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xls-r-300-cv17-upper-sorbian

Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
Eval Results
Inference Endpoints
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xls-r-300-cv17-upper-sorbian / README.md
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metadata
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
datasets:
  - common_voice_17_0
metrics:
  - wer
model-index:
  - name: xls-r-300-cv17-upper-sorbian
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_17_0
          type: common_voice_17_0
          config: hsb
          split: validation
          args: hsb
        metrics:
          - name: Wer
            type: wer
            value: 0

Visualize in Weights & Biases

xls-r-300-cv17-upper-sorbian

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0001
  • Wer: 0.0
  • Cer: 0.0

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: 500
  • num_epochs: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
4.0153 1.4286 100 4.2633 1.0 1.0
3.2616 2.8571 200 3.2377 1.0 1.0
3.1754 4.2857 300 3.2002 0.9892 0.9774
0.686 5.7143 400 0.5966 0.65 0.1446
0.2345 7.1429 500 0.2319 0.2981 0.0567
0.1576 8.5714 600 0.1181 0.1759 0.0309
0.1403 10.0 700 0.0603 0.0956 0.0155
0.0833 11.4286 800 0.0297 0.0475 0.0077
0.0552 12.8571 900 0.0261 0.0405 0.0055
0.059 14.2857 1000 0.0225 0.0335 0.0054
0.093 15.7143 1100 0.0136 0.0203 0.0035
0.0785 17.1429 1200 0.0153 0.0253 0.0043
0.0551 18.5714 1300 0.0120 0.0215 0.0030
0.0742 20.0 1400 0.0074 0.0095 0.0013
0.0285 21.4286 1500 0.0053 0.0095 0.0014
0.021 22.8571 1600 0.0066 0.0108 0.0015
0.0297 24.2857 1700 0.0038 0.0057 0.0007
0.0451 25.7143 1800 0.0062 0.0095 0.0014
0.0353 27.1429 1900 0.0270 0.0222 0.0038
0.0426 28.5714 2000 0.0066 0.0095 0.0015
0.0296 30.0 2100 0.0042 0.0076 0.0011
0.0348 31.4286 2200 0.0031 0.0051 0.0008
0.0336 32.8571 2300 0.0047 0.0070 0.0010
0.0126 34.2857 2400 0.0021 0.0051 0.0007
0.0287 35.7143 2500 0.0031 0.0063 0.0008
0.0253 37.1429 2600 0.0046 0.0070 0.0012
0.0317 38.5714 2700 0.0038 0.0044 0.0007
0.1223 40.0 2800 0.0035 0.0076 0.0012
0.0337 41.4286 2900 0.0031 0.0032 0.0005
0.0125 42.8571 3000 0.0039 0.0076 0.0010
0.0043 44.2857 3100 0.0026 0.0013 0.0003
0.0261 45.7143 3200 0.0016 0.0013 0.0002
0.0129 47.1429 3300 0.0014 0.0038 0.0007
0.0168 48.5714 3400 0.0031 0.0044 0.0006
0.0274 50.0 3500 0.0005 0.0 0.0
0.0157 51.4286 3600 0.0014 0.0025 0.0003
0.0149 52.8571 3700 0.0010 0.0019 0.0003
0.0095 54.2857 3800 0.0009 0.0019 0.0003
0.0158 55.7143 3900 0.0031 0.0044 0.0005
0.0103 57.1429 4000 0.0015 0.0019 0.0004
0.0262 58.5714 4100 0.0024 0.0013 0.0001
0.0515 60.0 4200 0.0007 0.0032 0.0003
0.0085 61.4286 4300 0.0004 0.0 0.0
0.0169 62.8571 4400 0.0018 0.0032 0.0005
0.0096 64.2857 4500 0.0004 0.0 0.0
0.0052 65.7143 4600 0.0002 0.0 0.0
0.0219 67.1429 4700 0.0003 0.0 0.0
0.0031 68.5714 4800 0.0002 0.0 0.0
0.0033 70.0 4900 0.0003 0.0 0.0
0.0026 71.4286 5000 0.0008 0.0006 0.0001
0.0036 72.8571 5100 0.0005 0.0006 0.0001
0.0045 74.2857 5200 0.0002 0.0 0.0
0.0038 75.7143 5300 0.0025 0.0044 0.0007
0.0101 77.1429 5400 0.0003 0.0 0.0
0.0075 78.5714 5500 0.0002 0.0 0.0
0.0086 80.0 5600 0.0001 0.0 0.0
0.0047 81.4286 5700 0.0001 0.0 0.0
0.0009 82.8571 5800 0.0001 0.0 0.0
0.0056 84.2857 5900 0.0001 0.0 0.0
0.009 85.7143 6000 0.0001 0.0 0.0
0.0003 87.1429 6100 0.0001 0.0 0.0
0.0005 88.5714 6200 0.0001 0.0 0.0
0.0034 90.0 6300 0.0001 0.0 0.0
0.0103 91.4286 6400 0.0001 0.0 0.0
0.0027 92.8571 6500 0.0001 0.0 0.0
0.0029 94.2857 6600 0.0001 0.0 0.0
0.004 95.7143 6700 0.0001 0.0 0.0
0.0011 97.1429 6800 0.0001 0.0 0.0
0.0033 98.5714 6900 0.0001 0.0 0.0
0.0003 100.0 7000 0.0001 0.0 0.0

Framework versions

  • Transformers 4.42.0.dev0
  • Pytorch 2.3.1+cu121
  • Datasets 2.19.2
  • Tokenizers 0.19.1