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--- |
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language: |
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- as |
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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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- as |
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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: wav2vec2-large-xls-r-300m-as-g1 |
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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: as |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 0.6540934419202743 |
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- name: Test CER |
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type: cer |
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value: 0.21454042646095625 |
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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: Robust Speech Event - Dev Data |
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type: speech-recognition-community-v2/dev_data |
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args: as |
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metrics: |
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- name: Test WER |
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type: wer |
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value: NA |
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- name: Test CER |
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type: cer |
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value: NA |
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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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# wav2vec2-large-xls-r-300m-as-g1 |
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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 - AS dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3327 |
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- Wer: 0.5744 |
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### Evaluation Commands |
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1. To evaluate on mozilla-foundation/common_voice_8_0 with test split |
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python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-as-g1 --dataset mozilla-foundation/common_voice_8_0 --config as --split test --log_outputs |
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2. To evaluate on speech-recognition-community-v2/dev_data |
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Assamese language isn't available in speech-recognition-community-v2/dev_data |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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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: 2 |
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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: 1000 |
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- num_epochs: 200 |
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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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| 14.1958 | 5.26 | 100 | 7.1919 | 1.0 | |
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| 5.0035 | 10.51 | 200 | 3.9362 | 1.0 | |
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| 3.6193 | 15.77 | 300 | 3.4451 | 1.0 | |
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| 3.4852 | 21.05 | 400 | 3.3536 | 1.0 | |
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| 2.8489 | 26.31 | 500 | 1.6451 | 0.9100 | |
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| 0.9568 | 31.56 | 600 | 1.0514 | 0.7561 | |
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| 0.4865 | 36.82 | 700 | 1.0434 | 0.7184 | |
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| 0.322 | 42.1 | 800 | 1.0825 | 0.7210 | |
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| 0.2383 | 47.36 | 900 | 1.1304 | 0.6897 | |
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| 0.2136 | 52.62 | 1000 | 1.1150 | 0.6854 | |
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| 0.179 | 57.87 | 1100 | 1.2453 | 0.6875 | |
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| 0.1539 | 63.15 | 1200 | 1.2211 | 0.6704 | |
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| 0.1303 | 68.41 | 1300 | 1.2859 | 0.6747 | |
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| 0.1183 | 73.67 | 1400 | 1.2775 | 0.6721 | |
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| 0.0994 | 78.92 | 1500 | 1.2321 | 0.6404 | |
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| 0.0991 | 84.21 | 1600 | 1.2766 | 0.6524 | |
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| 0.0887 | 89.46 | 1700 | 1.3026 | 0.6344 | |
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| 0.0754 | 94.72 | 1800 | 1.3199 | 0.6704 | |
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| 0.0693 | 99.97 | 1900 | 1.3044 | 0.6361 | |
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| 0.0568 | 105.26 | 2000 | 1.3541 | 0.6254 | |
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| 0.0536 | 110.51 | 2100 | 1.3320 | 0.6249 | |
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| 0.0529 | 115.77 | 2200 | 1.3370 | 0.6271 | |
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| 0.048 | 121.05 | 2300 | 1.2757 | 0.6031 | |
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| 0.0419 | 126.31 | 2400 | 1.2661 | 0.6172 | |
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| 0.0349 | 131.56 | 2500 | 1.2897 | 0.6048 | |
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| 0.0309 | 136.82 | 2600 | 1.2688 | 0.5962 | |
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| 0.0278 | 142.1 | 2700 | 1.2885 | 0.5954 | |
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| 0.0254 | 147.36 | 2800 | 1.2988 | 0.5915 | |
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| 0.0223 | 152.62 | 2900 | 1.3153 | 0.5941 | |
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| 0.0216 | 157.87 | 3000 | 1.2936 | 0.5937 | |
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| 0.0186 | 163.15 | 3100 | 1.2906 | 0.5877 | |
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| 0.0156 | 168.41 | 3200 | 1.3476 | 0.5962 | |
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| 0.0158 | 173.67 | 3300 | 1.3363 | 0.5847 | |
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| 0.0142 | 178.92 | 3400 | 1.3367 | 0.5847 | |
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| 0.0153 | 184.21 | 3500 | 1.3105 | 0.5757 | |
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| 0.0119 | 189.46 | 3600 | 1.3255 | 0.5705 | |
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| 0.0115 | 194.72 | 3700 | 1.3340 | 0.5787 | |
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| 0.0103 | 199.97 | 3800 | 1.3327 | 0.5744 | |
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### Framework versions |
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- Transformers 4.16.2 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.0 |
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