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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: unispeech-large-1500h-cv-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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# unispeech-large-1500h-cv-timit |
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This model is a fine-tuned version of [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) on the TIMIT_ASR - NA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3099 |
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- Wer: 0.2196 |
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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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| 4.64 | 0.69 | 100 | 3.9717 | 0.9981 | |
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| 2.6793 | 1.38 | 200 | 2.6264 | 1.0 | |
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| 1.2221 | 2.07 | 300 | 0.9999 | 0.7167 | |
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| 0.9009 | 2.76 | 400 | 0.6509 | 0.5570 | |
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| 0.4352 | 3.45 | 500 | 0.4682 | 0.4332 | |
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| 0.227 | 4.14 | 600 | 0.3661 | 0.3565 | |
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| 0.2169 | 4.83 | 700 | 0.3244 | 0.3203 | |
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| 0.2687 | 5.52 | 800 | 0.3137 | 0.2981 | |
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| 0.127 | 6.21 | 900 | 0.3220 | 0.2828 | |
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| 0.0922 | 6.9 | 1000 | 0.3075 | 0.2708 | |
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| 0.0965 | 7.59 | 1100 | 0.2779 | 0.2576 | |
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| 0.1298 | 8.28 | 1200 | 0.3111 | 0.2480 | |
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| 0.0855 | 8.97 | 1300 | 0.3021 | 0.2421 | |
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| 0.0629 | 9.66 | 1400 | 0.3122 | 0.2511 | |
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| 0.0471 | 10.34 | 1500 | 0.2965 | 0.2368 | |
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| 0.0871 | 11.03 | 1600 | 0.3247 | 0.2387 | |
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| 0.0503 | 11.72 | 1700 | 0.3359 | 0.2363 | |
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| 0.0402 | 12.41 | 1800 | 0.2976 | 0.2332 | |
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| 0.0336 | 13.1 | 1900 | 0.3139 | 0.2321 | |
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| 0.0634 | 13.79 | 2000 | 0.3188 | 0.2309 | |
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| 0.0429 | 14.48 | 2100 | 0.3145 | 0.2335 | |
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| 0.028 | 15.17 | 2200 | 0.3244 | 0.2242 | |
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| 0.0255 | 15.86 | 2300 | 0.2914 | 0.2196 | |
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| 0.0406 | 16.55 | 2400 | 0.3249 | 0.2202 | |
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| 0.0512 | 17.24 | 2500 | 0.3037 | 0.2198 | |
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| 0.0269 | 17.93 | 2600 | 0.3218 | 0.2242 | |
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| 0.0287 | 18.62 | 2700 | 0.3106 | 0.2185 | |
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| 0.0319 | 19.31 | 2800 | 0.3124 | 0.2217 | |
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| 0.0494 | 20.0 | 2900 | 0.3099 | 0.2196 | |
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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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