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torgo_xlsr_finetune_M01_old

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
Inference Endpoints
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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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+ datasets:
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+ - torgo
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+ metrics:
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+ - wer
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+ model-index:
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+ - name: torgo_xlsr_finetune_M01
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+ results:
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+ - task:
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+ name: Automatic Speech Recognition
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+ type: automatic-speech-recognition
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+ dataset:
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+ name: torgo
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+ type: torgo
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+ config: null
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+ split: None
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+ metrics:
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+ - name: Wer
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+ type: wer
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+ value: 0.596551724137931
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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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+ # torgo_xlsr_finetune_M01
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+
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+ This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the torgo dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.7808
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+ - Wer: 0.5966
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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: 8
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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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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Wer |
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+ |:-------------:|:-----:|:-----:|:---------------:|:------:|
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+ | 3.3568 | 0.45 | 500 | 3.2373 | 1.0 |
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+ | 2.6662 | 0.89 | 1000 | 1.9097 | 0.9707 |
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+ | 1.4433 | 1.34 | 1500 | 1.8284 | 0.8819 |
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+ | 1.0086 | 1.79 | 2000 | 1.5207 | 0.8233 |
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+ | 0.8253 | 2.24 | 2500 | 1.5046 | 0.7888 |
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+ | 0.7438 | 2.68 | 3000 | 1.4814 | 0.7457 |
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+ | 0.6364 | 3.13 | 3500 | 1.5690 | 0.7448 |
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+ | 0.593 | 3.58 | 4000 | 1.7702 | 0.7293 |
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+ | 0.555 | 4.03 | 4500 | 1.5775 | 0.7078 |
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+ | 0.479 | 4.47 | 5000 | 1.4384 | 0.7026 |
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+ | 0.4735 | 4.92 | 5500 | 1.6368 | 0.6940 |
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+ | 0.4284 | 5.37 | 6000 | 1.9717 | 0.6879 |
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+ | 0.429 | 5.81 | 6500 | 1.4882 | 0.6578 |
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+ | 0.4128 | 6.26 | 7000 | 1.4697 | 0.6664 |
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+ | 0.3869 | 6.71 | 7500 | 1.5555 | 0.6647 |
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+ | 0.3725 | 7.16 | 8000 | 1.7755 | 0.6664 |
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+ | 0.3104 | 7.6 | 8500 | 1.4753 | 0.65 |
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+ | 0.3355 | 8.05 | 9000 | 1.5212 | 0.6526 |
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+ | 0.3119 | 8.5 | 9500 | 1.6810 | 0.6345 |
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+ | 0.3186 | 8.94 | 10000 | 2.2611 | 0.6534 |
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+ | 0.3065 | 9.39 | 10500 | 1.2431 | 0.6397 |
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+ | 0.2862 | 9.84 | 11000 | 1.5408 | 0.6371 |
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+ | 0.2628 | 10.29 | 11500 | 2.0784 | 0.6474 |
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+ | 0.2792 | 10.73 | 12000 | 1.6698 | 0.6293 |
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+ | 0.2944 | 11.18 | 12500 | 1.7610 | 0.6336 |
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+ | 0.237 | 11.63 | 13000 | 1.5215 | 0.6371 |
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+ | 0.2471 | 12.08 | 13500 | 1.6935 | 0.65 |
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+ | 0.2154 | 12.52 | 14000 | 1.7569 | 0.6319 |
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+ | 0.2367 | 12.97 | 14500 | 1.6718 | 0.6284 |
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+ | 0.2499 | 13.42 | 15000 | 1.4102 | 0.6302 |
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+ | 0.2306 | 13.86 | 15500 | 1.5523 | 0.6147 |
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+ | 0.2337 | 14.31 | 16000 | 1.8419 | 0.6129 |
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+ | 0.206 | 14.76 | 16500 | 1.5285 | 0.6069 |
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+ | 0.2098 | 15.21 | 17000 | 1.7365 | 0.6198 |
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+ | 0.1994 | 15.65 | 17500 | 1.6756 | 0.6052 |
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+ | 0.1943 | 16.1 | 18000 | 1.9950 | 0.6241 |
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+ | 0.2077 | 16.55 | 18500 | 1.6966 | 0.6121 |
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+ | 0.2135 | 16.99 | 19000 | 1.7379 | 0.6233 |
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+ | 0.1721 | 17.44 | 19500 | 2.1036 | 0.6147 |
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+ | 0.1876 | 17.89 | 20000 | 1.7061 | 0.6129 |
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+ | 0.187 | 18.34 | 20500 | 1.7549 | 0.5897 |
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+ | 0.1699 | 18.78 | 21000 | 1.7215 | 0.6009 |
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+ | 0.1833 | 19.23 | 21500 | 1.5892 | 0.6069 |
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+ | 0.1545 | 19.68 | 22000 | 1.7657 | 0.6155 |
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+ | 0.1624 | 20.13 | 22500 | 1.5143 | 0.6103 |
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+ | 0.1631 | 20.57 | 23000 | 1.4752 | 0.5974 |
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+ | 0.1476 | 21.02 | 23500 | 1.5408 | 0.6 |
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+ | 0.141 | 21.47 | 24000 | 1.7880 | 0.6112 |
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+ | 0.1617 | 21.91 | 24500 | 1.7662 | 0.6190 |
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+ | 0.1435 | 22.36 | 25000 | 1.8012 | 0.6034 |
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+ | 0.1456 | 22.81 | 25500 | 1.7721 | 0.6052 |
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+ | 0.147 | 23.26 | 26000 | 1.6913 | 0.6 |
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+ | 0.137 | 23.7 | 26500 | 1.8138 | 0.5983 |
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+ | 0.1269 | 24.15 | 27000 | 2.0274 | 0.5974 |
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+ | 0.1203 | 24.6 | 27500 | 1.8193 | 0.5879 |
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+ | 0.1321 | 25.04 | 28000 | 1.7929 | 0.5853 |
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+ | 0.1333 | 25.49 | 28500 | 1.9791 | 0.6017 |
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+ | 0.1061 | 25.94 | 29000 | 1.7340 | 0.5957 |
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+ | 0.1099 | 26.39 | 29500 | 1.7547 | 0.6017 |
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+ | 0.111 | 26.83 | 30000 | 1.7777 | 0.5991 |
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+ | 0.1239 | 27.28 | 30500 | 1.7383 | 0.5991 |
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+ | 0.1179 | 27.73 | 31000 | 1.8627 | 0.6009 |
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+ | 0.0995 | 28.18 | 31500 | 1.7775 | 0.5966 |
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+ | 0.0924 | 28.62 | 32000 | 1.8651 | 0.5966 |
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+ | 0.1144 | 29.07 | 32500 | 1.7918 | 0.5974 |
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+ | 0.1082 | 29.52 | 33000 | 1.7697 | 0.5957 |
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+ | 0.1008 | 29.96 | 33500 | 1.7808 | 0.5966 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.26.1
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+ - Pytorch 2.1.0+cu118
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+ - Datasets 2.15.0
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+ - Tokenizers 0.13.3