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--- |
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language: |
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- ar |
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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_7_0 |
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- generated_from_trainer |
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- robust-speech-event |
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- ar |
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datasets: |
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- mozilla-foundation/common_voice_7_0 |
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model-index: |
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- name: 'XLS-R-300M - Arabic' |
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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 7 |
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type: mozilla-foundation/common_voice_7_0 |
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args: ar |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 47.54 |
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- name: Test CER |
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type: cer |
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value: 17.64 |
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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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# |
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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 - AR dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4502 |
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- Wer: 0.4783 |
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## Model description |
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More information needed |
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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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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 7.5e-05 |
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- train_batch_size: 8 |
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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: 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: 2000 |
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- num_epochs: 5.0 |
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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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| 4.7972 | 0.43 | 500 | 5.1401 | 1.0 | |
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| 3.3241 | 0.86 | 1000 | 3.3220 | 1.0 | |
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| 3.1432 | 1.29 | 1500 | 3.0806 | 0.9999 | |
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| 2.9297 | 1.72 | 2000 | 2.5678 | 1.0057 | |
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| 2.2593 | 2.14 | 2500 | 1.1068 | 0.8218 | |
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| 2.0504 | 2.57 | 3000 | 0.7878 | 0.7114 | |
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| 1.937 | 3.0 | 3500 | 0.6955 | 0.6450 | |
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| 1.8491 | 3.43 | 4000 | 0.6452 | 0.6304 | |
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| 1.803 | 3.86 | 4500 | 0.5961 | 0.6042 | |
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| 1.7545 | 4.29 | 5000 | 0.5550 | 0.5748 | |
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| 1.7045 | 4.72 | 5500 | 0.5374 | 0.5743 | |
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| 1.6733 | 5.15 | 6000 | 0.5337 | 0.5404 | |
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| 1.6761 | 5.57 | 6500 | 0.5054 | 0.5266 | |
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| 1.655 | 6.0 | 7000 | 0.4926 | 0.5243 | |
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| 1.6252 | 6.43 | 7500 | 0.4946 | 0.5183 | |
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| 1.6209 | 6.86 | 8000 | 0.4915 | 0.5194 | |
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| 1.5772 | 7.29 | 8500 | 0.4725 | 0.5104 | |
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| 1.5602 | 7.72 | 9000 | 0.4726 | 0.5097 | |
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| 1.5783 | 8.15 | 9500 | 0.4667 | 0.4956 | |
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| 1.5442 | 8.58 | 10000 | 0.4685 | 0.4937 | |
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| 1.5597 | 9.01 | 10500 | 0.4708 | 0.4957 | |
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| 1.5406 | 9.43 | 11000 | 0.4539 | 0.4810 | |
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| 1.5274 | 9.86 | 11500 | 0.4502 | 0.4783 | |
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### Framework versions |
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- Transformers 4.16.0.dev0 |
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- Pytorch 1.10.1+cu102 |
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- Datasets 1.17.1.dev0 |
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- Tokenizers 0.11.0 |
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