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--- |
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language: |
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- sw |
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license: apache-2.0 |
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base_model: openai/whisper-large |
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tags: |
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- hf-asr-leaderboard |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_14_0 |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Large - Denis Musinguzi |
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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 14.0 |
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type: mozilla-foundation/common_voice_14_0 |
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config: lg |
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split: None |
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args: 'config: sw, split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.24669449134992194 |
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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 - Denis Musinguzi |
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This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the Common Voice 14.0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2966 |
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- Wer: 0.2467 |
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- Cer: 0.0700 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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: 10000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Cer | Validation Loss | Wer | |
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|:-------------:|:-----:|:----:|:------:|:---------------:|:------:| |
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| 0.6329 | 0.61 | 1600 | 0.0878 | 0.3515 | 0.3385 | |
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| 0.2241 | 1.22 | 3200 | 0.0589 | 0.3045 | 0.2517 | |
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| 0.1618 | 1.82 | 4800 | 0.0707 | 0.2801 | 0.2645 | |
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| 0.1109 | 2.43 | 6400 | 0.0774 | 0.2870 | 0.2580 | |
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| 0.0837 | 3.04 | 8000 | 0.0597 | 0.2900 | 0.2333 | |
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| 0.045 | 3.65 | 9600 | 0.2966 | 0.2467 | 0.0700 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.2.1 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
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