--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer model-index: - name: ft_1 results: [] --- # ft_1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0074 - Cer: 0.0011 ## 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: 0.0001 - train_batch_size: 8 - 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: 1000 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:------:|:---------------:|:------:| | 9.6728 | 0.08 | 500 | 0.9218 | 1.0 | | 0.6369 | 0.17 | 1000 | 0.6685 | 1.0 | | 0.511 | 0.25 | 1500 | 0.4867 | 0.5464 | | 0.4632 | 0.34 | 2000 | 0.4821 | 0.2281 | | 0.4354 | 0.42 | 2500 | 0.3887 | 0.1763 | | 0.3664 | 0.51 | 3000 | 0.2324 | 0.0484 | | 0.2641 | 0.59 | 3500 | 0.1729 | 0.0276 | | 0.218 | 0.67 | 4000 | 0.1485 | 0.0213 | | 0.1938 | 0.76 | 4500 | 0.1270 | 0.0188 | | 0.1867 | 0.84 | 5000 | 0.1192 | 0.0177 | | 0.1756 | 0.93 | 5500 | 0.1047 | 0.0163 | | 0.1659 | 1.01 | 6000 | 0.1008 | 0.0150 | | 0.1522 | 1.1 | 6500 | 0.0970 | 0.0140 | | 0.1456 | 1.18 | 7000 | 0.0944 | 0.0140 | | 0.1431 | 1.26 | 7500 | 0.0905 | 0.0134 | | 0.1416 | 1.35 | 8000 | 0.0919 | 0.0120 | | 0.1356 | 1.43 | 8500 | 0.0792 | 0.0124 | | 0.1345 | 1.52 | 9000 | 0.0880 | 0.0129 | | 0.1316 | 1.6 | 9500 | 0.0753 | 0.0111 | | 0.1293 | 1.69 | 10000 | 0.0728 | 0.0106 | | 0.1238 | 1.77 | 10500 | 0.0682 | 0.0109 | | 0.121 | 1.85 | 11000 | 0.0645 | 0.0100 | | 0.1161 | 1.94 | 11500 | 0.0658 | 0.0094 | | 0.1107 | 2.02 | 12000 | 0.0669 | 0.0115 | | 0.1054 | 2.11 | 12500 | 0.0597 | 0.0093 | | 0.104 | 2.19 | 13000 | 0.0572 | 0.0088 | | 0.1007 | 2.28 | 13500 | 0.0522 | 0.0082 | | 0.0986 | 2.36 | 14000 | 0.0575 | 0.0090 | | 0.103 | 2.44 | 14500 | 0.0513 | 0.0085 | | 0.0973 | 2.53 | 15000 | 0.0597 | 0.0081 | | 0.0949 | 2.61 | 15500 | 0.0474 | 0.0074 | | 0.092 | 2.7 | 16000 | 0.0538 | 0.0083 | | 0.0944 | 2.78 | 16500 | 0.0471 | 0.0078 | | 0.0896 | 2.87 | 17000 | 0.0417 | 0.0064 | | 0.0895 | 2.95 | 17500 | 0.0406 | 0.0063 | | 0.0866 | 3.03 | 18000 | 0.0393 | 0.0064 | | 0.0795 | 3.12 | 18500 | 0.0377 | 0.0067 | | 0.0803 | 3.2 | 19000 | 0.0380 | 0.0063 | | 0.0771 | 3.29 | 19500 | 0.0386 | 0.0058 | | 0.0742 | 3.37 | 20000 | 0.0347 | 0.0057 | | 0.0743 | 3.46 | 20500 | 0.0363 | 0.0062 | | 0.073 | 3.54 | 21000 | 0.0314 | 0.0051 | | 0.0727 | 3.62 | 21500 | 0.0293 | 0.0050 | | 0.0693 | 3.71 | 22000 | 0.0331 | 0.0057 | | 0.0689 | 3.79 | 22500 | 0.0306 | 0.0050 | | 0.0712 | 3.88 | 23000 | 0.0304 | 0.0049 | | 0.0697 | 3.96 | 23500 | 0.0287 | 0.0050 | | 0.0656 | 4.05 | 24000 | 0.0269 | 0.0046 | | 0.0624 | 4.13 | 24500 | 0.0282 | 0.0048 | | 0.0614 | 4.21 | 25000 | 0.0262 | 0.0046 | | 0.0589 | 4.3 | 25500 | 0.0245 | 0.0044 | | 0.0596 | 4.38 | 26000 | 0.0237 | 0.0043 | | 0.0589 | 4.47 | 26500 | 0.0233 | 0.0045 | | 0.059 | 4.55 | 27000 | 0.0275 | 0.0044 | | 0.0564 | 4.64 | 27500 | 0.0241 | 0.0042 | | 0.0589 | 4.72 | 28000 | 0.0209 | 0.0044 | | 0.055 | 4.8 | 28500 | 0.0199 | 0.0039 | | 0.0629 | 4.89 | 29000 | 0.0223 | 0.0046 | | 0.0581 | 4.97 | 29500 | 0.0234 | 0.0044 | | 0.0529 | 5.06 | 30000 | 0.0197 | 0.0037 | | 0.0489 | 5.14 | 30500 | 0.0232 | 0.0044 | | 0.0512 | 5.23 | 31000 | 0.0202 | 0.0039 | | 0.0501 | 5.31 | 31500 | 0.0182 | 0.0036 | | 0.0494 | 5.39 | 32000 | 0.0181 | 0.0034 | | 0.049 | 5.48 | 32500 | 0.0173 | 0.0034 | | 0.0482 | 5.56 | 33000 | 0.0173 | 0.0034 | | 0.0505 | 5.65 | 33500 | 0.0172 | 0.0033 | | 0.048 | 5.73 | 34000 | 0.0168 | 0.0034 | | 0.0485 | 5.82 | 34500 | 0.0162 | 0.0032 | | 0.0461 | 5.9 | 35000 | 0.0154 | 0.0032 | | 0.0436 | 5.98 | 35500 | 0.0152 | 0.0033 | | 0.043 | 6.07 | 36000 | 0.0157 | 0.0033 | | 0.0429 | 6.15 | 36500 | 0.0160 | 0.0036 | | 0.0402 | 6.24 | 37000 | 0.0169 | 0.0034 | | 0.043 | 6.32 | 37500 | 0.0135 | 0.0030 | | 0.0409 | 6.41 | 38000 | 0.0167 | 0.0033 | | 0.0446 | 6.49 | 38500 | 0.0155 | 0.0035 | | 0.0402 | 6.57 | 39000 | 0.0145 | 0.0029 | | 0.0427 | 6.66 | 39500 | 0.0139 | 0.0028 | | 0.0392 | 6.74 | 40000 | 0.0148 | 0.0028 | | 0.0395 | 6.83 | 40500 | 0.0144 | 0.0026 | | 0.0396 | 6.91 | 41000 | 0.0208 | 0.0035 | | 0.0394 | 7.0 | 41500 | 0.0127 | 0.0026 | | 0.0367 | 7.08 | 42000 | 0.0129 | 0.0023 | | 0.0368 | 7.16 | 42500 | 0.0120 | 0.0024 | | 0.0345 | 7.25 | 43000 | 0.0119 | 0.0027 | | 0.0342 | 7.33 | 43500 | 0.0123 | 0.0026 | | 0.0368 | 7.42 | 44000 | 0.0109 | 0.0024 | | 0.0358 | 7.5 | 44500 | 0.0120 | 0.0026 | | 0.036 | 7.59 | 45000 | 0.0110 | 0.0026 | | 0.0332 | 7.67 | 45500 | 0.0115 | 0.0024 | | 0.0356 | 7.75 | 46000 | 