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jpdiazpardo
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408afb3
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450a858
Update app.py
Browse files
app.py
CHANGED
@@ -14,7 +14,7 @@ from functions.youtube import get_youtube_video_id
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#---------------------------------------------------------------------
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examples = [[#"When a Demon Defiles a Witch.wav", #"https://www.youtube.com/watch?v=W72Lnz1n-jw&ab_channel=Whitechapel-Topic",
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#"When a Demon Defiles a Witch.wav", #"<iframe src='https://www.youtube.com/embed/W72Lnz1n-jw' title='YouTube video player' frameborder='0' allow='accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture' allowfullscreen></iframe>",
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"When a Demon Defiles a Witch.wav",True
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MODEL_NAME = "openai/whisper-medium"
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BATCH_SIZE = 8
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@@ -49,7 +49,7 @@ title = "Scream: Fine-Tuned Whisper model for automatic gutural speech recogniti
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classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=None)
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#Functions-----------------------------------------------------------------------------------------------------------------------
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def transcribe(file,use_timestamps
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'''inputs: file, return_timestamps'''
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outputs = pipe(file, batch_size=BATCH_SIZE, generate_kwargs={"task": 'transcribe'}, return_timestamps=True)
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text = outputs["text"]
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#---------------------------------------------------------------------
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examples = [[#"When a Demon Defiles a Witch.wav", #"https://www.youtube.com/watch?v=W72Lnz1n-jw&ab_channel=Whitechapel-Topic",
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#"When a Demon Defiles a Witch.wav", #"<iframe src='https://www.youtube.com/embed/W72Lnz1n-jw' title='YouTube video player' frameborder='0' allow='accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture' allowfullscreen></iframe>",
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"When a Demon Defiles a Witch.wav",True]]#, True]]
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MODEL_NAME = "openai/whisper-medium"
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BATCH_SIZE = 8
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classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=None)
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#Functions-----------------------------------------------------------------------------------------------------------------------
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def transcribe(file,use_timestamps):#,sentiment_analysis):#file, return_timestamps, *kwargs):
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'''inputs: file, return_timestamps'''
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outputs = pipe(file, batch_size=BATCH_SIZE, generate_kwargs={"task": 'transcribe'}, return_timestamps=True)
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text = outputs["text"]
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