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wav2vec2LugandaASR20

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
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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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+
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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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+
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+ # wav2vec2LugandaASR20
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+
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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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