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# wav2vec2-large-xls-r-300m-bashkir-cv7_opt

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - BA dataset.
It achieves the following results on the evaluation set:
- Training Loss: 0.268400
- Validation Loss: 0.088252
- WER without LM: 0.085588
- WER with LM: 0.04440795062008041
- CER with LM: 0.010491234992390509


## Model description

Trained with this [jupiter notebook](https://drive.google.com/file/d/1KohDXZtKBWXVPZYlsLtqfxJGBzKmTtSh/view?usp=sharing)



## Intended uses & limitations

In order to reduce the number of characters, the following letters have been replaced or removed:

- 'я' -> 'йа'
- 'ю' -> 'йу'
- 'ё' -> 'йо'
- 'е' -> 'йэ' for first letter
- 'е' -> 'э' for other cases
- 'ъ' -> deleted
- 'ь' -> deleted

Therefore, in order to get the correct text, you need to do the reverse transformation and use the language model.

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 50
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.16.1
- Pytorch 1.10.0+cu113
- Datasets 1.18.2
- Tokenizers 0.10.3

{"language": ["ba"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-bashkir-cv7_opt", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "ba"}, "metrics": [{"type": "wer", "value": 0.04440795062008041, "name": "Test WER"}, {"type": "cer", "value": 0.010491234992390509, "name": "Test CER"}]}]}]}
transformers pytorch safetensors wav2vec2 automatic-speech-recognition generated_from_trainer hf-asr-leaderboard mozilla-foundation/common_voice_7_0 robust-speech-event ba dataset:mozilla-foundation/common_voice_7_0 license:apache-2.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #ba #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 




# wav2vec2-large-xls-r-300m-bashkir-cv7_opt

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - BA dataset.
It achieves the following results on the evaluation set:
- Training Loss: 0.268400
- Validation Loss: 0.088252
- WER without LM: 0.085588
- WER with LM: 0.04440795062008041
- CER with LM: 0.010491234992390509


## Model description

Trained with this jupiter notebook



## Intended uses & limitations

In order to reduce the number of characters, the following letters have been replaced or removed:

- 'я' -> 'йа'
- 'ю' -> 'йу'
- 'ё' -> 'йо'
- 'е' -> 'йэ' for first letter
- 'е' -> 'э' for other cases
- 'ъ' -> deleted
- 'ь' -> deleted

Therefore, in order to get the correct text, you need to do the reverse transformation and use the language model.

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 50
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.16.1
- Pytorch 1.10.0+cu113
- Datasets 1.18.2
- Tokenizers 0.10.3

# wav2vec2-large-xls-r-300m-bashkir-cv7_opt

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - BA dataset.
It achieves the following results on the evaluation set:
- Training Loss: 0.268400
- Validation Loss: 0.088252
- WER without LM: 0.085588
- WER with LM: 0.04440795062008041
- CER with LM: 0.010491234992390509 ## Model description

Trained with this jupiter notebook ## Intended uses & limitations

In order to reduce the number of characters, the following letters have been replaced or removed:

- 'я' -> 'йа'
- 'ю' -> 'йу'
- 'ё' -> 'йо'
- 'е' -> 'йэ' for first letter
- 'е' -> 'э' for other cases
- 'ъ' -> deleted
- 'ь' -> deleted

Therefore, in order to get the correct text, you need to do the reverse transformation and use the language model. ## Training and evaluation data

More information needed ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 50
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.16.1
- Pytorch 1.10.0+cu113
- Datasets 1.18.2
- Tokenizers 0.10.3
TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #ba #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
 # wav2vec2-large-xls-r-300m-bashkir-cv7_opt

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - BA dataset.
It achieves the following results on the evaluation set:
- Training Loss: 0.268400
- Validation Loss: 0.088252
- WER without LM: 0.085588
- WER with LM: 0.04440795062008041
- CER with LM: 0.010491234992390509 ## Model description

Trained with this jupiter notebook ## Intended uses & limitations

In order to reduce the number of characters, the following letters have been replaced or removed:

- 'я' -> 'йа'
- 'ю' -> 'йу'
- 'ё' -> 'йо'
- 'е' -> 'йэ' for first letter
- 'е' -> 'э' for other cases
- 'ъ' -> deleted
- 'ь' -> deleted

Therefore, in order to get the correct text, you need to do the reverse transformation and use the language model. ## Training and evaluation data

More information needed ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 50
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.16.1
- Pytorch 1.10.0+cu113
- Datasets 1.18.2
- Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #ba #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us 
# wav2vec2-large-xls-r-300m-bashkir-cv7_opt

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - BA dataset.
It achieves the following results on the evaluation set:
- Training Loss: 0.268400
- Validation Loss: 0.088252
- WER without LM: 0.085588
- WER with LM: 0.04440795062008041
- CER with LM: 0.010491234992390509## Model description

Trained with this jupiter notebook## Intended uses & limitations

In order to reduce the number of characters, the following letters have been replaced or removed:

- 'я' -> 'йа'
- 'ю' -> 'йу'
- 'ё' -> 'йо'
- 'е' -> 'йэ' for first letter
- 'е' -> 'э' for other cases
- 'ъ' -> deleted
- 'ь' -> deleted

Therefore, in order to get the correct text, you need to do the reverse transformation and use the language model.## Training and evaluation data

More information needed## Training procedure