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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- speech |
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- audio |
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- transformers- peft |
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- lora |
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- adapter |
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library_name: transformers |
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pipeline_tag: automatic-speech-recognition |
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--- |
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# Bruno7/ksa-whisper-model |
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## Model Description |
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Fine-tuned Arabic Whisper model for Saudi dialect |
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## Base Model |
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This adapter is designed to work with: `openai/whisper-large-v3` |
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## Usage |
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```python |
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from transformers import pipeline |
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from peft import PeftModel, PeftConfig |
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# Load the adapter configuration |
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config = PeftConfig.from_pretrained("Bruno7/ksa-whisper-model") |
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# Load base model and apply adapter |
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pipe = pipeline( |
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"automatic-speech-recognition", |
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model=config.base_model_name_or_path, |
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device="cuda" if torch.cuda.is_available() else "cpu" |
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) |
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# Load and apply the adapter |
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model = PeftModel.from_pretrained(pipe.model, "Bruno7/ksa-whisper-model") |
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pipe.model = model |
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# Process audio |
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result = pipe("path_to_audio.wav") |
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print(result["text"]) |
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``` |
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### Alternative Usage (Direct Loading) |
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```python |
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from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq |
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from peft import PeftModel |
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# Load base model and processor |
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processor = AutoProcessor.from_pretrained("openai/whisper-large-v3") |
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model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large-v3") |
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# Apply adapter |
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model = PeftModel.from_pretrained(model, "Bruno7/ksa-whisper-model") |
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# Your inference code here |
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``` |
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## Model Architecture |
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This is a PEFT (Parameter-Efficient Fine-Tuning) adapter model that modifies a base Whisper model for improved performance on specific domains or languages. The adapter uses LoRA (Low-Rank Adaptation) techniques to efficiently fine-tune the model while keeping the parameter count minimal. |
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## Inference |
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This adapter can be applied to the base model for domain-specific speech recognition tasks. |
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## Limitations |
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- Requires the base model to be loaded separately |
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- Performance may vary with different audio qualities and accents |
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- Requires audio preprocessing for optimal results |
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