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Update app.py
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app.py
CHANGED
@@ -10,17 +10,13 @@ import whisper
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import matplotlib as plt
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whisper_model = whisper.load_model('large-v2') # Whisper 모델을 불러오기
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path = "Hyeonsieun/NTtoGT_1epoch"
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tokenizer = T5Tokenizer.from_pretrained(path)
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model = T5ForConditionalGeneration.from_pretrained(path)
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BATCH_SIZE = 8
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FILE_LIMIT_MB = 1000
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YT_LENGTH_LIMIT_S = 3600 # limit to 1 hour YouTube files
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def do_correction(text, model, tokenizer):
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input_text = f"translate the text pronouncing the formula to a LaTeX equation: {text}"
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inputs = tokenizer.encode(
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@@ -46,7 +42,13 @@ def do_correction(text, model, tokenizer):
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return corrected_sentence
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pipe = pipeline(
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task="automatic-speech-recognition",
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model=MODEL_NAME,
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@@ -179,4 +181,5 @@ yt_transcribe = gr.Interface(
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with demo:
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gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
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demo.launch(enable_queue=True)
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import matplotlib as plt
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# whisper_model = whisper.load_model('large-v2') # Whisper 모델을 불러오기
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path = "Hyeonsieun/NTtoGT_1epoch"
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tokenizer = T5Tokenizer.from_pretrained(path)
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model = T5ForConditionalGeneration.from_pretrained(path)
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def do_correction(text, model, tokenizer):
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input_text = f"translate the text pronouncing the formula to a LaTeX equation: {text}"
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inputs = tokenizer.encode(
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)
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return corrected_sentence
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# corrected_sentence = do_correction(sentence, model, tokenizer)
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gr.Interface(fn=yt_do_correction, inputs="text", outputs="text")
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'''
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pipe = pipeline(
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task="automatic-speech-recognition",
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model=MODEL_NAME,
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with demo:
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gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
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demo.launch(enable_queue=True)
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'''
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