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wav2vec2-gpt2-wandb-grid-search

Automatic Speech Recognition
Transformers
PyTorch
speech-encoder-decoder
Generated from Trainer
Inference Endpoints
Model card Files Community
sanchit-gandhi HF staff commited on
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update model card README.md

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  1. README.md +2 -8
  2. wandb/run-20220302_135216-v36bieky/files/output.log +97 -0
  3. wandb/run-20220302_135216-v36bieky/logs/debug-internal.log +126 -0
  4. wandb/run-20220302_135216-v36bieky/run-v36bieky.wandb +2 -2
README.md CHANGED
@@ -14,9 +14,6 @@ should probably proofread and complete it, then remove this comment. -->
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  #
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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: 5.1431
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- - Wer: 1.9923
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  ## Model description
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@@ -39,8 +36,8 @@ The following hyperparameters were used during training:
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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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- - gradient_accumulation_steps: 4
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- - total_train_batch_size: 32
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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
@@ -49,9 +46,6 @@ The following hyperparameters were used during training:
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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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- | 4.4548 | 0.56 | 500 | 5.1431 | 1.9923 |
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  ### Framework versions
 
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  #
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  This model was trained from scratch on the librispeech_asr dataset.
 
 
 
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  ## Model description
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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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+ - gradient_accumulation_steps: 8
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+ - total_train_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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  ### Training results
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  ### Framework versions
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  [INFO|modeling_utils.py:1081] 2022-03-02 15:18:55,919 >> Model weights saved in ./pytorch_model.binssor_config.jsons/it][INFO|trainer.py:1492] 2022-03-02 15:18:23,755 >> 1,369 >> Could not estimate the number of tokens of the input, floating-point operations will not be computed
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