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: 9.2451

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: 0.307100
Validation Loss: 0.118680
Wer: 0.092451

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