Hugging Face's logo

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

Automatic Speech Recognition Transformers PyTorch
Georgian wav2vec2 mozilla-foundation/common_voice_7_0 generated_from_trainer robust-speech-event model_for_talk hf-asr-leaderboard Eval Results Inference Endpoints
Model card Files Community
1
wav2vec2-large-xls-r-300m-georgian / README.md
anton-l's picture
anton-l HF staff
Upload README.md
344bf82
preview code
|
raw history blame contribute delete
No virus
3.45 kB
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