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ctaguchi
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ssc-hch-model

Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
Model card Files
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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-hch-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-hch-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: 2.4915
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+ - Cer: 0.7560
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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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+ | 5.8531 | 0.2506 | 100 | 3.0776 | 0.9917 | 1.0 |
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+ | 3.1404 | 0.5013 | 200 | 2.9427 | 0.9917 | 1.0 |
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+ | 3.5462 | 0.7519 | 300 | 3.0055 | 0.9917 | 1.0 |
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+ | 3.3231 | 1.0025 | 400 | 2.9823 | 0.9917 | 1.0 |
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+ | 3.2095 | 1.2531 | 500 | 2.9268 | 0.9917 | 1.0 |
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+ | 3.1251 | 1.5038 | 600 | 2.9311 | 0.9917 | 1.0 |
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+ | 3.1566 | 1.7544 | 700 | 2.9446 | 0.9917 | 1.0 |
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+ | 3.1212 | 2.0050 | 800 | 2.9112 | 0.9917 | 1.0 |
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+ | 3.0708 | 2.2556 | 900 | 2.9260 | 0.9829 | 1.0 |
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+ | 3.0319 | 2.5063 | 1000 | 3.8026 | 0.9829 | 1.0 |
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+ | 3.0086 | 2.7569 | 1100 | 3.3550 | 0.9829 | 1.0 |
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+ | 2.9547 | 3.0075 | 1200 | 2.9463 | 0.9832 | 1.0 |
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+ | 2.8971 | 3.2581 | 1300 | 3.1363 | 0.9661 | 1.0 |
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+ | 2.8063 | 3.5088 | 1400 | 2.9367 | 0.9473 | 1.0 |
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+ | 2.7296 | 3.7594 | 1500 | 3.0035 | 0.8580 | 1.0 |
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+ | 2.5223 | 4.0100 | 1600 | 2.4125 | 0.7232 | 1.0 |
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+ | 2.3708 | 4.2607 | 1700 | 2.4144 | 0.6792 | 1.0 |
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+ | 2.3276 | 4.5113 | 1800 | 2.3535 | 0.6109 | 1.0 |
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+ | 2.2372 | 4.7619 | 1900 | 2.1567 | 0.5972 | 1.0 |
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+ | 2.136 | 5.0125 | 2000 | 2.2649 | 0.5880 | 0.9996 |
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+ | 2.0764 | 5.2632 | 2100 | 2.0068 | 0.5701 | 1.0 |
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+ | 2.0301 | 5.5138 | 2200 | 1.9794 | 0.5818 | 1.0 |
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+ | 1.9486 | 5.7644 | 2300 | 2.0473 | 0.5687 | 0.9995 |
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+ | 1.9485 | 6.0150 | 2400 | 1.9562 | 0.5288 | 1.0 |
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+ | 1.8988 | 6.2657 | 2500 | 1.8791 | 0.5423 | 1.0 |
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+ | 1.9147 | 6.5163 | 2600 | 1.8219 | 0.5453 | 1.0 |
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+ | 1.8067 | 6.7669 | 2700 | 1.8768 | 0.5321 | 0.9998 |
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+ | 1.7998 | 7.0175 | 2800 | 1.9343 | 0.5243 | 0.9996 |
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+ | 1.7891 | 7.2682 | 2900 | 1.8222 | 0.5198 | 0.9998 |
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+ | 1.7434 | 7.5188 | 3000 | 1.8008 | 0.5444 | 1.0 |
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+ | 1.7674 | 7.7694 | 3100 | 1.9248 | 0.5005 | 0.9989 |
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+ | 1.708 | 8.0201 | 3200 | 1.7520 | 0.5447 | 0.9995 |
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+ | 1.7056 | 8.2707 | 3300 | 1.8185 | 0.5321 | 0.9995 |
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+ | 1.6462 | 8.5213 | 3400 | 1.7989 | 0.5222 | 0.9998 |
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+ | 1.6381 | 8.7719 | 3500 | 1.8162 | 0.5100 | 0.9986 |
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+ | 1.6466 | 9.0226 | 3600 | 1.7556 | 0.5289 | 1.0 |