0.0128 | 0.0024 | | 0.0353 | 7.84 | 46500 | 0.0123 | 0.0024 | | 0.0365 | 7.92 | 47000 | 0.0106 | 0.0023 | | 0.0341 | 8.01 | 47500 | 0.0110 | 0.0023 | | 0.0286 | 8.09 | 48000 | 0.0109 | 0.0023 | | 0.0324 | 8.18 | 48500 | 0.0099 | 0.0022 | | 0.0321 | 8.26 | 49000 | 0.0111 | 0.0023 | | 0.0333 | 8.34 | 49500 | 0.0110 | 0.0023 | | 0.0302 | 8.43 | 50000 | 0.0116 | 0.0026 | | 0.0325 | 8.51 | 50500 | 0.0123 | 0.0025 | | 0.0317 | 8.6 | 51000 | 0.0101 | 0.0027 | | 0.032 | 8.68 | 51500 | 0.0108 | 0.0024 | | 0.0298 | 8.77 | 52000 | 0.0105 | 0.0024 | | 0.0304 | 8.85 | 52500 | 0.0113 | 0.0022 | | 0.0315 | 8.93 | 53000 | 0.0109 | 0.0023 | | 0.0299 | 9.02 | 53500 | 0.0102 | 0.0022 | | 0.029 | 9.1 | 54000 | 0.0108 | 0.0021 | | 0.0261 | 9.19 | 54500 | 0.0112 | 0.0022 | | 0.0286 | 9.27 | 55000 | 0.0116 | 0.0023 | | 0.0293 | 9.36 | 55500 | 0.0108 | 0.0023 | | 0.0267 | 9.44 | 56000 | 0.0109 | 0.0023 | | 0.0271 | 9.52 | 56500 | 0.0103 | 0.0021 | | 0.0276 | 9.61 | 57000 | 0.0102 | 0.0021 | | 0.0256 | 9.69 | 57500 | 0.0101 | 0.0021 | | 0.0264 | 9.78 | 58000 | 0.0131 | 0.0021 | | 0.0277 | 9.86 | 58500 | 0.0096 | 0.0021 | | 0.0303 | 9.95 | 59000 | 0.0097 | 0.0020 | | 0.027 | 10.03 | 59500 | 0.0102 | 0.0021 | | 0.0241 | 10.11 | 60000 | 0.0089 | 0.0020 | | 0.0226 | 10.2 | 60500 | 0.0108 | 0.0020 | | 0.0248 | 10.28 | 61000 | 0.0109 | 0.0021 | | 0.0244 | 10.37 | 61500 | 0.0093 | 0.0020 | | 0.025 | 10.45 | 62000 | 0.0167 | 0.0021 | | 0.0238 | 10.54 | 62500 | 0.0123 | 0.0019 | | 0.0253 | 10.62 | 63000 | 0.0089 | 0.0019 | | 0.0247 | 10.7 | 63500 | 0.0090 | 0.0019 | | 0.0229 | 10.79 | 64000 | 0.0084 | 0.0017 | | 0.0242 | 10.87 | 64500 | 0.0086 | 0.0019 | | 0.0251 | 10.96 | 65000 | 0.0082 | 0.0017 | | 0.0222 | 11.04 | 65500 | 0.0081 | 0.0017 | | 0.0233 | 11.13 | 66000 | 0.0078 | 0.0018 | | 0.0226 | 11.21 | 66500 | 0.0081 | 0.0017 | | 0.0227 | 11.29 | 67000 | 0.0096 | 0.0018 | | 0.024 | 11.38 | 67500 | 0.0081 | 0.0017 | | 0.0217 | 11.46 | 68000 | 0.0088 | 0.0019 | | 0.0222 | 11.55 | 68500 | 0.0086 | 0.0019 | | 0.0229 | 11.63 | 69000 | 0.0091 | 0.0021 | | 0.02 | 11.72 | 69500 | 0.0094 | 0.0020 | | 0.022 | 11.8 | 70000 | 0.0095 | 0.0017 | | 0.0232 | 11.88 | 70500 | 0.0078 | 0.0018 | | 0.0216 | 11.97 | 71000 | 0.0088 | 0.0017 | | 0.0216 | 12.05 | 71500 | 0.0081 | 0.0017 | | 0.0182 | 12.14 | 72000 | 0.0088 | 0.0018 | | 0.0196 | 12.22 | 72500 | 0.0092 | 0.0017 | | 0.0203 | 12.31 | 73000 | 0.0083 | 0.0018 | | 0.0191 | 12.39 | 73500 | 0.0086 | 0.0018 | | 0.0192 | 12.47 | 74000 | 0.0122 | 0.0016 | | 0.0194 | 12.56 | 74500 | 0.0084 | 0.0016 | | 0.0192 | 12.64 | 75000 | 0.0098 | 0.0016 | | 0.0192 | 12.73 | 75500 | 0.0101 | 0.0017 | | 0.0191 | 12.81 | 76000 | 0.0100 | 0.0016 | | 0.0191 | 12.9 | 76500 | 0.0091 | 0.0016 | | 0.0184 | 12.98 | 77000 | 0.0084 | 0.0017 | | 0.0188 | 13.06 | 77500 | 0.0080 | 0.0016 | | 0.0188 | 13.15 | 78000 | 0.0094 | 0.0017 | | 0.0172 | 13.23 | 78500 | 0.0098 | 0.0017 | | 0.0174 | 13.32 | 79000 | 0.0134 | 0.0015 | | 0.0177 | 13.4 | 79500 | 0.0106 | 0.0016 | | 0.0178 | 13.49 | 80000 | 0.0100 | 0.0014 | | 0.017 | 13.57 | 80500 | 0.0104 | 0.0016 | | 0.0169 | 13.65 | 81000 | 0.0094 | 0.0014 | | 0.0189 | 13.74 | 81500 | 0.0089 | 0.0015 | | 0.0172 | 13.82 | 82000 | 0.0086 | 0.0016 | | 0.0167 | 13.91 | 82500 | 0.0091 | 0.0015 | | 0.0179 | 13.99 | 83000 | 0.0088 | 0.0015 | | 0.0175 | 14.08 | 83500 | 0.0076 | 0.0014 | | 0.0164 | 14.16 | 84000 | 0.0082 | 0.0013 | | 0.0143 | 14.24 | 84500 | 0.0080 | 0.0015 | | 0.0158 | 14.33 | 85000 | 0.0082 | 0.0014 | | 0.0153 | 14.41 | 85500 | 0.0086 | 0.0016 | | 0.0173 | 14.5 | 86000 | 0.0077 | 0.0015 | | 0.0149 | 14.58 | 86500 | 0.0084 | 0.0016 | | 0.0153 | 14.67 | 87000 | 0.0078 | 0.0015 | | 0.0157 | 14.75 | 87500 | 0.0074 | 0.0014 | | 0.0173 | 14.83 | 88000 | 0.0086 | 0.0015 | | 0.0171 | 14.92 | 88500 | 0.0080 | 0.0014 | | 0.0148 | 15.0 | 89000 | 0.0073 | 0.0013 | | 0.0145 | 15.09 | 89500 | 0.0074 | 0.0014 | | 0.0153 | 15.17 | 90000 | 0.0068 | 0.0014 | | 0.0156 | 15.26 | 90500 | 0.0070 | 0.0014 | | 0.0143 | 15.34 | 91000 | 0.0072 | 0.0014 | | 0.0143 | 15.42 | 91500 | 0.0069 | 0.0015 | | 0.0139 | 15.51 | 92000 | 0.0070 | 0.0014 | | 0.0149 | 15.59 | 92500 | 0.0087 | 0.0015 | | 0.0124 | 15.68 | 93000 | 0.0074 | 0.0013 | | 0.0139 | 15.76 | 93500 | 0.0076 | 0.0013 | | 0.0148 | 15.85 | 94000 | 0.0074 | 0.0014 | | 0.013 | 15.93 | 94500 | 0.0073 | 0.0014 | | 0.0138 | 16.01 | 95000 | 0.0069 | 0.0013 | | 0.0148 | 16.1 | 