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--- |
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language: |
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- pl |
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license: apache-2.0 |
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tags: |
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- whisper-event |
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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 Large v2 PL |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: Common Voice 11.0 |
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type: mozilla-foundation/common_voice_11_0 |
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config: pl |
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split: test |
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args: pl |
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metrics: |
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- type: wer |
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value: 6.89 |
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name: WER |
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- type: wer_without_norm |
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value: 19.79 |
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name: WER unnormalized |
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- type: cer |
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value: 1.88 |
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name: CER |
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- type: mer |
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value: 6.84 |
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name: MER |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: facebook/voxpopuli |
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type: facebook/voxpopuli |
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config: pl |
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split: test |
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metrics: |
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- type: wer |
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value: 9.26 |
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name: WER |
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- type: wer_without_norm |
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value: 30.25 |
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name: WER unnormalized |
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- type: cer |
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value: 5.32 |
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name: CER |
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- type: mer |
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value: 9.1 |
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name: MER |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: google/fleurs |
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type: google/fleurs |
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config: pl_pl |
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split: test |
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metrics: |
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- type: wer |
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value: 9.88 |
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name: WER |
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- type: wer_without_norm |
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value: 29.53 |
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name: WER unnormalized |
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- type: cer |
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value: 5.09 |
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name: CER |
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- type: mer |
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value: 9.73 |
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name: MER |
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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 Large v2 PL |
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This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) 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: 0.4222 |
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- Wer: 6.9125 |
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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: 8 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 64 |
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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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- 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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| 0.1144 | 1.93 | 500 | 0.2016 | 7.4749 | |
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| 0.0441 | 3.86 | 1000 | 0.2193 | 7.3154 | |
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| 0.0099 | 5.79 | 1500 | 0.2983 | 7.0804 | |
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| 0.0048 | 7.72 | 2000 | 0.3514 | 7.0988 | |
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| 0.0017 | 9.65 | 2500 | 0.3614 | 7.0485 | |
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| 0.0014 | 11.58 | 3000 | 0.3814 | 7.1240 | |
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| 0.001 | 13.51 | 3500 | 0.3773 | 6.9931 | |
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| 0.0005 | 15.44 | 4000 | 0.4085 | 6.9662 | |
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| 0.0004 | 17.37 | 4500 | 0.4195 | 6.9192 | |
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| 0.0004 | 19.3 | 5000 | 0.4222 | 6.9125 | |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.7.1.dev0 |
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- Tokenizers 0.13.2 |
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