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import gradio as gr |
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models = { |
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"Whisper Small": "openai/whisper-small.en", |
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"Wav2Vec2": "facebook/wav2vec2-base-960h" |
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} |
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whisper = gr.Interface.load(f"huggingface/{models['Whisper Small']}") |
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wav2vec = gr.Interface.load(f"huggingface/{models['Wav2Vec2']}") |
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def transcribe_with_all(audio_path): |
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whisper_result = whisper(audio_path) |
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wav2vec_result = wav2vec(audio_path) |
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return whisper_result, wav2vec_result |
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with gr.Blocks() as demo: |
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gr.Markdown("# مقارنة بين نماذج التعرف على الصوت") |
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gr.Markdown("قارن بين نموذج Whisper و Wav2Vec2") |
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audio_input = gr.Audio(type="filepath", label="ملف صوتي") |
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transcribe_btn = gr.Button("تحويل النص") |
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with gr.Row(): |
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with gr.Column(): |
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gr.Markdown("### Whisper Small (OpenAI)") |
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whisper_output = gr.Textbox(label="نتيجة Whisper") |
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with gr.Column(): |
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gr.Markdown("### Wav2Vec2 (Facebook)") |
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wav2vec_output = gr.Textbox(label="نتيجة Wav2Vec2") |
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transcribe_btn.click( |
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fn=transcribe_with_all, |
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inputs=audio_input, |
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outputs=[whisper_output, wav2vec_output] |
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) |
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demo.launch(share=True) |
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