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--- |
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tags: |
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- automatic-speech-recognition |
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- timit_asr |
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- generated_from_trainer |
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datasets: |
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- timit_asr |
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model-index: |
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- name: wav2vec2-base-repro-timit |
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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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# wav2vec2-base-repro-timit |
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This model is a fine-tuned version of [patrickvonplaten/wav2vec2-base-repro-960h-libri-85k-steps](https://huggingface.co/patrickvonplaten/wav2vec2-base-repro-960h-libri-85k-steps) on the TIMIT_ASR - NA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8562 |
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- Wer: 0.5484 |
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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: 0.0001 |
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- train_batch_size: 32 |
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- eval_batch_size: 1 |
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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: 20.0 |
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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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| 5.9793 | 0.69 | 100 | 5.4532 | 1.0 | |
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| 2.9066 | 1.38 | 200 | 2.9070 | 1.0 | |
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| 2.2562 | 2.07 | 300 | 2.0323 | 1.0 | |
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| 1.5273 | 2.76 | 400 | 1.1510 | 0.8001 | |
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| 1.1085 | 3.45 | 500 | 0.9521 | 0.7053 | |
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| 0.813 | 4.14 | 600 | 0.8617 | 0.6702 | |
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| 0.8434 | 4.83 | 700 | 0.8068 | 0.6393 | |
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| 0.9631 | 5.52 | 800 | 0.7863 | 0.6248 | |
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| 0.707 | 6.21 | 900 | 0.7476 | 0.5973 | |
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| 0.5568 | 6.9 | 1000 | 0.7350 | 0.5911 | |
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| 0.6171 | 7.59 | 1100 | 0.7171 | 0.5841 | |
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| 0.7011 | 8.28 | 1200 | 0.7318 | 0.5798 | |
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| 0.5546 | 8.97 | 1300 | 0.7447 | 0.5767 | |
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| 0.4278 | 9.66 | 1400 | 0.7481 | 0.5650 | |
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| 0.3576 | 10.34 | 1500 | 0.7443 | 0.5713 | |
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| 0.5506 | 11.03 | 1600 | 0.7574 | 0.5664 | |
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| 0.4127 | 11.72 | 1700 | 0.8043 | 0.5631 | |
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| 0.3251 | 12.41 | 1800 | 0.7738 | 0.5550 | |
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| 0.3119 | 13.1 | 1900 | 0.7829 | 0.5516 | |
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| 0.4371 | 13.79 | 2000 | 0.8025 | 0.5556 | |
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| 0.3772 | 14.48 | 2100 | 0.8451 | 0.5559 | |
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| 0.2942 | 15.17 | 2200 | 0.8300 | 0.5556 | |
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| 0.2503 | 15.86 | 2300 | 0.8417 | 0.5541 | |
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| 0.3671 | 16.55 | 2400 | 0.8568 | 0.5528 | |
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| 0.3867 | 17.24 | 2500 | 0.8521 | 0.5510 | |
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| 0.2614 | 17.93 | 2600 | 0.8479 | 0.5523 | |
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| 0.2441 | 18.62 | 2700 | 0.8558 | 0.5494 | |
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| 0.3059 | 19.31 | 2800 | 0.8553 | 0.5474 | |
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| 0.3734 | 20.0 | 2900 | 0.8562 | 0.5484 | |
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### Framework versions |
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- Transformers 4.12.0.dev0 |
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- Pytorch 1.8.1 |
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- Datasets 1.14.1.dev0 |
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- Tokenizers 0.10.3 |
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