# lucio /xls-r-kyrgiz-cv8

 --- language: - ky license: apache-2.0 tags: - automatic-speech-recognition - generated_from_trainer - hf-asr-leaderboard - mozilla-foundation/common_voice_8_0 - robust-speech-event datasets: - mozilla-foundation/common_voice_8_0 model-index: - name: XLS-R-300M Kyrgiz CV8 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 8 type: mozilla-foundation/common_voice_8_0 args: ky metrics: - name: Test WER (with LM) type: wer value: 19.01 - name: Test CER (with LM) type: cer value: 5.38 - name: Test WER (no LM) type: wer value: 31.28 - name: Test CER (no LM) type: cer value: 7.66 --- # XLS-R-300M Kyrgiz CV8 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 - KY dataset. It achieves the following results on the validation set: - Loss: 0.5497 - Wer: 0.2945 - Cer: 0.0791 ## Model description For a description of the model architecture, see [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) The model vocabulary consists of the cyrillic alphabet with punctuation removed. The kenlm language model is built using the text of the train and invalidated corpus splits. ## Intended uses & limitations This model is expected to be of some utility for low-fidelity use cases such as: - Draft video captions - Indexing of recorded broadcasts 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. ## Training and evaluation data The combination of train, dev and other of common voice official splits were used as training data. The half of the official test split was used as validation data, as and the full test set was used for final evaluation. ## Training procedure The featurization layers of the XLS-R model are frozen while tuning a final CTC/LM layer on the Kyrgiz CV8 example sentences. A ramped learning rate is used with an initial warmup phase of 500 steps, a max of 0.0001, and cooling back towards 0 for the remainder of the 8100 steps (300 epochs). ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 300.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:------:|:----:|:---------------:|:------:|:------:| | 3.1079 | 18.51 | 500 | 2.6795 | 0.9996 | 0.9825 | | 0.8506 | 37.04 | 1000 | 0.4323 | 0.3718 | 0.0961 | | 0.6821 | 55.55 | 1500 | 0.4105 | 0.3311 | 0.0878 | | 0.6091 | 74.07 | 2000 | 0.4281 | 0.3168 | 0.0851 | | 0.5429 | 92.58 | 2500 | 0.4525 | 0.3147 | 0.0842 | | 0.5063 | 111.11 | 3000 | 0.4619 | 0.3144 | 0.0839 | | 0.4661 | 129.62 | 3500 | 0.4660 | 0.3039 | 0.0818 | | 0.4353 | 148.15 | 4000 | 0.4695 | 0.3083 | 0.0820 | | 0.4048 | 166.65 | 4500 | 0.4909 | 0.3085 | 0.0824 | | 0.3852 | 185.18 | 5000 | 0.5074 | 0.3048 | 0.0812 | | 0.3567 | 203.69 | 5500 | 0.5111 | 0.3012 | 0.0810 | | 0.3451 | 222.22 | 6000 | 0.5225 | 0.2982 | 0.0804 | | 0.325 | 240.73 | 6500 | 0.5270 | 0.2955 | 0.0796 | | 0.3089 | 259.25 | 7000 | 0.5381 | 0.2929 | 0.0793 | | 0.2941 | 277.76 | 7500 | 0.5565 | 0.2923 | 0.0794 | | 0.2945 | 296.29 | 8000 | 0.5495 | 0.2951 | 0.0789 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0