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--- |
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language: |
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- de |
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license: apache-2.0 |
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base_model: openai/whisper-tiny |
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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_11_0 |
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metrics: |
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- wer |
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model-index: |
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- name: whisper-tiny-german-V2-HanNeurAI |
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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 11.0 German shuffled 200k rows |
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type: mozilla-foundation/common_voice_11_0 |
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config: de |
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split: test |
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args: 'config: de, split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 32.33273006844562 |
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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-tiny-german-V2-HanNeurAI |
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This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 11.0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5818 |
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- Wer: 32.3327 |
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This fine-tuning model is part of my school project. |
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With limitation of my compute, I scale down the dataset from german common voice to shuffled 200k rows |
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Additional information can be found in this github: [HanCreation/Whisper-Tiny-German](https://github.com/HanCreation/Whisper-Tiny-German) |
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## Model description |
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Model Parameter (pipe.model.num_parameters()): 37760640 (37M) |
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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: 16 |
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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: 8000 |
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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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| 0.2054 | 0.08 | 1000 | 0.7062 | 39.0698 | |
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| 0.1861 | 0.16 | 2000 | 0.6687 | 36.4857 | |
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| 0.1677 | 0.24 | 3000 | 0.6393 | 35.6849 | |
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| 0.2019 | 0.32 | 4000 | 0.6193 | 34.4385 | |
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| 0.1808 | 0.4 | 5000 | 0.6103 | 33.8459 | |
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| 0.1697 | 0.48 | 6000 | 0.5956 | 32.8519 | |
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| 0.1468 | 0.56 | 7000 | 0.5884 | 32.7029 | |
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| 0.1906 | 0.64 | 8000 | 0.5818 | 32.3327 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.3.0 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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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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