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gopdataset_phonome_base_add_transformer

PyTorch
wav2vec2
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: gopdataset_phonome_base_add_transformer
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # gopdataset_phonome_base_add_transformer
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+
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+ 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.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3081
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+ - Cer: 0.1141
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 32
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+ - eval_batch_size: 8
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+ - seed: 42
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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: 30
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Cer |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | 6.7266 | 0.84 | 100 | 3.4268 | 0.9750 |
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+ | 3.258 | 1.68 | 200 | 3.2266 | 0.7902 |
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+ | 2.5421 | 2.52 | 300 | 1.1589 | 0.5124 |
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+ | 1.0681 | 3.36 | 400 | 0.4367 | 0.1676 |
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+ | 0.7192 | 4.2 | 500 | 0.4418 | 0.1658 |
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+ | 0.5793 | 5.04 | 600 | 0.3079 | 0.1331 |
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+ | 0.5329 | 5.88 | 700 | 0.3078 | 0.1287 |
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+ | 0.4988 | 6.72 | 800 | 0.3051 | 0.1251 |
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+ | 0.4455 | 7.56 | 900 | 0.2843 | 0.1206 |
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+ | 0.4271 | 8.4 | 1000 | 0.2865 | 0.1234 |
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+ | 0.4027 | 9.24 | 1100 | 0.2996 | 0.1214 |
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+ | 0.3939 | 10.08 | 1200 | 0.2874 | 0.1199 |
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+ | 0.3633 | 10.92 | 1300 | 0.2777 | 0.1237 |
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+ | 0.3482 | 11.76 | 1400 | 0.2648 | 0.1171 |
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+ | 0.3267 | 12.61 | 1500 | 0.2737 | 0.1174 |
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+ | 0.3334 | 13.45 | 1600 | 0.2812 | 0.1176 |
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+ | 0.3145 | 14.29 | 1700 | 0.2709 | 0.1163 |
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+ | 0.2921 | 15.13 | 1800 | 0.2689 | 0.1153 |
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+ | 0.2939 | 15.97 | 1900 | 0.2757 | 0.1153 |
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+ | 0.2681 | 16.81 | 2000 | 0.2785 | 0.1161 |
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+ | 0.2691 | 17.65 | 2100 | 0.2955 | 0.1196 |
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+ | 0.2627 | 18.49 | 2200 | 0.2922 | 0.1174 |
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+ | 0.2519 | 19.33 | 2300 | 0.2820 | 0.1148 |
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+ | 0.2391 | 20.17 | 2400 | 0.3038 | 0.1190 |
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+ | 0.2393 | 21.01 | 2500 | 0.2873 | 0.1162 |
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+ | 0.2324 | 21.85 | 2600 | 0.2903 | 0.1148 |
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+ | 0.2217 | 22.69 | 2700 | 0.3018 | 0.1167 |
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+ | 0.2156 | 23.53 | 2800 | 0.3033 | 0.1153 |
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+ | 0.2039 | 24.37 | 2900 | 0.2975 | 0.1147 |
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+ | 0.2018 | 25.21 | 3000 | 0.3055 | 0.1159 |
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+ | 0.1996 | 26.05 | 3100 | 0.3035 | 0.1151 |
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+ | 0.2013 | 26.89 | 3200 | 0.3032 | 0.1153 |
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+ | 0.2002 | 27.73 | 3300 | 0.3029 | 0.1146 |
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+ | 0.196 | 28.57 | 3400 | 0.3118 | 0.1157 |
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+ | 0.2047 | 29.41 | 3500 | 0.3081 | 0.1141 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.17.0
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+ - Pytorch 2.4.0
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+ - Datasets 1.18.3
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+ - Tokenizers 0.20.3