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wav2vec2-xls-r-300m-italian

Automatic Speech Recognition
Transformers
PyTorch
Safetensors
Italian
wav2vec2
Generated from Trainer
hf-asr-leaderboard
mozilla-foundation/common_voice_7_0
robust-speech-event
Eval Results
Inference Endpoints
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metadata
language:
  - it
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: wav2vec2-xls-r-300m-italian
    results: []

wav2vec2-xls-r-300m-italian

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - IT dataset. It achieves the following results on the evaluation set:

  • Loss: inf
  • Wer: 0.1710

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: 64
  • eval_batch_size: 8
  • 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: 5.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
No log 0.04 100 inf 1.0
No log 0.09 200 inf 0.9983
No log 0.13 300 inf 0.7672
No log 0.18 400 inf 0.6919
2.9929 0.22 500 inf 0.6266
2.9929 0.26 600 inf 0.5513
2.9929 0.31 700 inf 0.5081
2.9929 0.35 800 inf 0.4945
2.9929 0.39 900 inf 0.4720
0.5311 0.44 1000 inf 0.4387
0.5311 0.48 1100 inf 0.4411
0.5311 0.53 1200 inf 0.4429
0.5311 0.57 1300 inf 0.4322
0.5311 0.61 1400 inf 0.4532
0.4654 0.66 1500 inf 0.4492
0.4654 0.7 1600 inf 0.3879
0.4654 0.75 1700 inf 0.3836
0.4654 0.79 1800 inf 0.3743
0.4654 0.83 1900 inf 0.3687
0.4254 0.88 2000 inf 0.3793
0.4254 0.92 2100 inf 0.3766
0.4254 0.97 2200 inf 0.3705
0.4254 1.01 2300 inf 0.3272
0.4254 1.05 2400 inf 0.3185
0.3997 1.1 2500 inf 0.3244
0.3997 1.14 2600 inf 0.3082
0.3997 1.18 2700 inf 0.3040
0.3997 1.23 2800 inf 0.3028
0.3997 1.27 2900 inf 0.3112
0.3668 1.32 3000 inf 0.3110
0.3668 1.36 3100 inf 0.3067
0.3668 1.4 3200 inf 0.2961
0.3668 1.45 3300 inf 0.3081
0.3668 1.49 3400 inf 0.2936
0.3645 1.54 3500 inf 0.3037
0.3645 1.58 3600 inf 0.2974
0.3645 1.62 3700 inf 0.3010
0.3645 1.67 3800 inf 0.2985
0.3645 1.71 3900 inf 0.2976
0.3624 1.76 4000 inf 0.2928
0.3624 1.8 4100 inf 0.2860
0.3624 1.84 4200 inf 0.2922
0.3624 1.89 4300 inf 0.2866
0.3624 1.93 4400 inf 0.2776
0.3527 1.97 4500 inf 0.2792
0.3527 2.02 4600 inf 0.2858
0.3527 2.06 4700 inf 0.2767
0.3527 2.11 4800 inf 0.2824
0.3527 2.15 4900 inf 0.2799
0.3162 2.19 5000 inf 0.2673
0.3162 2.24 5100 inf 0.2962
0.3162 2.28 5200 inf 0.2736
0.3162 2.33 5300 inf 0.2652
0.3162 2.37 5400 inf 0.2551
0.3063 2.41 5500 inf 0.2680
0.3063 2.46 5600 inf 0.2558
0.3063 2.5 5700 inf 0.2598
0.3063 2.54 5800 inf 0.2518
0.3063 2.59 5900 inf 0.2541
0.2913 2.63 6000 inf 0.2507
0.2913 2.68 6100 inf 0.2500
0.2913 2.72 6200 inf 0.2435
0.2913 2.76 6300 inf 0.2376
0.2913 2.81 6400 inf 0.2348
0.2797 2.85 6500 inf 0.2512
0.2797 2.9 6600 inf 0.2382
0.2797 2.94 6700 inf 0.2523
0.2797 2.98 6800 inf 0.2522
0.2797 3.03 6900 inf 0.2409
0.2766 3.07 7000 inf 0.2453
0.2766 3.12 7100 inf 0.2326
0.2766 3.16 7200 inf 0.2286
0.2766 3.2 7300 inf 0.2342
0.2766 3.25 7400 inf 0.2305
0.2468 3.29 7500 inf 0.2238
0.2468 3.33 7600 inf 0.2321
0.2468 3.38 7700 inf 0.2305
0.2468 3.42 7800 inf 0.2174
0.2468 3.47 7900 inf 0.2201
0.2439 3.51 8000 inf 0.2133
0.2439 3.55 8100 inf 0.2217
0.2439 3.6 8200 inf 0.2189
0.2439 3.64 8300 inf 0.2105
0.2439 3.69 8400 inf 0.2118
0.2357 3.73 8500 inf 0.2093
0.2357 3.77 8600 inf 0.2103
0.2357 3.82 8700 inf 0.2035
0.2357 3.86 8800 inf 0.2019
0.2357 3.91 8900 inf 0.2032
0.2217 3.95 9000 inf 0.2056
0.2217 3.99 9100 inf 0.2022
0.2217 4.04 9200 inf 0.1932
0.2217 4.08 9300 inf 0.1935
0.2217 4.12 9400 inf 0.1906
0.2025 4.17 9500 inf 0.1879
0.2025 4.21 9600 inf 0.1882
0.2025 4.26 9700 inf 0.1854
0.2025 4.3 9800 inf 0.1865
0.2025 4.34 9900 inf 0.1844
0.1869 4.39 10000 inf 0.1822
0.1869 4.43 10100 inf 0.1815
0.1869 4.48 10200 inf 0.1812
0.1869 4.52 10300 inf 0.1792
0.1869 4.56 10400 inf 0.1797
0.1863 4.61 10500 inf 0.1774
0.1863 4.65 10600 inf 0.1767
0.1863 4.7 10700 inf 0.1765
0.1863 4.74 10800 inf 0.1753
0.1863 4.78 10900 inf 0.1731
0.178 4.83 11000 inf 0.1727
0.178 4.87 11100 inf 0.1724
0.178 4.91 11200 inf 0.1722
0.178 4.96 11300 inf 0.1712

Framework versions

  • Transformers 4.16.0.dev0
  • Pytorch 1.10.1+cu102
  • Datasets 1.17.1.dev0
  • Tokenizers 0.11.0