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xls-r-uzbek-cv8

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Uzbek
wav2vec2
Generated from Trainer
hf-asr-leaderboard
mozilla-foundation/common_voice_8_0
robust-speech-event
Eval Results
Inference Endpoints
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xls-r-uzbek-cv8 / README.md
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metadata
language:
  - uz
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: xls-r-uzbek-cv8
    results: []

xls-r-uzbek-cv8

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - UZ dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3063
  • Wer: 0.3852
  • Cer: 0.0777

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: 3e-05
  • train_batch_size: 32
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • 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 Cer
3.1401 3.25 500 3.1146 1.0 1.0
2.7484 6.49 1000 2.2842 1.0065 0.7069
1.0899 9.74 1500 0.5414 0.6125 0.1351
0.9465 12.99 2000 0.4566 0.5635 0.1223
0.8771 16.23 2500 0.4212 0.5366 0.1161
0.8346 19.48 3000 0.3994 0.5144 0.1102
0.8127 22.73 3500 0.3819 0.4944 0.1051
0.7833 25.97 4000 0.3705 0.4798 0.1011
0.7603 29.22 4500 0.3661 0.4704 0.0992
0.7424 32.47 5000 0.3529 0.4577 0.0957
0.7251 35.71 5500 0.3410 0.4473 0.0928
0.7106 38.96 6000 0.3401 0.4428 0.0919
0.7027 42.21 6500 0.3355 0.4353 0.0905
0.6927 45.45 7000 0.3308 0.4296 0.0885
0.6828 48.7 7500 0.3246 0.4204 0.0863
0.6706 51.95 8000 0.3250 0.4233 0.0868
0.6629 55.19 8500 0.3264 0.4159 0.0849
0.6556 58.44 9000 0.3213 0.4100 0.0835
0.6484 61.69 9500 0.3182 0.4124 0.0837
0.6407 64.93 10000 0.3171 0.4050 0.0825
0.6375 68.18 10500 0.3150 0.4039 0.0822
0.6363 71.43 11000 0.3129 0.3991 0.0810
0.6307 74.67 11500 0.3114 0.3986 0.0807
0.6232 77.92 12000 0.3103 0.3895 0.0790
0.6216 81.17 12500 0.3086 0.3891 0.0790
0.6174 84.41 13000 0.3082 0.3881 0.0785
0.6196 87.66 13500 0.3059 0.3875 0.0782
0.6174 90.91 14000 0.3084 0.3862 0.0780
0.6169 94.16 14500 0.3070 0.3860 0.0779
0.6166 97.4 15000 0.3066 0.3855 0.0778

Framework versions

  • Transformers 4.16.2
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.3
  • Tokenizers 0.11.0