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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- 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: wav2vec2LugandaASR20 |
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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_13_0 |
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type: common_voice_13_0 |
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config: lg |
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split: validation |
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args: lg |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.23221005634102265 |
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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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# wav2vec2LugandaASR20 |
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This model is a fine-tuned version of [Gemmar/wav2vec2LugandaASR](https://huggingface.co/Gemmar/wav2vec2LugandaASR) on the common_voice_13_0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2393 |
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- Wer: 0.2322 |
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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: 0.0003 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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: 200 |
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- num_epochs: 5 |
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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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| 0.1093 | 0.18 | 100 | 0.2134 | 0.2480 | |
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| 0.1141 | 0.36 | 200 | 0.2329 | 0.2724 | |
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| 0.1224 | 0.54 | 300 | 0.2560 | 0.2864 | |
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| 0.1345 | 0.72 | 400 | 0.2348 | 0.2716 | |
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| 0.1271 | 0.9 | 500 | 0.2339 | 0.2702 | |
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| 0.1232 | 1.08 | 600 | 0.2457 | 0.2806 | |
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| 0.1149 | 1.27 | 700 | 0.2372 | 0.2695 | |
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| 0.1129 | 1.45 | 800 | 0.2328 | 0.2718 | |
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| 0.1196 | 1.63 | 900 | 0.2326 | 0.2615 | |
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| 0.1185 | 1.81 | 1000 | 0.2249 | 0.2672 | |
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| 0.1159 | 1.99 | 1100 | 0.2202 | 0.2559 | |
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| 0.0933 | 2.17 | 1200 | 0.2302 | 0.2559 | |
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| 0.0947 | 2.35 | 1300 | 0.2306 | 0.2530 | |
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| 0.0941 | 2.53 | 1400 | 0.2325 | 0.2509 | |
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| 0.0946 | 2.71 | 1500 | 0.2233 | 0.2495 | |
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| 0.0949 | 2.89 | 1600 | 0.2320 | 0.2443 | |
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| 0.0883 | 3.07 | 1700 | 0.2383 | 0.2463 | |
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| 0.0783 | 3.25 | 1800 | 0.2386 | 0.2437 | |
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| 0.0753 | 3.43 | 1900 | 0.2329 | 0.2426 | |
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| 0.0772 | 3.62 | 2000 | 0.2317 | 0.2392 | |
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| 0.0774 | 3.8 | 2100 | 0.2308 | 0.2353 | |
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| 0.0764 | 3.98 | 2200 | 0.2293 | 0.2357 | |
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| 0.0666 | 4.16 | 2300 | 0.2446 | 0.2388 | |
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| 0.065 | 4.34 | 2400 | 0.2456 | 0.2359 | |
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| 0.0643 | 4.52 | 2500 | 0.2446 | 0.2345 | |
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| 0.0652 | 4.7 | 2600 | 0.2430 | 0.2325 | |
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| 0.0669 | 4.88 | 2700 | 0.2393 | 0.2322 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.0 |
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- Tokenizers 0.13.3 |
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