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--- |
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language: en |
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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: unispeech-sat-large-timit-ft |
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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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# unispeech-sat-large-timit-ft |
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This model is a fine-tuned version of [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) on the TIMIT_ASR - NA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6074 |
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- Wer: 0.3880 |
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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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| 6.2516 | 0.69 | 100 | 5.8638 | 1.0 | |
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| 2.9596 | 1.38 | 200 | 2.9550 | 1.0 | |
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| 2.8831 | 2.07 | 300 | 2.8547 | 1.0 | |
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| 2.3223 | 2.76 | 400 | 2.2044 | 1.0063 | |
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| 1.2104 | 3.45 | 500 | 1.0845 | 0.7706 | |
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| 0.6779 | 4.14 | 600 | 0.7342 | 0.5663 | |
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| 0.6319 | 4.83 | 700 | 0.6054 | 0.4881 | |
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| 0.664 | 5.52 | 800 | 0.5808 | 0.4913 | |
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| 0.402 | 6.21 | 900 | 0.5647 | 0.4611 | |
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| 0.3176 | 6.9 | 1000 | 0.5211 | 0.4440 | |
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| 0.3392 | 7.59 | 1100 | 0.5187 | 0.4359 | |
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| 0.3888 | 8.28 | 1200 | 0.5501 | 0.4391 | |
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| 0.2874 | 8.97 | 1300 | 0.5249 | 0.4148 | |
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| 0.208 | 9.66 | 1400 | 0.5407 | 0.4152 | |
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| 0.1457 | 10.34 | 1500 | 0.5722 | 0.4155 | |
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| 0.2375 | 11.03 | 1600 | 0.5780 | 0.4059 | |
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| 0.2111 | 11.72 | 1700 | 0.5823 | 0.4094 | |
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| 0.1422 | 12.41 | 1800 | 0.5754 | 0.3977 | |
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| 0.125 | 13.1 | 1900 | 0.5784 | 0.4031 | |
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| 0.1996 | 13.79 | 2000 | 0.5630 | 0.3956 | |
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| 0.1747 | 14.48 | 2100 | 0.5880 | 0.3964 | |
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| 0.1263 | 15.17 | 2200 | 0.5987 | 0.3951 | |
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| 0.11 | 15.86 | 2300 | 0.5688 | 0.3964 | |
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| 0.1411 | 16.55 | 2400 | 0.6223 | 0.3906 | |
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| 0.1647 | 17.24 | 2500 | 0.6135 | 0.3960 | |
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| 0.1162 | 17.93 | 2600 | 0.6224 | 0.3960 | |
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| 0.098 | 18.62 | 2700 | 0.6017 | 0.3907 | |
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| 0.1183 | 19.31 | 2800 | 0.6121 | 0.3885 | |
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| 0.1717 | 20.0 | 2900 | 0.6074 | 0.3880 | |
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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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