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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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- generated_from_trainer |
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- hf-asr-leaderboard |
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- mozilla-foundation/common_voice_8_0 |
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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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base_model: facebook/wav2vec2-xls-r-300m |
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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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type: automatic-speech-recognition |
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name: 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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- type: wer |
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value: 30.5 |
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name: Test WER |
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- type: cer |
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value: 5.8 |
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name: Test CER |
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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: 0.2026 |
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- Wer: 0.3248 |
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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 9400 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: 16 |
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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: 64 |
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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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| 3.3036 | 5.32 | 500 | 3.2628 | 1.0 | |
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| 2.9734 | 10.63 | 1000 | 2.5677 | 0.9980 | |
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| 1.3466 | 15.95 | 1500 | 0.4455 | 0.6306 | |
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| 1.2424 | 21.28 | 2000 | 0.3603 | 0.5301 | |
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| 1.1655 | 26.59 | 2500 | 0.3165 | 0.4740 | |
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| 1.1026 | 31.91 | 3000 | 0.2930 | 0.4400 | |
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| 1.0655 | 37.23 | 3500 | 0.2675 | 0.4159 | |
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| 1.0239 | 42.55 | 4000 | 0.2580 | 0.3913 | |
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| 0.9938 | 47.87 | 4500 | 0.2373 | 0.3698 | |
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| 0.9655 | 53.19 | 5000 | 0.2379 | 0.3675 | |
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| 0.9374 | 58.51 | 5500 | 0.2486 | 0.3795 | |
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| 0.9065 | 63.83 | 6000 | 0.2243 | 0.3405 | |
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| 0.888 | 69.15 | 6500 | 0.2157 | 0.3277 | |
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| 0.8646 | 74.47 | 7000 | 0.2103 | 0.3288 | |
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| 0.8602 | 79.78 | 7500 | 0.2088 | 0.3238 | |
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| 0.8442 | 85.11 | 8000 | 0.2045 | 0.3266 | |
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| 0.8335 | 90.42 | 8500 | 0.2038 | 0.3241 | |
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| 0.8288 | 95.74 | 9000 | 0.2024 | 0.3280 | |
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### Framework versions |
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- Transformers 4.17.0.dev0 |
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- Pytorch 1.10.2+cu102 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.0 |
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