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README.md
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---
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language:
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- it
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license: apache-2.0
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base_model: openai/whisper-medium
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tags:
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- generated_from_trainer
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datasets:
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- mozilla-foundation/common_voice_17_0
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metrics:
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- wer
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model-index:
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- name: Whisper Medium it
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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: Common Voice 17.0
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type: mozilla-foundation/common_voice_17_0
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config: it
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split: test
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args: it
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metrics:
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- name: Wer
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type: wer
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value: 5.709779804285139
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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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# Whisper Medium it
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 17.0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1462
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- Wer: 5.7098
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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: 1e-05
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- train_batch_size: 32
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- eval_batch_size: 8
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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: linear
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- lr_scheduler_warmup_steps: 500
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- training_steps: 5000
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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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| 0.1755 | 0.1730 | 1000 | 0.1974 | 8.0595 |
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| 0.157 | 0.3461 | 2000 | 0.1776 | 7.1199 |
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| 0.1287 | 0.5191 | 3000 | 0.1622 | 6.5201 |
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| 0.1287 | 0.6922 | 4000 | 0.1521 | 5.9863 |
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| 0.1168 | 0.8652 | 5000 | 0.1462 | 5.7098 |
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### Framework versions
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- Transformers 4.42.0.dev0
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- Pytorch 2.3.0+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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