README.md

metadata

language:
  - ru
license: apache-2.0
tags:
  - generated_from_trainer
  - hf-asr-leaderboard
  - robust-speech-event
datasets:
  - common_voice
base_model: facebook/wav2vec2-xls-r-300m
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

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

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