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DrishtiSharma
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wav2vec2-large-xls-r-300m-as-g1

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Assamese
wav2vec2
mozilla-foundation/common_voice_8_0
Generated from Trainer
robust-speech-event
model_for_talk
hf-asr-leaderboard
Eval Results
Inference Endpoints
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wav2vec2-large-xls-r-300m-as-g1 / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: wav2vec2-large-xls-r-300m-as-g1
    results: []

wav2vec2-large-xls-r-300m-as-g1

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: 1.3327
  • Wer: 0.5744

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: 1000
  • num_epochs: 200
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
14.1958 5.26 100 7.1919 1.0
5.0035 10.51 200 3.9362 1.0
3.6193 15.77 300 3.4451 1.0
3.4852 21.05 400 3.3536 1.0
2.8489 26.31 500 1.6451 0.9100
0.9568 31.56 600 1.0514 0.7561
0.4865 36.82 700 1.0434 0.7184
0.322 42.1 800 1.0825 0.7210
0.2383 47.36 900 1.1304 0.6897
0.2136 52.62 1000 1.1150 0.6854
0.179 57.87 1100 1.2453 0.6875
0.1539 63.15 1200 1.2211 0.6704
0.1303 68.41 1300 1.2859 0.6747
0.1183 73.67 1400 1.2775 0.6721
0.0994 78.92 1500 1.2321 0.6404
0.0991 84.21 1600 1.2766 0.6524
0.0887 89.46 1700 1.3026 0.6344
0.0754 94.72 1800 1.3199 0.6704
0.0693 99.97 1900 1.3044 0.6361
0.0568 105.26 2000 1.3541 0.6254
0.0536 110.51 2100 1.3320 0.6249
0.0529 115.77 2200 1.3370 0.6271
0.048 121.05 2300 1.2757 0.6031
0.0419 126.31 2400 1.2661 0.6172
0.0349 131.56 2500 1.2897 0.6048
0.0309 136.82 2600 1.2688 0.5962
0.0278 142.1 2700 1.2885 0.5954
0.0254 147.36 2800 1.2988 0.5915
0.0223 152.62 2900 1.3153 0.5941
0.0216 157.87 3000 1.2936 0.5937
0.0186 163.15 3100 1.2906 0.5877
0.0156 168.41 3200 1.3476 0.5962
0.0158 173.67 3300 1.3363 0.5847
0.0142 178.92 3400 1.3367 0.5847
0.0153 184.21 3500 1.3105 0.5757
0.0119 189.46 3600 1.3255 0.5705
0.0115 194.72 3700 1.3340 0.5787
0.0103 199.97 3800 1.3327 0.5744

Framework versions

  • Transformers 4.16.2
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.3
  • Tokenizers 0.11.0