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Gemmar
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LugandaASRwav2Vec300M

Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
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  2. pytorch_model.bin +1 -1
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: facebook/wav2vec2-xls-r-300m
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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: LugandaASRwav2Vec300M
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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: 0.22313171042840438
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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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+ # LugandaASRwav2Vec300M
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+
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+ This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) 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: 0.1741
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+ - Wer: 0.2231
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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: 4
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - gradient_accumulation_steps: 24
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+ - total_train_batch_size: 96
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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: 3
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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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+ | 6.4394 | 0.14 | 100 | 2.9784 | 1.0 |
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+ | 2.8739 | 0.27 | 200 | 2.7056 | 1.0000 |
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+ | 1.2203 | 0.41 | 300 | 0.5656 | 0.7264 |
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+ | 0.4507 | 0.54 | 400 | 0.3978 | 0.5258 |
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+ | 0.3657 | 0.68 | 500 | 0.3314 | 0.4416 |
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+ | 0.3131 | 0.81 | 600 | 0.2996 | 0.4049 |
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+ | 0.2886 | 0.95 | 700 | 0.2823 | 0.3766 |
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+ | 0.2535 | 1.08 | 800 | 0.2517 | 0.3317 |
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+ | 0.2279 | 1.22 | 900 | 0.2407 | 0.3190 |
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+ | 0.2209 | 1.36 | 1000 | 0.2296 | 0.3077 |
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+ | 0.2075 | 1.49 | 1100 | 0.2228 | 0.2931 |
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+ | 0.1983 | 1.63 | 1200 | 0.2139 | 0.2809 |
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+ | 0.1902 | 1.76 | 1300 | 0.2093 | 0.2688 |
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+ | 0.1931 | 1.9 | 1400 | 0.2019 | 0.2666 |
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+ | 0.1741 | 2.03 | 1500 | 0.1951 | 0.2521 |
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+ | 0.1481 | 2.17 | 1600 | 0.1934 | 0.2435 |
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+ | 0.1423 | 2.3 | 1700 | 0.1912 | 0.2409 |
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+ | 0.1413 | 2.44 | 1800 | 0.1841 | 0.2368 |
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+ | 0.1361 | 2.58 | 1900 | 0.1813 | 0.2310 |
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+ | 0.1337 | 2.71 | 2000 | 0.1775 | 0.2279 |
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+ | 0.1358 | 2.85 | 2100 | 0.1756 | 0.2247 |
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+ | 0.133 | 2.98 | 2200 | 0.1741 | 0.2231 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.32.0
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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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