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--- |
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license: apache-2.0 |
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tags: |
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- speech-recognition |
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- librispeech_asr |
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- generated_from_trainer |
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model-index: |
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- name: wav2vec2-librispeech-clean-100h-demo-dist |
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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-librispeech-clean-100h-demo-dist |
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This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the LIBRISPEECH_ASR - CLEAN dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0572 |
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- Wer: 0.0417 |
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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: 0.0003 |
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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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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- total_train_batch_size: 32 |
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- total_eval_batch_size: 64 |
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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: 3.0 |
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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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| 3.399 | 0.11 | 100 | 3.6153 | 1.0 | |
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| 2.8892 | 0.22 | 200 | 2.8963 | 1.0 | |
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| 2.8284 | 0.34 | 300 | 2.8574 | 1.0 | |
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| 0.7347 | 0.45 | 400 | 0.6158 | 0.4850 | |
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| 0.1138 | 0.56 | 500 | 0.2038 | 0.1560 | |
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| 0.248 | 0.67 | 600 | 0.1274 | 0.1024 | |
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| 0.2586 | 0.78 | 700 | 0.1108 | 0.0876 | |
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| 0.0733 | 0.9 | 800 | 0.0936 | 0.0762 | |
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| 0.044 | 1.01 | 900 | 0.0834 | 0.0662 | |
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| 0.0393 | 1.12 | 1000 | 0.0792 | 0.0622 | |
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| 0.0941 | 1.23 | 1100 | 0.0769 | 0.0627 | |
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| 0.036 | 1.35 | 1200 | 0.0731 | 0.0603 | |
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| 0.0768 | 1.46 | 1300 | 0.0713 | 0.0559 | |
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| 0.0518 | 1.57 | 1400 | 0.0686 | 0.0537 | |
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| 0.0815 | 1.68 | 1500 | 0.0639 | 0.0515 | |
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| 0.0603 | 1.79 | 1600 | 0.0636 | 0.0500 | |
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| 0.056 | 1.91 | 1700 | 0.0609 | 0.0480 | |
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| 0.0265 | 2.02 | 1800 | 0.0621 | 0.0465 | |
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| 0.0496 | 2.13 | 1900 | 0.0607 | 0.0449 | |
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| 0.0436 | 2.24 | 2000 | 0.0591 | 0.0446 | |
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| 0.0421 | 2.35 | 2100 | 0.0590 | 0.0428 | |
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| 0.0641 | 2.47 | 2200 | 0.0603 | 0.0443 | |
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| 0.0466 | 2.58 | 2300 | 0.0580 | 0.0429 | |
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| 0.0132 | 2.69 | 2400 | 0.0574 | 0.0423 | |
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| 0.0073 | 2.8 | 2500 | 0.0586 | 0.0417 | |
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| 0.0021 | 2.91 | 2600 | 0.0574 | 0.0412 | |
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### Framework versions |
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- Transformers 4.11.0.dev0 |
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- Pytorch 1.9.0+cu111 |
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- Datasets 1.12.1 |
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- Tokenizers 0.10.3 |
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