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--- |
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language: |
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- ug |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- mozilla-foundation/common_voice_8_0 |
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- generated_from_trainer |
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- robust-speech-event |
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- ug |
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datasets: |
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- mozilla-foundation/common_voice_8_0 |
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model-index: |
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- name: XLS-R-300M Uyghur CV8 |
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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 8 |
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type: mozilla-foundation/common_voice_8_0 |
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args: ug |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 36.33 |
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- name: Test CER |
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type: cer |
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value: 6.75 |
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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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# XLS-R-300M Uyghur CV8 |
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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 MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - UG dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2240 |
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- Wer: 0.3693 |
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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: 4e-05 |
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- train_batch_size: 8 |
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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: 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: 2000 |
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- num_epochs: 100.0 |
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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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| 4.1169 | 2.66 | 500 | 4.0146 | 1.0 | |
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| 3.2512 | 5.32 | 1000 | 3.2342 | 1.0 | |
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| 2.5435 | 7.97 | 1500 | 1.8155 | 1.0286 | |
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| 1.5575 | 10.64 | 2000 | 0.6346 | 0.7058 | |
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| 1.3979 | 13.3 | 2500 | 0.4885 | 0.6320 | |
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| 1.2874 | 15.95 | 3000 | 0.4271 | 0.6088 | |
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| 1.2383 | 18.61 | 3500 | 0.3889 | 0.5869 | |
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| 1.2054 | 21.28 | 4000 | 0.3609 | 0.5793 | |
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| 1.1866 | 23.93 | 4500 | 0.3450 | 0.5513 | |
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| 1.1332 | 26.59 | 5000 | 0.3214 | 0.5379 | |
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| 1.135 | 29.25 | 5500 | 0.3122 | 0.5384 | |
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| 1.0992 | 31.91 | 6000 | 0.2948 | 0.5078 | |
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| 1.0707 | 34.57 | 6500 | 0.2928 | 0.5128 | |
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| 1.0754 | 37.23 | 7000 | 0.2857 | 0.5017 | |
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| 1.0461 | 39.89 | 7500 | 0.2791 | 0.5099 | |
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| 1.0328 | 42.55 | 8000 | 0.2729 | 0.5120 | |
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| 1.0201 | 45.21 | 8500 | 0.2654 | 0.4720 | |
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| 1.0035 | 47.87 | 9000 | 0.2623 | 0.4659 | |
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| 1.0069 | 50.53 | 9500 | 0.2569 | 0.4593 | |
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| 0.9998 | 53.19 | 10000 | 0.2519 | 0.4405 | |
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| 0.9762 | 55.85 | 10500 | 0.2505 | 0.4588 | |
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| 0.9755 | 58.51 | 11000 | 0.2479 | 0.4564 | |
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| 0.9624 | 61.17 | 11500 | 0.2460 | 0.4298 | |
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| 0.9494 | 63.83 | 12000 | 0.2402 | 0.4182 | |
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| 0.948 | 66.49 | 12500 | 0.2412 | 0.4212 | |
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| 0.9312 | 69.15 | 13000 | 0.2352 | 0.3970 | |
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| 0.9172 | 71.81 | 13500 | 0.2357 | 0.3926 | |
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| 0.9101 | 74.47 | 14000 | 0.2305 | 0.3905 | |
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| 0.9177 | 77.13 | 14500 | 0.2307 | 0.3838 | |
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| 0.9083 | 79.78 | 15000 | 0.2313 | 0.3800 | |
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| 0.9068 | 82.45 | 15500 | 0.2275 | 0.3742 | |
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| 0.9087 | 85.11 | 16000 | 0.2283 | 0.3747 | |
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| 0.8838 | 87.76 | 16500 | 0.2286 | 0.3777 | |
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| 0.8868 | 90.42 | 17000 | 0.2269 | 0.3722 | |
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| 0.8895 | 93.08 | 17500 | 0.2246 | 0.3714 | |
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| 0.8926 | 95.74 | 18000 | 0.2241 | 0.3705 | |
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| 0.8856 | 98.4 | 18500 | 0.2242 | 0.3693 | |
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### Framework versions |
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- Transformers 4.16.0.dev0 |
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- Pytorch 1.10.1+cu102 |
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- Datasets 1.18.2.dev0 |
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- Tokenizers 0.11.0 |
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