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Rolv-Arild
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xls-r-300m-npsc-seq2seq

Automatic Speech Recognition
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PyTorch
TensorBoard
speech-encoder-decoder
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update model card README.md

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@@ -13,8 +13,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model was trained from scratch on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3350
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- - Wer: 0.3441
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  ## Model description
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@@ -33,7 +33,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.0003
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
@@ -41,7 +41,7 @@ The following hyperparameters were used during training:
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  - total_train_batch_size: 64
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - lr_scheduler_warmup_steps: 400
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  - num_epochs: 20.0
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  - mixed_precision_training: Native AMP
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@@ -49,45 +49,45 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:-----:|:---------------:|:------:|
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- | 2.2943 | 0.51 | 400 | 4.4367 | 0.9745 |
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- | 2.8816 | 1.02 | 800 | 2.8103 | 1.2576 |
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- | 2.7842 | 1.53 | 1200 | 2.6832 | 1.1330 |
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- | 2.313 | 2.04 | 1600 | 2.6678 | 1.1348 |
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- | 2.2897 | 2.55 | 2000 | 2.5744 | 1.2102 |
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- | 1.3821 | 3.06 | 2400 | 2.9908 | 0.9377 |
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- | 1.074 | 3.57 | 2800 | 2.6649 | 0.8966 |
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- | 1.3643 | 4.08 | 3200 | 1.0064 | 0.7236 |
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- | 0.8286 | 4.59 | 3600 | 0.6339 | 0.5454 |
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- | 0.3872 | 5.1 | 4000 | 0.5170 | 0.4718 |
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- | 0.3654 | 5.61 | 4400 | 0.4386 | 0.4420 |
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- | 0.2672 | 6.12 | 4800 | 0.5186 | 0.4679 |
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- | 0.2519 | 6.63 | 5200 | 0.4238 | 0.4177 |
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- | 0.3293 | 7.14 | 5600 | 0.3584 | 0.3970 |
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- | 0.314 | 7.65 | 6000 | 0.3325 | 0.3911 |
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- | 0.1698 | 8.16 | 6400 | 0.3411 | 0.3855 |
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- | 0.1682 | 8.67 | 6800 | 0.3239 | 0.3801 |
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- | 0.1325 | 9.18 | 7200 | 0.3474 | 0.3832 |
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- | 0.1577 | 9.69 | 7600 | 0.3289 | 0.3839 |
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- | 0.2259 | 10.2 | 8000 | 0.3183 | 0.3756 |
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- | 0.2473 | 10.71 | 8400 | 0.3132 | 0.3654 |
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- | 0.1136 | 11.22 | 8800 | 0.3242 | 0.3670 |
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- | 0.108 | 11.73 | 9200 | 0.3201 | 0.3601 |
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- | 0.0806 | 12.24 | 9600 | 0.3223 | 0.3609 |
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- | 0.0896 | 12.75 | 10000 | 0.3228 | 0.3584 |
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- | 0.1642 | 13.27 | 10400 | 0.3140 | 0.3547 |
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- | 0.1442 | 13.77 | 10800 | 0.3235 | 0.3539 |
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- | 0.0802 | 14.29 | 11200 | 0.3175 | 0.3553 |
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- | 0.0747 | 14.8 | 11600 | 0.3126 | 0.3512 |
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- | 0.0488 | 15.31 | 12000 | 0.3292 | 0.3525 |
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- | 0.0469 | 15.82 | 12400 | 0.3231 | 0.3504 |
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- | 0.1021 | 16.33 | 12800 | 0.3230 | 0.3502 |
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- | 0.0841 | 16.84 | 13200 | 0.3348 | 0.3513 |
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- | 0.0502 | 17.35 | 13600 | 0.3318 | 0.3486 |
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- | 0.059 | 17.86 | 14000 | 0.3359 | 0.3462 |
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- | 0.0417 | 18.37 | 14400 | 0.3310 | 0.3467 |
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- | 0.0322 | 18.88 | 14800 | 0.3325 | 0.3467 |
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- | 0.0531 | 19.39 | 15200 | 0.3357 | 0.3449 |
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- | 0.0623 | 19.9 | 15600 | 0.3350 | 0.3441 |
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  ### Framework versions
 