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+ | 1.6018 | 9.2732 | 3700 | 1.6908 | 0.5010 | 0.9991 |
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+ | 1.606 | 9.5238 | 3800 | 1.6967 | 0.4951 | 0.9977 |
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+ | 1.6045 | 9.7744 | 3900 | 1.7846 | 0.5240 | 0.9982 |
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+ | 1.5963 | 10.0251 | 4000 | 1.7657 | 0.5304 | 0.9995 |
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+ | 1.5649 | 10.2757 | 4100 | 1.7817 | 0.5407 | 0.9995 |
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+ | 1.5553 | 10.5263 | 4200 | 1.7213 | 0.5265 | 0.9993 |
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+ | 1.5201 | 10.7769 | 4300 | 1.7401 | 0.5195 | 1.0 |
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+ | 1.5254 | 11.0276 | 4400 | 1.8063 | 0.4984 | 0.9998 |
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+ | 1.5068 | 11.2782 | 4500 | 1.8452 | 0.5403 | 1.0 |
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+ | 1.5342 | 11.5288 | 4600 | 2.0170 | 0.5464 | 0.9998 |
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+ | 1.7503 | 11.7794 | 4700 | 2.2264 | 0.5262 | 1.0 |
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+ | 1.8808 | 12.0301 | 4800 | 1.9860 | 0.5120 | 1.0 |
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+ | 1.9139 | 12.2807 | 4900 | 2.2726 | 0.5269 | 1.0 |
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+ | 1.9509 | 12.5313 | 5000 | 1.9673 | 0.5404 | 1.0 |
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+ | 2.0051 | 12.7820 | 5100 | 2.2250 | 0.5604 | 1.0 |
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+ | 1.9579 | 13.0326 | 5200 | 2.1640 | 0.5959 | 1.0 |
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+ | 1.8771 | 13.2832 | 5300 | 2.1336 | 0.5563 | 1.0 |
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+ | 1.9823 | 13.5338 | 5400 | 2.2691 | 0.7077 | 1.0 |
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+ | 2.1507 | 13.7845 | 5500 | 2.1997 | 0.6465 | 1.0 |
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+ | 2.0604 | 14.0351 | 5600 | 2.3030 | 0.8980 | 1.0 |
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+ | 2.1699 | 14.2857 | 5700 | 2.4337 | 0.8201 | 1.0 |
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+ | 2.2419 | 14.5363 | 5800 | 2.4167 | 0.9188 | 1.0 |
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+ | 2.2654 | 14.7870 | 5900 | 2.3791 | 0.9183 | 1.0 |
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+ | 2.2122 | 15.0376 | 6000 | 2.3923 | 0.8131 | 1.0 |
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+ | 2.1877 | 15.2882 | 6100 | 2.4024 | 0.7830 | 1.0 |
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+ | 2.1916 | 15.5388 | 6200 | 2.4181 | 0.8381 | 1.0 |
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+ | 2.1353 | 15.7895 | 6300 | 2.4418 | 0.8358 | 1.0 |
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+ | 2.1799 | 16.0401 | 6400 | 2.4687 | 0.8174 | 1.0 |
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+ | 2.2087 | 16.2907 | 6500 | 2.5634 | 0.8334 | 1.0 |
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+ | 2.2825 | 16.5414 | 6600 | 2.5488 | 0.8351 | 1.0 |
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+ | 2.3422 | 16.7920 | 6700 | 2.5349 | 0.8768 | 1.0 |
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+ | 2.3002 | 17.0426 | 6800 | 2.5182 | 0.8676 | 1.0 |
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+ | 2.3231 | 17.2932 | 6900 | 2.5707 | 0.8705 | 1.0 |
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+ | 2.3212 | 17.5439 | 7000 | 2.5670 | 0.8576 | 1.0 |
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+ | 2.294 | 17.7945 | 7100 | 2.5520 | 0.8529 | 1.0 |
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+ | 2.3093 | 18.0451 | 7200 | 2.5077 | 0.8755 | 1.0 |
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+ | 2.2777 | 18.2957 | 7300 | 2.5170 | 0.8419 | 1.0 |
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+ | 2.2669 | 18.5464 | 7400 | 2.5190 | 0.8236 | 1.0 |
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+ | 2.2838 | 18.7970 | 7500 | 2.4830 | 0.8454 | 1.0 |
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+ | 2.2629 | 19.0476 | 7600 | 2.4891 | 0.8255 | 1.0 |
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+ | 2.2429 | 19.2982 | 7700 | 2.4772 | 0.8228 | 1.0 |
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+ | 2.2664 | 19.5489 | 7800 | 2.4824 | 0.8192 | 1.0 |
