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