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wav2vec2-2-bert-large-no-adapter

Automatic Speech Recognition
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speech-encoder-decoder
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sanchit-gandhi HF staff commited on
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update model card README.md

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  2. runs/Feb21_09-27-04_sanchit--v100/events.out.tfevents.1645435652.sanchit--v100.80427.0 +2 -2
README.md CHANGED
@@ -15,8 +15,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model was trained from scratch on the librispeech_asr dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 6.9553
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- - Wer: 2.0097
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  ## Model description
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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: 8
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  - eval_batch_size: 8
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  - seed: 42
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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- | 6.8818 | 0.28 | 500 | 7.0258 | 1.0 |
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- | 6.7558 | 0.56 | 1000 | 6.9731 | 1.0 |
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- | 6.587 | 0.84 | 1500 | 6.9588 | 2.0097 |
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- | 6.881 | 1.12 | 2000 | 6.9579 | 2.0097 |
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- | 6.9616 | 1.4 | 2500 | 6.9553 | 2.0097 |
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- | 6.8572 | 1.68 | 3000 | 6.9488 | 2.0097 |
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- | 6.8827 | 1.96 | 3500 | 6.9496 | 2.0097 |
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- | 6.8313 | 2.24 | 4000 | 6.9496 | 2.0097 |
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- | 6.9634 | 2.52 | 4500 | 6.9445 | 2.0097 |
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- | 6.7935 | 2.8 | 5000 | 6.9553 | 2.0097 |
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  ### Framework versions
 
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  This model was trained from scratch on the librispeech_asr dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 3.9280
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+ - Wer: 0.9561
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 3e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | 5.3825 | 0.28 | 500 | 5.5398 | 0.9602 |
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+ | 4.6164 | 0.56 | 1000 | 4.8871 | 0.9781 |
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+ | 4.5587 | 0.84 | 1500 | 4.6683 | 1.0010 |
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+ | 4.3428 | 1.12 | 2000 | 4.4745 | 1.6443 |
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+ | 4.2545 | 1.4 | 2500 | 4.2854 | 1.3727 |
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+ | 4.0861 | 1.68 | 3000 | 4.1451 | 1.3667 |
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+ | 4.0882 | 1.96 | 3500 | 4.0432 | 1.2540 |
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+ | 3.5985 | 2.24 | 4000 | 4.0155 | 1.0706 |
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+ | 3.7731 | 2.52 | 4500 | 3.9633 | 1.0627 |
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+ | 3.6984 | 2.8 | 5000 | 3.9280 | 0.9561 |
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  ### Framework versions
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