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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- common_voice_11_0 |
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metrics: |
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- wer |
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model-index: |
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- name: fine-tune-wav2vec2-large-xls-r-1b-sw |
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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_11_0 |
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type: common_voice_11_0 |
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config: sw |
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split: test[:1%] |
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args: sw |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.5834348355663824 |
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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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# fine-tune-wav2vec2-large-xls-r-300m-sw |
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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 common_voice_11_0 swahili dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2834 |
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- Wer: 0.5834 |
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## Model description |
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This model is fine-tuned for general swahili speech recognition tasks. You can watch our hour long [webinar](https://drive.google.com/file/d/1OkLx3d9xivdyxH8yYsZtwObhEX5Ptn5y/view?usp=drive_link) and see the [slides](https://docs.google.com/presentation/d/1sExJLwZLMNMKGnpuxy-ttF5KqDXJyKK2jNNTUabo5_Q/edit?usp=sharing) on this work. |
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## Intended uses & limitations |
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The intention is to transcribe general swahili speeches. With further development, we'll fine-tune the model for domain-specific (we are focused on hospital tasks) swahili conversations. |
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## Training and evaluation data |
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To appreciate the transformation we did on the data, you can read our [blog on data preparation](https://medium.com/@gitau_am/from-raw-data-to-accurate-speech-recognition-asr-my-journey-of-data-preparation-df3a1b0dee3a). |
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## Training procedure |
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We also [documented](https://medium.com/@gitau_am/exploring-asr-model-development-fine-tuning-xls-r-wav2vec2-model-with-swahili-data-b95134d116b8) some lessons from the fine-tuning exercise. |
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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: 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: 500 |
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- num_epochs: 9 |
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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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| No log | 1.72 | 200 | 3.0092 | 1.0 | |
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| 4.1305 | 3.43 | 400 | 2.9159 | 1.0 | |
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| 4.1305 | 5.15 | 600 | 1.4301 | 0.7040 | |
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| 0.9217 | 6.87 | 800 | 1.3143 | 0.6529 | |
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| 0.9217 | 8.58 | 1000 | 1.2834 | 0.5834 | |
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### Framework versions |
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- Transformers 4.27.0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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