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ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1-5gram

This model is a fine-tuned version of gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1-5gram on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING3 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4505
  • Wer: 0.2119

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: 1e-07
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • 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: 100
  • num_epochs: 100.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.3355 1.0 144 0.4505 0.2119
0.3069 2.0 288 0.4509 0.2124
0.3049 3.0 432 0.4511 0.2119
0.3028 4.0 576 0.4521 0.2114
0.3092 5.0 720 0.4532 0.2112
0.3043 6.0 864 0.4536 0.2117
0.2903 7.0 1008 0.4543 0.2114
0.3124 8.0 1152 0.4538 0.2118
0.3079 9.0 1296 0.4541 0.2121
0.3093 10.0 1440 0.4537 0.2117
0.3093 11.0 1584 0.4544 0.2111
0.3202 12.0 1728 0.4549 0.2110
0.3086 13.0 1872 0.4546 0.2104
0.2947 14.0 2016 0.4542 0.2119
0.3145 15.0 2160 0.4539 0.2115
0.3292 16.0 2304 0.4532 0.2115
0.3049 17.0 2448 0.4547 0.2117
0.3177 18.0 2592 0.4544 0.2111
0.3108 19.0 2736 0.4547 0.2114
0.2944 20.0 2880 0.4560 0.2105
0.3232 21.0 3024 0.4560 0.2113
0.3196 22.0 3168 0.4559 0.2107
0.3207 23.0 3312 0.4563 0.2106
0.3039 24.0 3456 0.4555 0.2110
0.3157 25.0 3600 0.4560 0.2117
0.3285 26.0 3744 0.4561 0.2102
0.3125 27.0 3888 0.4553 0.2107
0.3051 28.0 4032 0.4560 0.2103
0.3166 29.0 4176 0.4560 0.2103
0.321 30.0 4320 0.4551 0.2101
0.3146 31.0 4464 0.4552 0.2100
0.323 32.0 4608 0.4551 0.2105
0.3223 33.0 4752 0.4554 0.2101
0.3105 34.0 4896 0.4549 0.2102
0.3134 35.0 5040 0.4552 0.2101
0.3054 36.0 5184 0.4550 0.2103
0.3162 37.0 5328 0.4554 0.2106
0.3094 38.0 5472 0.4551 0.2099
0.3174 39.0 5616 0.4553 0.2105
0.3218 40.0 5760 0.4553 0.2106
0.3134 41.0 5904 0.4552 0.2101
0.3019 42.0 6048 0.4552 0.2101
0.3169 43.0 6192 0.4552 0.2095
0.3209 44.0 6336 0.4550 0.2090
0.3035 45.0 6480 0.4550 0.2100
0.3181 46.0 6624 0.4550 0.2104
0.3133 47.0 6768 0.4546 0.2096
0.3173 48.0 6912 0.4556 0.2099
0.3174 49.0 7056 0.4552 0.2101
0.313 50.0 7200 0.4553 0.2100
0.3139 51.0 7344 0.4555 0.2101
0.3054 52.0 7488 0.4555 0.2100
0.3212 53.0 7632 0.4554 0.2097
0.3252 54.0 7776 0.4553 0.2097
0.3063 55.0 7920 0.4554 0.2106
0.3206 56.0 8064 0.4551 0.2097
0.3176 57.0 8208 0.4552 0.2101
0.3179 58.0 8352 0.4554 0.2099
0.3064 59.0 8496 0.4559 0.2092
0.301 60.0 8640 0.4559 0.2103
0.3103 61.0 8784 0.4559 0.2102
0.3169 62.0 8928 0.4559 0.2103
0.3081 63.0 9072 0.4559 0.2101
0.3249 64.0 9216 0.4555 0.2106
0.3031 65.0 9360 0.4553 0.2105
0.3017 66.0 9504 0.4556 0.2105
0.3261 67.0 9648 0.4551 0.2100
0.3196 68.0 9792 0.4553 0.2096
0.3085 69.0 9936 0.4554 0.2095
0.3235 70.0 10080 0.4552 0.2096
0.3194 71.0 10224 0.4550 0.2102
0.3243 72.0 10368 0.4546 0.2098
0.3115 73.0 10512 0.4542 0.2101
0.3307 74.0 10656 0.4545 0.2100
0.3072 75.0 10800 0.4547 0.2100
0.3218 76.0 10944 0.4545 0.2102
0.3116 77.0 11088 0.4540 0.2103
0.3021 78.0 11232 0.4542 0.2101
0.3165 79.0 11376 0.4539 0.2109
0.327 80.0 11520 0.4539 0.2090
0.3268 81.0 11664 0.4540 0.2110
0.304 82.0 11808 0.4537 0.2097
0.3256 83.0 11952 0.4537 0.2102
0.3208 84.0 12096 0.4544 0.2101
0.3199 85.0 12240 0.4541 0.2094
0.3104 86.0 12384 0.4543 0.2097
0.3218 87.0 12528 0.4542 0.2106
0.3301 88.0 12672 0.4538 0.2098
0.3055 89.0 12816 0.4540 0.2101
0.3154 90.0 12960 0.4533 0.2098
0.3169 91.0 13104 0.4543 0.2098
0.3122 92.0 13248 0.4541 0.2098
0.319 93.0 13392 0.4536 0.2094
0.307 94.0 13536 0.4538 0.2092
0.3132 95.0 13680 0.4540 0.2094
0.3185 96.0 13824 0.4536 0.2099
0.2996 97.0 13968 0.4541 0.2100
0.3193 98.0 14112 0.4539 0.2092
0.3091 99.0 14256 0.4538 0.2096
0.315 100.0 14400 0.4544 0.2100

Framework versions

  • Transformers 4.21.0.dev0
  • Pytorch 1.9.1+cu102
  • Datasets 2.3.3.dev0
  • Tokenizers 0.12.1
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