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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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- ug |
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- robust-speech-event |
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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: 28.74 |
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- name: Test CER |
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type: cer |
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value: 5.38 |
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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: 0.2036 |
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- WER: 0.2977 |
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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 CV8 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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| 3.2892 | 2.66 | 500 | 3.2415 | 1.0 | |
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| 2.9206 | 5.32 | 1000 | 2.4381 | 1.0056 | |
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| 1.4909 | 7.97 | 1500 | 0.5428 | 0.6705 | |
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| 1.3395 | 10.64 | 2000 | 0.4207 | 0.5995 | |
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| 1.2718 | 13.3 | 2500 | 0.3743 | 0.5648 | |
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| 1.1798 | 15.95 | 3000 | 0.3225 | 0.4927 | |
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| 1.1392 | 18.61 | 3500 | 0.3097 | 0.4627 | |
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| 1.1143 | 21.28 | 4000 | 0.2996 | 0.4505 | |
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| 1.0923 | 23.93 | 4500 | 0.2841 | 0.4229 | |
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| 1.0516 | 26.59 | 5000 | 0.2705 | 0.4113 | |
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| 1.051 | 29.25 | 5500 | 0.2622 | 0.4078 | |
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| 1.021 | 31.91 | 6000 | 0.2611 | 0.4009 | |
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| 0.9886 | 34.57 | 6500 | 0.2498 | 0.3921 | |
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| 0.984 | 37.23 | 7000 | 0.2521 | 0.3845 | |
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| 0.9631 | 39.89 | 7500 | 0.2413 | 0.3791 | |
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| 0.9353 | 42.55 | 8000 | 0.2391 | 0.3612 | |
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| 0.922 | 45.21 | 8500 | 0.2363 | 0.3571 | |
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| 0.9116 | 47.87 | 9000 | 0.2285 | 0.3668 | |
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| 0.8951 | 50.53 | 9500 | 0.2256 | 0.3729 | |
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| 0.8865 | 53.19 | 10000 | 0.2228 | 0.3663 | |
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| 0.8792 | 55.85 | 10500 | 0.2221 | 0.3656 | |
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| 0.8682 | 58.51 | 11000 | 0.2228 | 0.3323 | |
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| 0.8492 | 61.17 | 11500 | 0.2167 | 0.3446 | |
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| 0.8365 | 63.83 | 12000 | 0.2156 | 0.3321 | |
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| 0.8298 | 66.49 | 12500 | 0.2142 | 0.3400 | |
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| 0.808 | 69.15 | 13000 | 0.2079 | 0.3148 | |
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| 0.7999 | 71.81 | 13500 | 0.2117 | 0.3225 | |
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| 0.7871 | 74.47 | 14000 | 0.2088 | 0.3174 | |
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| 0.7858 | 77.13 | 14500 | 0.2060 | 0.3008 | |
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| 0.7764 | 79.78 | 15000 | 0.2128 | 0.3146 | |
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| 0.7684 | 82.45 | 15500 | 0.2086 | 0.3101 | |
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| 0.7717 | 85.11 | 16000 | 0.2048 | 0.3069 | |
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| 0.7435 | 87.76 | 16500 | 0.2027 | 0.3055 | |
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| 0.7378 | 90.42 | 17000 | 0.2059 | 0.2993 | |
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| 0.7406 | 93.08 | 17500 | 0.2040 | 0.2966 | |
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| 0.7361 | 95.74 | 18000 | 0.2056 | 0.3000 | |
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| 0.7379 | 98.4 | 18500 | 0.2031 | 0.2976 | |
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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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