Wav2vec2 Large Xls R 300m Bashkir Cv7 Opt

🚀 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, which is used for automatic speech recognition.

🚀 Quick Start

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

✨ Features

• Fine - Tuned Model: Based on facebook/wav2vec2-xls-r-300m, fine - tuned on the MOZILLA - FOUNDATION/COMMON_VOICE_7_0 - BA dataset.
• Good Performance: Achieves low WER and CER on the evaluation set.

📚 Documentation

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

📄 License

This model is licensed under the Apache - 2.0 license.