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--- |
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library_name: transformers |
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language: |
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- tr |
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license: mit |
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base_model: openai/whisper-large-v3-turbo |
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tags: |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_17_0 |
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metrics: |
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- wer |
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model-index: |
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- name: "Whisper Large v3 Turbo TR - Selim \xC7ava\u015F" |
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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 17.0 |
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type: mozilla-foundation/common_voice_17_0 |
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config: tr |
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split: test |
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args: 'config: tr, split: test' |
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metrics: |
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- name: Wer |
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type: wer |
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value: 18.92291759135967 |
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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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# Whisper Large v3 Turbo TR - Selim Çavaş |
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This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the Common Voice 17.0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3123 |
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- Wer: 18.9229 |
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## Intended uses & limitations |
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This model can be used in various application areas, including |
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- Transcription of Turkish language |
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- Voice commands |
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- Automatic subtitling for Turkish videos |
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## How To Use |
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```python |
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import torch |
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from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline |
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device = "cuda:0" if torch.cuda.is_available() else "cpu" |
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torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 |
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model_id = "selimc/whisper-large-v3-turbo-turkish" |
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model = AutoModelForSpeechSeq2Seq.from_pretrained( |
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model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True |
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) |
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model.to(device) |
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processor = AutoProcessor.from_pretrained(model_id) |
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pipe = pipeline( |
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"automatic-speech-recognition", |
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model=model, |
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tokenizer=processor.tokenizer, |
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feature_extractor=processor.feature_extractor, |
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chunk_length_s=30, |
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batch_size=16, |
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return_timestamps=True, |
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torch_dtype=torch_dtype, |
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device=device, |
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) |
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result = pipe("test.mp3") |
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print(result["text"]) |
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``` |
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## Training |
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Due to colab GPU constraints I was able to train using only the 25% of the Turkish data available in the Common Voice 17.0 dataset. 😔 |
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Got a GPU to spare? Let's collaborate and take this model to the next level! 🚀 |
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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: 16 |
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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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- training_steps: 4000 |
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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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| 0.1223 | 1.6 | 1000 | 0.3187 | 24.4415 | |
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| 0.0501 | 3.2 | 2000 | 0.3123 | 20.9720 | |
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| 0.0226 | 4.8 | 3000 | 0.3010 | 19.6183 | |
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| 0.001 | 6.4 | 4000 | 0.3123 | 18.9229 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.1 |
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