metadata

language:
  - ka
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - ka
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: sammy786/wav2vec2-xlsr-czech
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: ka
        metrics:
          - name: Test WER
            type: wer
            value: 23.9
          - name: Test CER
            type: cer
            value: 3.59
      - 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: 75.07
      - 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: 74.41

sammy786/wav2vec2-xlsr-georgian

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ka dataset. It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):

Loss: 10.54
Wer: 27.53

Model description

"facebook/wav2vec2-xls-r-1b" was finetuned.

Intended uses & limitations

More information needed

Training and evaluation data

Training data - Common voice Finnish train.tsv, dev.tsv and other.tsv

Training procedure

For creating the train dataset, all possible datasets were appended and 90-10 split was used.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.000045637994662983496
train_batch_size: 8
eval_batch_size: 16
seed: 13
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine_with_restarts
lr_scheduler_warmup_steps: 500
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Step	Training Loss	Validation Loss	Wer
200	4.152100	0.823672	0.967814
400	0.889500	0.196740	0.444792
600	0.493700	0.155659	0.366115
800	0.328000	0.138066	0.358069
1000	0.260600	0.119236	0.324989
1200	0.217200	0.114050	0.313366
1400	0.188800	0.112600	0.302190
1600	0.166900	0.111154	0.295485
1800	0.155500	0.109963	0.286544
2000	0.140400	0.107587	0.277604
2200	0.142600	0.105662	0.277157
2400	0.135400	0.105414	0.275369

Framework versions

Transformers 4.16.0.dev0
Pytorch 1.10.0+cu102
Datasets 1.17.1.dev0
Tokenizers 0.10.3

Evaluation Commands

To evaluate on mozilla-foundation/common_voice_8_0 with split test

python eval.py --model_id sammy786/wav2vec2-xlsr-georgian --dataset mozilla-foundation/common_voice_8_0 --config ka --split test