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wavlm-libri-clean-100h-base-plus

Automatic Speech Recognition Transformers PyTorch TensorBoard wavlm librispeech_asr generated_from_trainer wavlm_libri_finetune Inference Endpoints
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  1. README.md +71 -0
  2. added_tokens.json +1 -0
  3. all_results.json +14 -0
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README.md ADDED
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+ ---
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+ tags:
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+ - automatic-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: wavlm-libri-clean-100h-base-plus-2
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+ results: []
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+ ---
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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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+
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+ # wavlm-libri-clean-100h-base-plus-2
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+
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+ This model is a fine-tuned version of [microsoft/wavlm-base-plus](https://huggingface.co/microsoft/wavlm-base-plus) on the LIBRISPEECH_ASR - CLEAN dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0819
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+ - Wer: 0.0683
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 4
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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: 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
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+ - num_epochs: 3.0
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Wer |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | 2.8877 | 0.34 | 300 | 2.8649 | 1.0 |
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+ | 0.2852 | 0.67 | 600 | 0.2196 | 0.1830 |
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+ | 0.1198 | 1.01 | 900 | 0.1438 | 0.1273 |
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+ | 0.0906 | 1.35 | 1200 | 0.1145 | 0.1035 |
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+ | 0.0729 | 1.68 | 1500 | 0.1055 | 0.0955 |
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+ | 0.0605 | 2.02 | 1800 | 0.0936 | 0.0859 |
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+ | 0.0402 | 2.35 | 2100 | 0.0885 | 0.0746 |
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+ | 0.0421 | 2.69 | 2400 | 0.0848 | 0.0700 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.15.0.dev0
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+ - Pytorch 1.9.0+cu111
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+ - Datasets 1.16.2.dev0
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+ - Tokenizers 0.10.3
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+ "train_loss": 0.6277089082985359,
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+ "train_samples": 28538,
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+ "train_samples_per_second": 38.854,
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+ "train_steps_per_second": 1.214
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