metadata

language:
  - ka
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
  - ka
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: XLS-R-300M - Georgian
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: ka
        metrics:
          - name: Test WER
            type: wer
            value: 42.09
          - name: Test CER
            type: cer
            value: 8.01
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: ka
        metrics:
          - name: Test WER
            type: wer
            value: 65.32
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: ka
        metrics:
          - name: Test WER
            type: wer
            value: 65.03

wav2vec2-large-xls-r-300m-georgian

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - KA dataset. It achieves the following results on the evaluation set:

Loss: 0.3666
Wer: 0.4211

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 100.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
1.8805	5.95	500	0.7547	0.8438
1.2123	11.9	1000	0.4732	0.6542
1.0822	17.86	1500	0.4027	0.5778
0.9938	23.81	2000	0.3847	0.5524
0.9383	29.76	2500	0.3845	0.5204
0.8932	35.71	3000	0.3833	0.5297
0.8495	41.67	3500	0.3759	0.5036
0.8201	47.62	4000	0.3616	0.4859
0.7794	53.57	4500	0.3874	0.4938
0.735	59.52	5000	0.3748	0.4782
0.7082	65.48	5500	0.3615	0.4675
0.669	71.43	6000	0.3797	0.4601
0.6457	77.38	6500	0.3812	0.4515
0.6098	83.33	7000	0.3660	0.4343
0.5874	89.29	7500	0.3640	0.4257
0.5627	95.24	8000	0.3661	0.4239

Framework versions

Transformers 4.16.0.dev0
Pytorch 1.10.1+cu102
Datasets 1.17.1.dev0
Tokenizers 0.11.0