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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_7_0 |
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- generated_from_trainer |
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- ug |
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- robust-speech-event |
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- hf-asr-leaderboard |
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datasets: |
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- mozilla-foundation/common_voice_7_0 |
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model-index: |
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- name: XLS-R-300M Uyghur CV7 |
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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 7 |
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type: mozilla-foundation/common_voice_7_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: 25.845 |
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- name: Test CER |
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type: cer |
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value: 4.795 |
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--- |
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# XLS-R-300M Uyghur CV7 |
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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_7_0 - UG dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1772 |
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- Wer: 0.2589 |
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## Model description |
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For a description of the model architecture, see [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) |
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The model vocabulary consists of the alphabetic characters of the [Perso-Arabic script for the Uyghur language](https://omniglot.com/writing/uyghur.htm), with punctuation removed. |
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## Intended uses & limitations |
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This model is expected to be of some utility for low-fidelity use cases such as: |
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- Draft video captions |
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- Indexing of recorded broadcasts |
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The model is not reliable enough to use as a substitute for live captions for accessibility purposes, and it should not be used in a manner that would infringe the privacy of any of the contributors to the Common Voice dataset nor any other speakers. |
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## Training and evaluation data |
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The combination of `train` and `dev` of common voice official splits were used as training data. The official `test` split was used as validation data as well as for final evaluation. |
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## Training procedure |
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The featurization layers of the XLS-R model are frozen while tuning a final CTC/LM layer on the Uyghur CV7 example sentences. A ramped learning rate is used with an initial warmup phase of 2000 steps, a max of 0.0001, and cooling back towards 0 for the remainder of the 18500 steps (100 epochs). |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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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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| 3.3043 | 2.73 | 500 | 3.2415 | 1.0 | |
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| 3.0482 | 5.46 | 1000 | 2.9591 | 1.0 | |
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| 1.4767 | 8.2 | 1500 | 0.4779 | 0.5777 | |
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| 1.3152 | 10.93 | 2000 | 0.3697 | 0.4938 | |
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| 1.2246 | 13.66 | 2500 | 0.3084 | 0.4459 | |
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| 1.1781 | 16.39 | 3000 | 0.2842 | 0.4154 | |
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| 1.1351 | 19.13 | 3500 | 0.2615 | 0.3929 | |
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| 1.1052 | 21.86 | 4000 | 0.2462 | 0.3747 | |
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| 1.0711 | 24.59 | 4500 | 0.2366 | 0.3652 | |
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| 1.035 | 27.32 | 5000 | 0.2268 | 0.3557 | |
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| 1.0277 | 30.05 | 5500 | 0.2243 | 0.3450 | |
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| 1.002 | 32.79 | 6000 | 0.2204 | 0.3389 | |
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| 0.9837 | 35.52 | 6500 | 0.2156 | 0.3349 | |
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| 0.9773 | 38.25 | 7000 | 0.2127 | 0.3289 | |
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| 0.9807 | 40.98 | 7500 | 0.2142 | 0.3274 | |
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| 0.9582 | 43.72 | 8000 | 0.2004 | 0.3142 | |
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| 0.9548 | 46.45 | 8500 | 0.2022 | 0.3050 | |
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| 0.9251 | 49.18 | 9000 | 0.2019 | 0.3035 | |
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| 0.9103 | 51.91 | 9500 | 0.1964 | 0.3021 | |
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| 0.915 | 54.64 | 10000 | 0.1970 | 0.3032 | |
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| 0.8962 | 57.38 | 10500 | 0.2007 | 0.3046 | |
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| 0.8729 | 60.11 | 11000 | 0.1967 | 0.2942 | |
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| 0.8744 | 62.84 | 11500 | 0.1952 | 0.2885 | |
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| 0.874 | 65.57 | 12000 | 0.1894 | 0.2895 | |
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| 0.8457 | 68.31 | 12500 | 0.1895 | 0.2828 | |
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| 0.8519 | 71.04 | 13000 | 0.1912 | 0.2875 | |
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| 0.8301 | 73.77 | 13500 | 0.1878 | 0.2760 | |
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| 0.8226 | 76.5 | 14000 | 0.1808 | 0.2701 | |
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| 0.8071 | 79.23 | 14500 | 0.1849 | 0.2741 | |
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| 0.7999 | 81.97 | 15000 | 0.1808 | 0.2717 | |
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| 0.7947 | 84.7 | 15500 | 0.1821 | 0.2716 | |
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| 0.7783 | 87.43 | 16000 | 0.1824 | 0.2661 | |
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| 0.7729 | 90.16 | 16500 | 0.1773 | 0.2639 | |
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| 0.7759 | 92.9 | 17000 | 0.1767 | 0.2629 | |
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| 0.7713 | 95.63 | 17500 | 0.1780 | 0.2621 | |
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| 0.7628 | 98.36 | 18000 | 0.1773 | 0.2594 | |
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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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