metadata

language:
  - ba
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
  - robust-speech-event
datasets:
  - common_voice
model-index:
  - name: wav2vec2-large-xls-r-300m-bashkir-cv7_opt
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: ba
        metrics:
          - name: Test WER
            type: wer
            value: 0.04688827226909791

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: 0.04688827226909791
CER with LM: 0.01121871653232625

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