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+ | 2.2216 | 19.7995 | 7900 | 2.4672 | 0.8239 | 1.0 |
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+ | 2.2353 | 20.0501 | 8000 | 2.4799 | 0.7995 | 1.0 |
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+ | 2.2275 | 20.3008 | 8100 | 2.4589 | 0.8008 | 1.0 |
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+ | 2.2194 | 20.5514 | 8200 | 2.4732 | 0.7896 | 1.0 |
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+ | 2.2165 | 20.8020 | 8300 | 2.4684 | 0.7895 | 1.0 |
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+ | 2.1849 | 21.0526 | 8400 | 2.4801 | 0.7780 | 1.0 |
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+ | 2.1829 | 21.3033 | 8500 | 2.4796 | 0.7700 | 1.0 |
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+ | 2.1967 | 21.5539 | 8600 | 2.4524 | 0.7785 | 1.0 |
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+ | 2.1703 | 21.8045 | 8700 | 2.4873 | 0.7599 | 1.0 |
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+ | 2.195 | 22.0551 | 8800 | 2.4935 | 0.7567 | 1.0 |
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+ | 2.2086 | 22.3058 | 8900 | 2.4447 | 0.7694 | 1.0 |
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+ | 2.1866 | 22.5564 | 9000 | 2.4866 | 0.7501 | 1.0 |
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+ | 2.1733 | 22.8070 | 9100 | 2.4694 | 0.7529 | 1.0 |
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+ | 2.1637 | 23.0576 | 9200 | 2.4896 | 0.7430 | 1.0 |
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+ | 2.1756 | 23.3083 | 9300 | 2.4785 | 0.7428 | 1.0 |
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+ | 2.1634 | 23.5589 | 9400 | 2.5084 | 0.7336 | 1.0 |
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+ | 2.1894 | 23.8095 | 9500 | 2.4891 | 0.7340 | 1.0 |
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+ | 2.178 | 24.0602 | 9600 | 2.4982 | 0.7314 | 1.0 |
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+ | 2.1725 | 24.3108 | 9700 | 2.4581 | 0.7392 | 1.0 |
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+ | 2.1795 | 24.5614 | 9800 | 2.4721 | 0.7328 | 1.0 |
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+ | 2.1679 | 24.8120 | 9900 | 2.4810 | 0.7300 | 1.0 |
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+ | 2.2202 | 25.0627 | 10000 | 2.4784 | 0.7240 | 1.0 |
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+ | 2.2071 | 25.3133 | 10100 | 2.5258 | 0.7131 | 1.0 |
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+ | 2.2473 | 25.5639 | 10200 | 2.5072 | 0.7158 | 1.0 |
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+ | 2.2474 | 25.8145 | 10300 | 2.5417 | 0.7117 | 1.0 |
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+ | 2.2283 | 26.0652 | 10400 | 2.5465 | 0.7092 | 1.0 |
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+ | 2.2543 | 26.3158 | 10500 | 2.5444 | 0.7102 | 1.0 |
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+ | 2.2358 | 26.5664 | 10600 | 2.5406 | 0.7119 | 1.0 |
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+ | 2.2889 | 26.8170 | 10700 | 2.4930 | 0.7231 | 1.0 |
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+ | 2.2624 | 27.0677 | 10800 | 2.5067 | 0.7179 | 1.0 |
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+ | 2.2619 | 27.3183 | 10900 | 2.4803 | 0.7338 | 1.0 |
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+ | 2.2208 | 27.5689 | 11000 | 2.4662 | 0.7391 | 1.0 |
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+ | 2.254 | 27.8195 | 11100 | 2.4554 | 0.7454 | 1.0 |
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+ | 2.2567 | 28.0702 | 11200 | 2.4682 | 0.7418 | 1.0 |
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+ | 2.2286 | 28.3208 | 11300 | 2.4771 | 0.7381 | 1.0 |
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+ | 2.2648 | 28.5714 | 11400 | 2.4754 | 0.7405 | 1.0 |
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+ | 2.2666 | 28.8221 | 11500 | 2.4745 | 0.7449 | 1.0 |
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+ | 2.2456 | 29.0727 | 11600 | 2.4793 | 0.7509 | 1.0 |
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+ | 2.2692 | 29.3233 | 11700 | 2.4868 | 0.7522 | 1.0 |
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+ | 2.2602 | 29.5739 | 11800 | 2.4893 | 0.7541 | 1.0 |
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+ | 2.2932 | 29.8246 | 11900 | 2.4915 | 0.7560 | 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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