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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-large-960h |
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tags: |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-large-960h |
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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-large-960h |
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This model is a fine-tuned version of [facebook/wav2vec2-large-960h](https://huggingface.co/facebook/wav2vec2-large-960h) on an [acc_dataset_v2](https://huggingface.co/datasets/monadical-labs/acc_dataset_v2)" dataset (commit: [a41c520](https://huggingface.co/datasets/monadical-labs/acc_dataset_v2/commit/a41c5204b95bcf293ac5ee1a0da94170374b1cb0)). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6286 |
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- Wer: 0.1538 |
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## Model description |
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This is a voice-2-text transcription model specialized for the acc dataset. |
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## Training and evaluation data |
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Training was based on the training set in [acc_dataset_v2](https://huggingface.co/datasets/monadical-labs/acc_dataset_v2) and evaluation based on |
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the validation and test sets in the same dataset. |
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## Training procedure |
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See the Jupyter notebook [Finetuning-notebook-wav2vec2-large-960h-on-acc-data](https://huggingface.co/ilokavat/wav2vec2-large-960h/blob/main/Finetuning-notebook-wav2vec2-large-960h-on-acc-data.ipynb) |
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for the full 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: 0.0001 |
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- train_batch_size: 64 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 15 |
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- num_epochs: 128 |
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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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| 1.2379 | 8.3333 | 50 | 0.6501 | 0.3056 | |
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| 0.4865 | 16.6667 | 100 | 0.7069 | 0.2790 | |
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| 0.3054 | 25.0 | 150 | 0.6598 | 0.2369 | |
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| 0.2308 | 33.3333 | 200 | 0.6517 | 0.2215 | |
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| 0.1793 | 41.6667 | 250 | 0.6884 | 0.2103 | |
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| 0.1379 | 50.0 | 300 | 0.6418 | 0.1949 | |
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| 0.1253 | 58.3333 | 350 | 0.7004 | 0.1918 | |
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| 0.0988 | 66.6667 | 400 | 0.6059 | 0.1846 | |
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| 0.088 | 75.0 | 450 | 0.6507 | 0.1826 | |
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| 0.0773 | 83.3333 | 500 | 0.5473 | 0.1682 | |
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| 0.0686 | 91.6667 | 550 | 0.6027 | 0.1682 | |
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| 0.0643 | 100.0 | 600 | 0.6192 | 0.1713 | |
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| 0.0595 | 108.3333 | 650 | 0.6119 | 0.1703 | |
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| 0.0562 | 116.6667 | 700 | 0.5953 | 0.16 | |
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| 0.0507 | 125.0 | 750 | 0.6286 | 0.1538 | |
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### Framework versions |
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- Transformers 4.48.2 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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