Update app.py
Browse files
app.py
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from transformers import pipeline
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tts = pipeline("text-to-speech")
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st.sidebar.markdown("<h3 style='text-align: center; font-size: 16px; background-color: white; color: black;'>TTS - Pipeline</h3>", unsafe_allow_html=True)
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# Create a text area for user input
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STATEMENT = st.sidebar.text_area('Enter Text', DEFAULT_STATEMENT, height=150)
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# Enable the button only if there is text in the TTS variable
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if STATEMENT:
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else:
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# ################ CHAT BOT - main area #################
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from transformers import pipeline
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import sounddevice as sd # Import for audio playback (optional)
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# Load the pipeline
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tts = pipeline("text-to-speech")
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st.sidebar.markdown("<h3 style='text-align: center; font-size: 16px; background-color: white; color: black;'>TTS - Pipeline</h3>", unsafe_allow_html=True)
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DEFAULT_STATEMENT = "This is a sample text to be converted to speech."
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# Create a text area for user input
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STATEMENT = st.sidebar.text_area('Enter Text', DEFAULT_STATEMENT, height=150)
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# Enable the button only if there is text in the TTS variable
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if STATEMENT:
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if st.sidebar.button('TTS'):
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# Text to generate speech from
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text = STATEMENT # Use the user input from STATEMENT
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# Generate speech
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speech = tts(text)
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# Access the audio waveform from the dictionary (assuming key name is 'waveform')
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audio_data = speech['waveform']
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# Optional: Save the audio to a file (uncomment if needed)
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# sd.write(audio_data, samplerate=speech['sampling_rate']) # Adjust samplerate if necessary
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# with open("sample_tts.wav", "wb") as f:
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# f.write(audio_data)
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# Optional: Play the audio directly in Streamlit (uncomment if needed)
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sd.play(audio_data, samplerate=speech['sampling_rate']) # Adjust samplerate if necessary
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st.sidebar.write('Text converted to speech')
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else:
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st.sidebar.button('TTS', disabled=True)
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# st.warning(' Please enter Statement!')
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# ################ CHAT BOT - main area #################
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