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--- |
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language: |
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- ar |
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- multilingual |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- hf-asr-leaderboard |
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- whisper-event |
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- generated_from_trainer |
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- Arabic |
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- multilingual |
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- STT |
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datasets: |
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- mozilla-foundation/common_voice_12_0 |
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metrics: |
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- wer |
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model-index: |
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- name: Kalemat-Tech Arabic Speech Recognition Model (STT) |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Speech Recognition |
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dataset: |
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type: mozilla-foundation/common_voice_12_0 |
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name: mozilla-foundation/common_voice_12_0 |
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config: ar |
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split: test |
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args: ar |
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metrics: |
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- type: wer |
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value: 58.5848 |
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name: wer |
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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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# Kalemat-Tech Arabic Speech Recognition Model (STT) - Mohamed Salama |
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# نموذج كلماتك للتعرف على الأصوات العربية الفصحى و تحويلها إلى نصوص |
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# KalemaTech-Arabic-STT-ASR-based-on-Whisper-Small |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on Common_Voice_Arabic_12.0_Augmented. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5362 |
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- Wer: 58.5848 |
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## Example of usage: |
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``` |
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from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq |
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processor = AutoProcessor.from_pretrained("Salama1429/KalemaTech-Arabic-STT-ASR-based-on-Whisper-Small") |
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model = AutoModelForSpeechSeq2Seq.from_pretrained("Salama1429/KalemaTech-Arabic-STT-ASR-based-on-Whisper-Small") |
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``` |
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## Intended uses & limitations |
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Automatic Speech Recognition |
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## Training and evaluation data |
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``` |
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Common_Voice_Arabic_12.0 and I made some augmentations to it as follows: |
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- 25% of the data TimeMasking |
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- 25% of the data SpecAugmentation |
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- 25% of the data WavAugmentation (AddGaussianNoise) |
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- The final dataset is the original common voice plus the augmented files |
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``` |
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## Training procedure |
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### Training hyperparameters |
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``` |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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: 500 |
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- num_epochs: 25 |
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- mixed_precision_training: Native AMP |
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``` |
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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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| 0.2728 | 1.01 | 1000 | 0.3063 | 60.4733 | |
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| 0.1442 | 2.01 | 2000 | 0.2878 | 55.6935 | |
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| 0.0648 | 3.02 | 3000 | 0.3009 | 59.2568 | |
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| 0.0318 | 4.03 | 4000 | 0.3278 | 59.2993 | |
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| 0.0148 | 5.04 | 5000 | 0.3539 | 61.0364 | |
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| 0.0088 | 6.04 | 6000 | 0.3714 | 56.9154 | |
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| 0.0061 | 7.05 | 7000 | 0.3920 | 57.5515 | |
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| 0.0041 | 8.06 | 8000 | 0.4149 | 61.6328 | |
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| 0.0033 | 9.06 | 9000 | 0.4217 | 58.0310 | |
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| 0.0033 | 10.07 | 10000 | 0.4376 | 59.9594 | |
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| 0.0021 | 11.08 | 11000 | 0.4485 | 56.7812 | |
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| 0.0015 | 12.08 | 12000 | 0.4577 | 57.6936 | |
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| 0.0013 | 13.09 | 13000 | 0.4671 | 60.6606 | |
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| 0.0011 | 14.1 | 14000 | 0.4686 | 59.8159 | |
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| 0.0008 | 15.11 | 15000 | 0.4856 | 60.7111 | |
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| 0.0011 | 16.11 | 16000 | 0.4851 | 59.5198 | |
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| 0.0005 | 17.12 | 17000 | 0.4936 | 59.2608 | |
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| 0.0004 | 18.13 | 18000 | 0.4995 | 57.9619 | |
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| 0.0003 | 19.13 | 19000 | 0.5085 | 58.3630 | |
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| 0.0002 | 20.14 | 20000 | 0.5155 | 58.0987 | |
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| 0.0001 | 21.15 | 21000 | 0.5251 | 58.8504 | |
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| 0.0001 | 22.16 | 22000 | 0.5268 | 58.4228 | |
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| 0.0001 | 23.16 | 23000 | 0.5317 | 59.0881 | |
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| 0.0001 | 24.17 | 24000 | 0.5362 | 58.5848 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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