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ctaguchi
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ssc-kcn-model

Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
Model card Files
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  2. model.safetensors +1 -1
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: facebook/wav2vec2-xls-r-300m
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - wer
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+ model-index:
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+ - name: ssc-kcn-model
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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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+ # ssc-kcn-model
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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 an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: nan
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+ - Cer: 0.9940
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+ - Wer: 1.0
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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: 0.0003
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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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+ - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 16
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 100
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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 | Wer |
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+ |:-------------:|:-------:|:-----:|:---------------:|:------:|:---:|
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+ | 5.3972 | 0.1736 | 100 | 2.9985 | 0.9942 | 1.0 |
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+ | 3.1473 | 0.3472 | 200 | 2.9632 | 0.9942 | 1.0 |
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+ | 3.2868 | 0.5208 | 300 | 2.9640 | 0.9942 | 1.0 |
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+ | 3.1277 | 0.6944 | 400 | 2.8740 | 0.9942 | 1.0 |
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+ | 3.1015 | 0.8681 | 500 | 2.8538 | 0.9942 | 1.0 |
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+ | 3.0851 | 1.0417 | 600 | 2.9601 | 0.9942 | 1.0 |
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+ | 3.0431 | 1.2153 | 700 | 2.8703 | 0.9942 | 1.0 |
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+ | 3.0662 | 1.3889 | 800 | 3.0285 | 0.9942 | 1.0 |
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+ | 3.0358 | 1.5625 | 900 | 2.9641 | 0.9942 | 1.0 |
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+ | 2.9999 | 1.7361 | 1000 | 3.0095 | 0.9942 | 1.0 |
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+ | 3.0629 | 1.9097 | 1100 | 2.8662 | 0.9942 | 1.0 |
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+ | 3.033 | 2.0833 | 1200 | 2.8406 | 0.9942 | 1.0 |
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+ | 2.9814 | 2.2569 | 1300 | 2.8291 | 0.9942 | 1.0 |
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+ | 3.0079 | 2.4306 | 1400 | 2.8602 | 0.9942 | 1.0 |
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+ | 3.0814 | 2.6042 | 1500 | 2.8299 | 0.9942 | 1.0 |
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+ | 3.0949 | 2.7778 | 1600 | 2.8245 | 0.9942 | 1.0 |
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+ | 3.1026 | 2.9514 | 1700 | 2.8080 | 0.9942 | 1.0 |
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+ | 3.0229 | 3.125 | 1800 | 2.9045 | 0.9942 | 1.0 |
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+ | 3.0744 | 3.2986 | 1900 | 2.8780 | 0.9942 | 1.0 |
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+ | 3.0559 | 3.4722 | 2000 | 2.9353 | 0.9942 | 1.0 |
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+ | 3.0577 | 3.6458 | 2100 | 2.9453 | 0.9942 | 1.0 |
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+ | 3.0844 | 3.8194 | 2200 | 3.0902 | 0.9942 | 1.0 |
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+ | 3.2003 | 3.9931 | 2300 | 2.9683 | 0.9942 | 1.0 |
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+ | 3.233 | 4.1667 | 2400 | 3.2298 | 0.9942 | 1.0 |
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+ | 3.4188 | 4.3403 | 2500 | 3.3186 | 0.9942 | 1.0 |
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+ | 3.5046 | 4.5139 | 2600 | 3.3847 | 0.9942 | 1.0 |
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+ | 3.5387 | 4.6875 | 2700 | 3.4096 | 0.9942 | 1.0 |
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+ | 3.517 | 4.8611 | 2800 | 3.4097 | 0.9942 | 1.0 |
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+ | 3.5483 | 5.0347 | 2900 | 3.4097 | 0.9942 | 1.0 |
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+ | 3.5612 | 5.2083 | 3000 | 3.4097 | 0.9942 | 1.0 |
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+ | 3.5469 | 5.3819 | 3100 | 3.4097 | 0.9942 | 1.0 |
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+ | 3.5421 | 5.5556 | 3200 | 3.4097 | 0.9942 | 1.0 |
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+ | 3.5186 | 5.9028 | 3400 | 3.4097 | 0.9942 | 1.0 |
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+ | 3.5325 | 6.0764 | 3500 | 3.4097 | 0.9942 | 1.0 |
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+ | 3.5384 | 6.25 | 3600 | 3.4097 | 0.9942 | 1.0 |
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+ | 3.5326 | 6.4236 | 3700 | 3.4097 | 0.9942 | 1.0 |
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+ | 3.513 | 6.5972 | 3800 | 3.4097 | 0.9942 | 1.0 |
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+ | 3.5751 | 6.7708 | 3900 | 3.4097 | 0.9942 | 1.0 |
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+ | 3.5294 | 6.9444 | 4000 | 3.4097 | 0.9942 | 1.0 |
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+ | 3.545 | 7.1181 | 4100 | 3.4097 | 0.9942 | 1.0 |
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+ | 3.5764 | 7.2917 | 4200 | 3.4097 | 0.9942 | 1.0 |
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+ | 3.5258 | 7.4653 | 4300 | 3.4097 | 0.9942 | 1.0 |
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+ | 3.5538 | 7.8125 | 4500 | 3.4097 | 0.9942 | 1.0 |
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+ | 3.5278 | 10.2431 | 5900 | 3.4097 | 0.9942 | 1.0 |
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+ | 6.9849 | 20.1389 | 11600 | nan | 0.9940 | 1.0 |
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+ | 0.0 | 27.7778 | 16000 | nan | 0.9940 | 1.0 |
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+ | 0.0 | 27.9514 | 16100 | nan | 0.9940 | 1.0 |
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+ | 0.0 | 28.125 | 16200 | nan | 0.9940 | 1.0 |
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+ | 0.0 | 28.2986 | 16300 | nan | 0.9940 | 1.0 |
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+ | 0.0 | 28.4722 | 16400 | nan | 0.9940 | 1.0 |
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+ | 0.0 | 28.6458 | 16500 | nan | 0.9940 | 1.0 |
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+ | 0.0 | 28.8194 | 16600 | nan | 0.9940 | 1.0 |
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+ | 0.0 | 28.9931 | 16700 | nan | 0.9940 | 1.0 |
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+ | 0.0 | 29.1667 | 16800 | nan | 0.9940 | 1.0 |
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+ | 0.0 | 29.3403 | 16900 | nan | 0.9940 | 1.0 |
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+ | 0.0 | 29.5139 | 17000 | nan | 0.9940 | 1.0 |
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+ | 0.0 | 29.6875 | 17100 | nan | 0.9940 | 1.0 |
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+ | 0.0 | 29.8611 | 17200 | nan | 0.9940 | 1.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.57.2
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+ - Pytorch 2.9.1+cu128
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+ - Datasets 3.6.0
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+ - Tokenizers 0.22.0
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