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--- |
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language: |
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- uz |
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license: apache-2.0 |
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tags: |
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- whisper-event |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_11_0 |
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- google/fleurs |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Small Uzbek |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: mozilla-foundation/common_voice_11_0 |
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type: mozilla-foundation/common_voice_11_0 |
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config: uz |
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split: test |
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args: da |
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metrics: |
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- type: wer |
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value: 23.650914047642605 |
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name: Wer |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: google/fleurs |
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type: google/fleurs |
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config: uz_uz |
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split: test |
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metrics: |
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- type: wer |
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value: 47.15 |
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name: WER |
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--- |
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<!-- Disclaimer: I've never written a model card before. I'm probably not correctly following standard practices on how they should be written. |
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I'm new to this. I'm sorry --> |
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# Whisper Small Uzbek |
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This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) trained on the mozilla-foundation/common_voice_11_0 uz and google/fleurs uz_uz datasets, and evaluated on the mozilla-foundation/common_voice_11_0 uz dataset. |
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It achieves the following results on the common_voice_11_0 evaluation set: |
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- Loss: 0.3872 |
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- Wer: 23.6509 |
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It achieves the following results on the FLEURS evaluation set: |
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- Wer: 47.15 |
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## Model description |
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This model was created as part of the Whisper fine-tune sprint event. |
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Based on eval, this model achieves a WER of 23.6509 against the Common Voice 11 dataset and 47.15 against the FLEURS dataset. |
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This is a significant improvement over the reported WER of 90.2 recorded on the [Whisper article](https://cdn.openai.com/papers/whisper.pdf): |
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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 was performed using the train and evaluation splits from [Mozilla's Common Voice 11](https://huggingface.co/mozilla-foundation/common_voice_11_0) and [Google's FLEURS](https://huggingface.co/google/fleurs) datasets. |
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Testing was performed using the test splits from the same datasets. |
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## Training procedure |
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Training and CV11 testing was performed using a modified version of the [run_speech_recognition_seq2seq_streaming.py](https://github.com/kamfonas/whisper-fine-tuning-event/blob/e0377f55004667f18b37215d11bf0e54f5bda463/run_speech_recognition_seq2seq_streaming.py) script by farsipal, which enabled training on multiple datasets in a convenient way. |
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FLEURS testing was performed using the standard [run_eval_whisper_streaming.py](https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/run_eval_whisper_streaming.py) script. |
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### Training hyperparameters |
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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: 32 |
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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: 400 |
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- training_steps: 5000 |
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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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| 0.1542 | 0.2 | 1000 | 0.4711 | 30.8413 | |
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| 0.0976 | 0.4 | 2000 | 0.4040 | 26.6464 | |
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| 0.1088 | 1.0 | 3000 | 0.3765 | 24.4952 | |
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| 0.0527 | 1.21 | 4000 | 0.3872 | 23.6509 | |
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| 0.0534 | 1.41 | 5000 | 0.3843 | 23.6817 | |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.7.1.dev0 |
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- Tokenizers 0.13.2 |
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