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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: wav2vec_asr_swbd |
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results: [] |
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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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# wav2vec_asr_swbd |
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This model is a fine-tuned version of [facebook/wav2vec2-large-robust-ft-swbd-300h](https://huggingface.co/facebook/wav2vec2-large-robust-ft-swbd-300h) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3052 |
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- Wer: 0.5302 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0004 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 20 |
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- total_train_batch_size: 80 |
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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: 500 |
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- num_epochs: 20 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 1.5445 | 0.29 | 500 | 0.9114 | 0.6197 | |
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| 0.9397 | 0.58 | 1000 | 0.5057 | 0.5902 | |
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| 0.8557 | 0.86 | 1500 | 0.4465 | 0.6264 | |
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| 0.7716 | 1.15 | 2000 | 0.4182 | 0.5594 | |
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| 0.7659 | 1.44 | 2500 | 0.4111 | 0.7048 | |
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| 0.7406 | 1.73 | 3000 | 0.3927 | 0.5944 | |
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| 0.6857 | 2.02 | 3500 | 0.3852 | 0.7118 | |
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| 0.7113 | 2.31 | 4000 | 0.3775 | 0.5608 | |
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| 0.6804 | 2.59 | 4500 | 0.3885 | 0.5759 | |
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| 0.6654 | 2.88 | 5000 | 0.3703 | 0.7226 | |
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| 0.6569 | 3.17 | 5500 | 0.3688 | 0.5972 | |
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| 0.6335 | 3.46 | 6000 | 0.3661 | 0.7278 | |
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| 0.6309 | 3.75 | 6500 | 0.3579 | 0.6324 | |
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| 0.6231 | 4.03 | 7000 | 0.3620 | 0.5770 | |
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| 0.6171 | 4.32 | 7500 | 0.3640 | 0.5772 | |
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| 0.6191 | 4.61 | 8000 | 0.3553 | 0.6075 | |
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| 0.6142 | 4.9 | 8500 | 0.3543 | 0.6126 | |
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| 0.5905 | 5.19 | 9000 | 0.3601 | 0.6319 | |
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| 0.5846 | 5.48 | 9500 | 0.3429 | 0.7343 | |
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| 0.5874 | 5.76 | 10000 | 0.3429 | 0.5962 | |
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| 0.5768 | 6.05 | 10500 | 0.3381 | 0.7410 | |
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| 0.5783 | 6.34 | 11000 | 0.3391 | 0.5823 | |
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| 0.5835 | 6.63 | 11500 | 0.3447 | 0.5821 | |
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| 0.5817 | 6.92 | 12000 | 0.3314 | 0.6890 | |
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| 0.5459 | 7.2 | 12500 | 0.3363 | 0.5727 | |
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| 0.5575 | 7.49 | 13000 | 0.3363 | 0.7387 | |
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| 0.5505 | 7.78 | 13500 | 0.3368 | 0.5685 | |
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| 0.55 | 8.07 | 14000 | 0.3330 | 0.5587 | |
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| 0.5523 | 8.36 | 14500 | 0.3338 | 0.5484 | |
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| 0.5116 | 8.65 | 15000 | 0.3350 | 0.4351 | |
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| 0.5263 | 8.93 | 15500 | 0.3254 | 0.6235 | |
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| 0.5265 | 9.22 | 16000 | 0.3297 | 0.6207 | |
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| 0.5265 | 9.51 | 16500 | 0.3279 | 0.6143 | |
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| 0.5172 | 9.8 | 17000 | 0.3260 | 0.5800 | |
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| 0.5028 | 10.09 | 17500 | 0.3259 | 0.5774 | |
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| 0.5062 | 10.37 | 18000 | 0.3259 | 0.5552 | |
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| 0.5112 | 10.66 | 18500 | 0.3201 | 0.6625 | |
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| 0.5149 | 10.95 | 19000 | 0.3184 | 0.6865 | |
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| 0.4939 | 11.24 | 19500 | 0.3152 | 0.6116 | |
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| 0.5065 | 11.53 | 20000 | 0.3172 | 0.5246 | |
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| 0.5129 | 11.82 | 20500 | 0.3129 | 0.5908 | |
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| 0.4909 | 12.1 | 21000 | 0.3152 | 0.6075 | |
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| 0.4865 | 12.39 | 21500 | 0.3160 | 0.5037 | |
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| 0.4805 | 12.68 | 22000 | 0.3139 | 0.5458 | |
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| 0.4691 | 12.97 | 22500 | 0.3225 | 0.5815 | |
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| 0.4534 | 13.26 | 23000 | 0.3168 | 0.5614 | |
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| 0.4661 | 13.54 | 23500 | 0.3135 | 0.6053 | |
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| 0.4636 | 13.83 | 24000 | 0.3120 | 0.5142 | |
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| 0.4554 | 14.12 | 24500 | 0.3127 | 0.5552 | |
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| 0.4602 | 14.41 | 25000 | 0.3117 | 0.5562 | |
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| 0.4521 | 14.7 | 25500 | 0.3106 | 0.4995 | |
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| 0.4369 | 14.99 | 26000 | 0.3100 | 0.5663 | |
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| 0.4249 | 15.27 | 26500 | 0.3110 | 0.5262 | |
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| 0.4321 | 15.56 | 27000 | 0.3106 | 0.5183 | |
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| 0.4293 | 15.85 | 27500 | 0.3091 | 0.5311 | |
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| 0.4537 | 16.14 | 28000 | 0.3134 | 0.4986 | |
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| 0.4258 | 16.43 | 28500 | 0.3138 | 0.4487 | |
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| 0.4347 | 16.71 | 29000 | 0.3091 | 0.5011 | |
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| 0.4615 | 17.0 | 29500 | 0.3068 | 0.5616 | |
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| 0.4163 | 17.29 | 30000 | 0.3115 | 0.5426 | |
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| 0.4074 | 17.58 | 30500 | 0.3079 | 0.5341 | |
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| 0.4121 | 17.87 | 31000 | 0.3047 | 0.5619 | |
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| 0.4219 | 18.16 | 31500 | 0.3085 | 0.5051 | |
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| 0.4049 | 18.44 | 32000 | 0.3084 | 0.5116 | |
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| 0.4119 | 18.73 | 32500 | 0.3071 | 0.5028 | |
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| 0.4129 | 19.02 | 33000 | 0.3064 | 0.5030 | |
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| 0.4143 | 19.31 | 33500 | 0.3040 | 0.5086 | |
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| 0.4013 | 19.6 | 34000 | 0.3057 | 0.5271 | |
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| 0.4162 | 19.88 | 34500 | 0.3052 | 0.5302 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 1.18.4 |
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- Tokenizers 0.11.6 |
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