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  This model was trained from scratch on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2965
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+ - Wer: 0.3144
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.0001
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
 
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  - total_train_batch_size: 64
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 1000
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  - num_epochs: 20.0
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  - mixed_precision_training: Native AMP
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:-----:|:---------------:|:------:|
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+ | 2.888 | 0.51 | 400 | 3.7320 | 0.9440 |
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+ | 3.1636 | 1.02 | 800 | 2.9188 | 1.1916 |
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+ | 2.773 | 1.53 | 1200 | 2.3347 | 1.0134 |
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+ | 0.7198 | 2.04 | 1600 | 0.6678 | 0.4826 |
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+ | 0.5255 | 2.55 | 2000 | 0.4605 | 0.4135 |
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+ | 0.3961 | 3.06 | 2400 | 0.4266 | 0.3955 |
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+ | 0.3424 | 3.57 | 2800 | 0.3786 | 0.3741 |
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+ | 0.3858 | 4.08 | 3200 | 0.3161 | 0.3552 |
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+ | 0.3218 | 4.59 | 3600 | 0.3029 | 0.3510 |
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+ | 0.199 | 5.1 | 4000 | 0.2988 | 0.3418 |
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+ | 0.2054 | 5.61 | 4400 | 0.2873 | 0.3434 |
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+ | 0.1704 | 6.12 | 4800 | 0.3129 | 0.3432 |
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+ | 0.1805 | 6.63 | 5200 | 0.2963 | 0.3413 |
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+ | 0.2091 | 7.14 | 5600 | 0.2755 | 0.3329 |
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+ | 0.1971 | 7.65 | 6000 | 0.2706 | 0.3309 |
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+ | 0.1237 | 8.16 | 6400 | 0.2823 | 0.3270 |
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+ | 0.123 | 8.67 | 6800 | 0.2754 | 0.3246 |
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+ | 0.103 | 9.18 | 7200 | 0.2917 | 0.3272 |
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+ | 0.1143 | 9.69 | 7600 | 0.2885 | 0.3305 |
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+ | 0.156 | 10.2 | 8000 | 0.2810 | 0.3288 |
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+ | 0.167 | 10.71 | 8400 | 0.2689 | 0.3232 |
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+ | 0.0815 | 11.22 | 8800 | 0.2899 | 0.3236 |
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+ | 0.0844 | 11.73 | 9200 | 0.2798 | 0.3225 |
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+ | 0.0775 | 12.24 | 9600 | 0.2894 | 0.3224 |
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+ | 0.0677 | 12.75 | 10000 | 0.2838 | 0.3204 |
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+ | 0.1383 | 13.27 | 10400 | 0.2959 | 0.3211 |
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+ | 0.1233 | 13.77 | 10800 | 0.2922 | 0.3213 |
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+ | 0.0688 | 14.29 | 11200 | 0.2903 | 0.3209 |
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+ | 0.0655 | 14.8 | 11600 | 0.2868 | 0.3182 |
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+ | 0.0449 | 15.31 | 12000 | 0.2959 | 0.3172 |
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+ | 0.0421 | 15.82 | 12400 | 0.2966 | 0.3180 |
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+ | 0.0858 | 16.33 | 12800 | 0.2941 | 0.3164 |
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+ | 0.0859 | 16.84 | 13200 | 0.2980 | 0.3165 |
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+ | 0.0561 | 17.35 | 13600 | 0.2965 | 0.3165 |
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+ | 0.0506 | 17.86 | 14000 | 0.2935 | 0.3148 |
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+ | 0.0312 | 18.37 | 14400 | 0.2964 | 0.3154 |
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+ | 0.0403 | 18.88 | 14800 | 0.2967 | 0.3160 |
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+ | 0.0924 | 19.39 | 15200 | 0.2955 | 0.3147 |
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+ | 0.0585 | 19.9 | 15600 | 0.2965 | 0.3144 |
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  ### Framework versions