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Browse files- app.py +27 -0
- requirements.txt +8 -0
app.py
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import gradio as gr
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from transformers import pipeline
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# Load the Whisper model
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model_name = "AventIQ-AI/whisper_small_Automatic_speech_recognition"
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asr_pipeline = pipeline("automatic-speech-recognition", model=model_name)
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def transcribe_audio(audio):
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if audio is None:
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return "⚠️ Please upload or record an audio file."
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transcript = asr_pipeline(audio)["text"]
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return transcript if transcript else "⚠️ No speech detected."
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# Create Gradio UI
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with gr.Blocks() as demo:
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gr.Markdown("## 🎤 Whisper Small - Speech to Text")
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gr.Markdown("Upload an audio file or record your voice to get a transcript.")
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audio_input = gr.Audio(type="filepath", interactive=True, label="🎙️ Upload or Record Audio")
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transcribe_button = gr.Button("🔍 Transcribe")
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output_text = gr.Textbox(label="📝 Transcription Output")
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transcribe_button.click(transcribe_audio, inputs=audio_input, outputs=output_text)
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# Launch the app
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demo.launch()
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requirements.txt
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torch
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transformers
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gradio
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sentencepiece
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torchvision
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huggingface_hub
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pillow
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numpy
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