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--- |
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language: |
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- sr |
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license: apache-2.0 |
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base_model: openai/whisper-large-v2 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- google/fleurs |
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- mozilla-foundation/common_voice_13_0 |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Large Sr Combined |
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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: Google Fleurs |
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type: google/fleurs |
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config: sr |
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split: test |
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args: sr |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.06233709817549957 |
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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 Sr Fleurs and CommonVoice |
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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 combined Google Fleurs and Mozilla Foundation Common Voice 13 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1749 |
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- Wer Ortho: 0.1678 |
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- Wer: 0.0623 |
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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: 2 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 16 |
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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: 50 |
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- training_steps: 1500 |
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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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| 0.0737 | 1.34 | 500 | 0.1735 | 0.1865 | 0.0908 | |
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| 0.0304 | 2.67 | 1000 | 0.1622 | 0.1670 | 0.0728 | |
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| 0.0156 | 4.01 | 1500 | 0.1749 | 0.1678 | 0.0623 | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
