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--- |
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language: |
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- en |
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tags: |
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- automatic-speech-recognition |
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- ahazeemi/librispeech10h |
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- generated_from_trainer |
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datasets: |
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- ahazeemi/librispeech10h |
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metrics: |
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- wer |
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pipeline_tag: automatic-speech-recognition |
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base_model: microsoft/wavlm-large |
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model-index: |
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- name: wavlm-libri-clean-100h-large |
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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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# wavlm-libri-clean-100h-large |
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This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the AHAZEEMI/LIBRISPEECH10H - CLEAN dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0893 |
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- Wer: 0.0655 |
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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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 4 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 0.0144 | 0.42 | 300 | 0.0947 | 0.0749 | |
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| 0.1408 | 0.84 | 600 | 0.1347 | 0.1363 | |
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| 0.0396 | 1.26 | 900 | 0.1090 | 0.0935 | |
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| 0.0353 | 1.68 | 1200 | 0.1032 | 0.0832 | |
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| 0.051 | 2.1 | 1500 | 0.0969 | 0.0774 | |
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| 0.0254 | 2.52 | 1800 | 0.0930 | 0.0715 | |
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| 0.0579 | 2.94 | 2100 | 0.0894 | 0.0660 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 2.0.0+cpu |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
