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wav2vec2-base-ms-with-lm

Automatic Speech Recognition
Transformers
PyTorch
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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+ metrics:
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+ - wer
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+ model-index:
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+ - name: wav2vec2-base-ms-with-lm
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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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+ # wav2vec2-base-ms-with-lm
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+
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+ This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.1256
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+ - Wer: 0.3991
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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: 4
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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: 100
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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 | Wer |
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+ |:-------------:|:-----:|:-----:|:---------------:|:------:|
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+ | 4.0672 | 1.85 | 500 | 2.9666 | 1.0 |
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+ | 1.6566 | 3.7 | 1000 | 0.7862 | 0.6177 |
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+ | 0.6424 | 5.56 | 1500 | 0.6600 | 0.5478 |
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+ | 0.4438 | 7.41 | 2000 | 0.6148 | 0.4835 |
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+ | 0.3133 | 9.26 | 2500 | 0.6949 | 0.4516 |
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+ | 0.2822 | 11.11 | 3000 | 0.6844 | 0.4533 |
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+ | 0.2338 | 12.96 | 3500 | 0.7682 | 0.4634 |
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+ | 0.2246 | 14.81 | 4000 | 0.8370 | 0.4925 |
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+ | 0.1957 | 16.67 | 4500 | 0.8554 | 0.4377 |
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+ | 0.1809 | 18.52 | 5000 | 0.7211 | 0.4505 |
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+ | 0.1496 | 20.37 | 5500 | 0.8081 | 0.4354 |
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+ | 0.1369 | 22.22 | 6000 | 0.9099 | 0.4360 |
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+ | 0.1285 | 24.07 | 6500 | 0.8051 | 0.4231 |
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+ | 0.1174 | 25.93 | 7000 | 0.9041 | 0.4505 |
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+ | 0.1074 | 27.78 | 7500 | 0.8096 | 0.4310 |
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+ | 0.0904 | 29.63 | 8000 | 0.8589 | 0.4237 |
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+ | 0.0975 | 31.48 | 8500 | 0.9019 | 0.4142 |
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+ | 0.0806 | 33.33 | 9000 | 0.8966 | 0.4382 |
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+ | 0.0772 | 35.19 | 9500 | 1.0612 | 0.4181 |
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+ | 0.0738 | 37.04 | 10000 | 0.8979 | 0.4215 |
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+ | 0.0678 | 38.89 | 10500 | 0.9342 | 0.4103 |
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+ | 0.0626 | 40.74 | 11000 | 0.9992 | 0.4187 |
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+ | 0.0641 | 42.59 | 11500 | 1.0101 | 0.4120 |
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+ | 0.0565 | 44.44 | 12000 | 0.9841 | 0.4287 |
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+ | 0.0535 | 46.3 | 12500 | 1.0049 | 0.4097 |
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+ | 0.0512 | 48.15 | 13000 | 0.9569 | 0.4080 |
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+ | 0.0466 | 50.0 | 13500 | 0.9863 | 0.4276 |
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+ | 0.0514 | 51.85 | 14000 | 0.9594 | 0.4120 |
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+ | 0.0397 | 53.7 | 14500 | 0.9775 | 0.4103 |
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+ | 0.0394 | 55.56 | 15000 | 1.0077 | 0.4131 |
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+ | 0.0393 | 57.41 | 15500 | 0.9763 | 0.4108 |
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+ | 0.0355 | 59.26 | 16000 | 1.1432 | 0.4226 |
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+ | 0.0323 | 61.11 | 16500 | 1.1553 | 0.4192 |
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+ | 0.0344 | 62.96 | 17000 | 1.0437 | 0.4203 |
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+ | 0.0296 | 64.81 | 17500 | 1.0227 | 0.4209 |
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+ | 0.0267 | 66.67 | 18000 | 1.0408 | 0.4287 |
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+ | 0.0283 | 68.52 | 18500 | 1.0811 | 0.4192 |
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+ | 0.0243 | 70.37 | 19000 | 1.0697 | 0.4075 |
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+ | 0.0238 | 72.22 | 19500 | 1.1047 | 0.4164 |
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+ | 0.0234 | 74.07 | 20000 | 1.1585 | 0.4142 |
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+ | 0.02 | 75.93 | 20500 | 1.1183 | 0.4187 |
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+ | 0.0231 | 77.78 | 21000 | 1.0923 | 0.4080 |
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+ | 0.018 | 79.63 | 21500 | 1.1299 | 0.4058 |
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+ | 0.0179 | 81.48 | 22000 | 1.0963 | 0.4008 |
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+ | 0.0152 | 83.33 | 22500 | 1.0676 | 0.4002 |
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+ | 0.0142 | 85.19 | 23000 | 1.1026 | 0.4047 |
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+ | 0.0159 | 87.04 | 23500 | 1.0876 | 0.4058 |
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+ | 0.0144 | 88.89 | 24000 | 1.0943 | 0.3963 |
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+ | 0.0152 | 90.74 | 24500 | 1.0827 | 0.4075 |
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+ | 0.0155 | 92.59 | 25000 | 1.0982 | 0.4019 |
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+ | 0.0124 | 94.44 | 25500 | 1.1284 | 0.3985 |
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+ | 0.0132 | 96.3 | 26000 | 1.1233 | 0.3991 |
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+ | 0.0109 | 98.15 | 26500 | 1.1196 | 0.3980 |
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+ | 0.0102 | 100.0 | 27000 | 1.1256 | 0.3991 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.24.0
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+ - Pytorch 2.0.0+cu118
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+ - Datasets 1.18.3
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+ - Tokenizers 0.13.3