Update app.py
Browse files
app.py
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@@ -189,9 +189,58 @@ def emotion(file_path):
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audio_examples=["Audio 1.wav","Audio 2.wav","Audio 3.wav","Audio 4.wav","Audio 5.wav"]
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import gradio as gr
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# Gradio
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audio_title = "Audio Emotion Detector ποΈ"
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audio_description = gr.Markdown(
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"""Ever wondered what emotions someone's voice might be hiding?
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Prepare to dive into the wild world of vocal emotions with this uproariously entertaining app!
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Just upload an audio clip in WAV or MP3 format, and our trusty emotion detector
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will spill the beans on what kind of emotional rollercoaster ride they're on.
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## Pro Tip
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Make sure the audio clip captures only their voice for maximum hilarity!
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Let's see if they're serenading with sorrow or belting out bursts of bliss! πΆ
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And hey, if you're feeling a bit blue yourself, this app is sure to hit all the right notes
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and lift your spirits!""").value
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audio_article = gr.Markdown(
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"""
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## DISCLAIMER
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This app isn't equipped with psychic powers!
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So, take the results with a pinch of musical humor, or better yet, a dollop of whimsy!
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## FUN FACT
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Our audio emotion detector was trained on a zany collection of audio clips
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that range from dramatic monologues to cat karaoke concerts! It's not just about the tones;
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it's about the tunes too! π±π€
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## DATA DELIGHTS
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Did you know our dataset includes a cacophony of audio clips
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in WAV and MP3 formats? It's like tuning into an emotion-filled radio station
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where every frequency is a new adventure in hilarity! π»
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""").value
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audio_examples=["Audio 1.wav","Audio 2.wav","Audio 3.wav","Audio 4.wav","Audio 5.wav"]
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audio_model=gr.Interface(fn = emotion,
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inputs = gr.Textbox( label='Audio File Path'),
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outputs = gr.Textbox(label='Emotion'),
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title = audio_title,
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examples = audio_examples,
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description = audio_description,
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article=audio_article,
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allow_flagging='never')
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