0.3693        | 7.95  | 2400 | 0.3609          | 0.5121 |
| 0.3004        | 9.27  | 2800 | 0.3514          | 0.4838 |


### Framework versions

- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3

{"language": ["ru"], "license": "apache-2.0", "tags": ["generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-Russian-small", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice ru", "type": "common_voice", "args": "ru"}, "metrics": [{"type": "wer", "value": 48.38, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "ru"}, "metrics": [{"type": "wer", "value": 58.25, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "ru"}, "metrics": [{"type": "wer", "value": 56.83, "name": "Test WER"}]}]}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer hf-asr-leaderboard robust-speech-event ru dataset:common_voice license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #ru #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us 

wav2vec2-xls-r-300m-Russian-small
=================================


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: 0.3514
* Wer: 0.4838


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: 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: 500
* num\_epochs: 10
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* 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: 500
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #ru #dataset-common_voice #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: 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: 500
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #ru #dataset-common_voice #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: 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: 500
* num\_epochs: 10
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3