95500 | 0.0069 | 0.0013 | | 0.0135 | 16.18 | 96000 | 0.0067 | 0.0014 | | 0.0143 | 16.27 | 96500 | 0.0067 | 0.0012 | | 0.014 | 16.35 | 97000 | 0.0071 | 0.0013 | | 0.0138 | 16.44 | 97500 | 0.0072 | 0.0012 | | 0.0132 | 16.52 | 98000 | 0.0070 | 0.0012 | | 0.0122 | 16.6 | 98500 | 0.0077 | 0.0013 | | 0.0124 | 16.69 | 99000 | 0.0078 | 0.0013 | | 0.0138 | 16.77 | 99500 | 0.0070 | 0.0012 | | 0.0118 | 16.86 | 100000 | 0.0071 | 0.0013 | | 0.0123 | 16.94 | 100500 | 0.0068 | 0.0013 | | 0.0113 | 17.03 | 101000 | 0.0070 | 0.0013 | | 0.011 | 17.11 | 101500 | 0.0076 | 0.0013 | | 0.0117 | 17.19 | 102000 | 0.0073 | 0.0013 | | 0.0123 | 17.28 | 102500 | 0.0078 | 0.0012 | | 0.0132 | 17.36 | 103000 | 0.0072 | 0.0013 | | 0.0105 | 17.45 | 103500 | 0.0078 | 0.0012 | | 0.0115 | 17.53 | 104000 | 0.0078 | 0.0012 | | 0.0112 | 17.62 | 104500 | 0.0076 | 0.0012 | | 0.0108 | 17.7 | 105000 | 0.0080 | 0.0012 | | 0.0106 | 17.78 | 105500 | 0.0080 | 0.0012 | | 0.0114 | 17.87 | 106000 | 0.0074 | 0.0012 | | 0.0127 | 17.95 | 106500 | 0.0070 | 0.0012 | | 0.0105 | 18.04 | 107000 | 0.0073 | 0.0012 | | 0.0108 | 18.12 | 107500 | 0.0083 | 0.0012 | | 0.0107 | 18.21 | 108000 | 0.0088 | 0.0012 | | 0.0102 | 18.29 | 108500 | 0.0080 | 0.0011 | | 0.0089 | 18.37 | 109000 | 0.0073 | 0.0011 | | 0.0107 | 18.46 | 109500 | 0.0073 | 0.0011 | | 0.0112 | 18.54 | 110000 | 0.0078 | 0.0011 | | 0.0106 | 18.63 | 110500 | 0.0077 | 0.0011 | | 0.0101 | 18.71 | 111000 | 0.0078 | 0.0011 | | 0.0111 | 18.8 | 111500 | 0.0080 | 0.0011 | | 0.0106 | 18.88 | 112000 | 0.0073 | 0.0011 | | 0.0102 | 18.96 | 112500 | 0.0075 | 0.0011 | | 0.0098 | 19.05 | 113000 | 0.0075 | 0.0011 | | 0.0118 | 19.13 | 113500 | 0.0078 | 0.0011 | | 0.0094 | 19.22 | 114000 | 0.0077 | 0.0011 | | 0.009 | 19.3 | 114500 | 0.0077 | 0.0011 | | 0.0094 | 19.39 | 115000 | 0.0077 | 0.0011 | | 0.0084 | 19.47 | 115500 | 0.0074 | 0.0010 | | 0.011 | 19.55 | 116000 | 0.0076 | 0.0011 | | 0.0106 | 19.64 | 116500 | 0.0073 | 0.0011 | | 0.009 | 19.72 | 117000 | 0.0074 | 0.0011 | | 0.0097 | 19.81 | 117500 | 0.0074 | 0.0011 | | 0.0095 | 19.89 | 118000 | 0.0074 | 0.0011 | | 0.0094 | 19.98 | 118500 | 0.0074 | 0.0011 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.13.0 - Tokenizers 0.15.0