118 lines
3.1 KiB
Markdown
118 lines
3.1 KiB
Markdown
---
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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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