--- language: - kab license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_8_0 - generated_from_trainer - sw - robust-speech-event - model_for_talk - hf-asr-leaderboard datasets: - mozilla-foundation/common_voice_8_0 model-index: - name: Akashpb13/Kabyle_xlsr results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 8 type: mozilla-foundation/common_voice_8_0 args: kab metrics: - name: Test WER type: wer value: 0.3188425282720088 - name: Test CER type: cer value: 0.09443079928558358 --- # Akashpb13/Kabyle_xlsr 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 - hu dataset. It achieves the following results on the evaluation set (which is 10 percent of train data set merged with dev datasets): - Loss: 0.159032 - Wer: 0.187934 ## Model description "facebook/wav2vec2-xls-r-300m" was finetuned. ## Intended uses & limitations More information needed ## Training and evaluation data Training data - Common voice Kabyle train.tsv. Only 50,000 records were sampled randomly and trained due to huge size of dataset. Only those points were considered where upvotes were greater than downvotes and duplicates were removed after concatenation of all the datasets given in common voice 7.0 ## Training procedure For creating the training dataset, all possible datasets were appended and 90-10 split was used. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000096 - train_batch_size: 8 - seed: 13 - gradient_accumulation_steps: 4 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Step | Training Loss | Validation Loss | Wer | |-------|---------------|-----------------|----------| | 500 | 7.199800 | 3.130564 | 1.000000 | | 1000 | 1.570200 | 0.718097 | 0.734682 | | 1500 | 0.850800 | 0.524227 | 0.640532 | | 2000 | 0.712200 | 0.468694 | 0.603454 | | 2500 | 0.651200 | 0.413833 | 0.573025 | | 3000 | 0.603100 | 0.403680 | 0.552847 | | 3500 | 0.553300 | 0.372638 | 0.541719 | | 4000 | 0.537200 | 0.353759 | 0.531191 | | 4500 | 0.506300 | 0.359109 | 0.519601 | | 5000 | 0.479600 | 0.343937 | 0.511336 | | 5500 | 0.479800 | 0.338214 | 0.503948 | | 6000 | 0.449500 | 0.332600 | 0.495221 | | 6500 | 0.439200 | 0.323905 | 0.492635 | | 7000 | 0.434900 | 0.310417 | 0.484555 | | 7500 | 0.403200 | 0.311247 | 0.483262 | | 8000 | 0.401500 | 0.295637 | 0.476566 | | 8500 | 0.397000 | 0.301321 | 0.471672 | | 9000 | 0.371600 | 0.295639 | 0.468440 | | 9500 | 0.370700 | 0.294039 | 0.468902 | | 10000 | 0.364900 | 0.291195 | 0.468440 | | 10500 | 0.348300 | 0.284898 | 0.461098 | | 11000 | 0.350100 | 0.281764 | 0.459805 | | 11500 | 0.336900 | 0.291022 | 0.461606 | | 12000 | 0.330700 | 0.280467 | 0.455234 | | 12500 | 0.322500 | 0.271714 | 0.452694 | | 13000 | 0.307400 | 0.289519 | 0.455465 | | 13500 | 0.309300 | 0.281922 | 0.451217 | | 14000 | 0.304800 | 0.271514 | 0.452186 | | 14500 | 0.288100 | 0.286801 | 0.446830 | | 15000 | 0.293200 | 0.276309 | 0.445399 | | 15500 | 0.289800 | 0.287188 | 0.446230 | | 16000 | 0.274800 | 0.286406 | 0.441243 | | 16500 | 0.271700 | 0.284754 | 0.441520 | | 17000 | 0.262500 | 0.275431 | 0.442167 | | 17500 | 0.255500 | 0.276575 | 0.439858 | | 18000 | 0.260200 | 0.269911 | 0.435425 | | 18500 | 0.250600 | 0.270519 | 0.434686 | | 19000 | 0.243300 | 0.267655 | 0.437826 | | 19500 | 0.240600 | 0.277109 | 0.431731 | | 20000 | 0.237200 | 0.266622 | 0.433994 | | 20500 | 0.231300 | 0.273015 | 0.428868 | | 21000 | 0.227200 | 0.263024 | 0.430161 | | 21500 | 0.220400 | 0.272880 | 0.429607 | | 22000 | 0.218600 | 0.272340 | 0.426883 | | 22500 | 0.213100 | 0.277066 | 0.428407 | | 23000 | 0.205000 | 0.278404 | 0.424020 | | 23500 | 0.200900 | 0.270877 | 0.418987 | | 24000 | 0.199000 | 0.289120 | 0.425821 | | 24500 | 0.196100 | 0.275831 | 0.424066 | | 25000 | 0.191100 | 0.282822 | 0.421850 | | 25500 | 0.190100 | 0.275820 | 0.418248 | | 26000 | 0.178800 | 0.279208 | 0.419125 | | 26500 | 0.183100 | 0.271464 | 0.419218 | | 27000 | 0.177400 | 0.280869 | 0.419680 | | 27500 | 0.171800 | 0.279593 | 0.414924 | | 28000 | 0.172900 | 0.276949 | 0.417648 | | 28500 | 0.164900 | 0.283491 | 0.417786 | | 29000 | 0.164800 | 0.283122 | 0.416078 | | 29500 | 0.165500 | 0.281969 | 0.415801 | | 30000 | 0.163800 | 0.283319 | 0.412753 | | 30500 | 0.153500 | 0.285702 | 0.414046 | | 31000 | 0.156500 | 0.285041 | 0.412615 | | 31500 | 0.150900 | 0.284336 | 0.413723 | | 32000 | 0.151800 | 0.285922 | 0.412292 | | 32500 | 0.149200 | 0.289461 | 0.412153 | | 33000 | 0.145400 | 0.291322 | 0.409567 | | 33500 | 0.145600 | 0.294361 | 0.409614 | | 34000 | 0.144200 | 0.290686 | 0.409059 | | 34500 | 0.143400 | 0.289474 | 0.409844 | | 35000 | 0.143500 | 0.290340 | 0.408367 | | 35500 | 0.143200 | 0.289581 | 0.407351 | | 36000 | 0.138400 | 0.292782 | 0.408736 | | 36500 | 0.137900 | 0.289108 | 0.408044 | | 37000 | 0.138200 | 0.292127 | 0.407166 | | 37500 | 0.134600 | 0.291797 | 0.408413 | | 38000 | 0.139800 | 0.290056 | 0.408090 | | 38500 | 0.136500 | 0.291198 | 0.408090 | | 39000 | 0.137700 | 0.289696 | 0.408044 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 1.18.3 - Tokenizers 0.10.3 #### Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id Akashpb13/Kabyle_xlsr --dataset mozilla-foundation/common_voice_8_0 --config kab --split test ```