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--- |
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language: |
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- en |
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license: apache-2.0 |
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base_model: futureProofGlitch/whisper-small-v2 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- futureProofGlitch/Lectures-test-V1 |
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metrics: |
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- wer |
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model-index: |
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- name: FutureProofGlitch - Whisper Small - Fine Tuned on Lectures |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: TBK's Treasured Lectures |
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type: futureProofGlitch/Lectures-test-V1 |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.056233149313133904 |
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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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# FutureProofGlitch - Whisper Small - Fine Tuned on Lectures |
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This model is a fine-tuned version of [futureProofGlitch/whisper-small-v2](https://huggingface.co./futureProofGlitch/whisper-small-v2) on the TBK's Treasured Lectures dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3574 |
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- Wer Ortho: 0.1834 |
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- Wer: 0.0562 |
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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: 1.1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: constant_with_warmup |
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- lr_scheduler_warmup_steps: 10 |
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- training_steps: 100 |
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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 Ortho | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| |
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| No log | 0.21 | 25 | 0.8342 | 0.2377 | 0.0939 | |
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| 3.0694 | 0.42 | 50 | 0.4413 | 0.2100 | 0.0651 | |
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| 3.0694 | 0.64 | 75 | 0.3754 | 0.1859 | 0.0557 | |
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| 0.3126 | 0.85 | 100 | 0.3574 | 0.1834 | 0.0562 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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