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--- |
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license: apache-2.0 |
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base_model: openai/whisper-small |
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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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- google/fleurs |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper small shona |
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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 sn_zw |
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type: google/fleurs |
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config: sn_zw |
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split: test |
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args: sn_zw |
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metrics: |
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- name: Wer |
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type: wer |
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value: 49.90958408679928 |
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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 small shona |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs sn_zw dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1220 |
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- Wer: 49.9096 |
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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: 16 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 3 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 48 |
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- total_eval_batch_size: 48 |
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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: 2000 |
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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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| 0.0064 | 24.24 | 400 | 0.9630 | 50.7233 | |
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| 0.001 | 48.48 | 800 | 1.0617 | 49.9397 | |
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| 0.0005 | 72.73 | 1200 | 1.1016 | 49.9397 | |
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| 0.0004 | 96.97 | 1600 | 1.1220 | 49.9096 | |
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| 0.0003 | 121.21 | 2000 | 1.1298 | 50.0422 | |
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### Framework versions |
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- Transformers 4.37.1 |
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- Pytorch 1.12.0+cu102 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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