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import gradio as gr
from transformers import pipeline
from gtts import gTTS
from pydub import AudioSegment
from summarizer import summarize_file
#text to sppech function
def text_to_speech(text):
# Convert text to speech with a US accent using gTTS
tts = gTTS(text=text, lang='en', tld='us', slow=False)
tts.save('temp.mp3')
# Load the audio file
audio = AudioSegment.from_file('temp.mp3')
# Adjust the speed to approximately 170 wpm
playback_speed = 1.20
audio = audio.speedup(playback_speed=playback_speed)
# Save and return the adjusted audio file
final_filename = 'text_to_speech.mp3'
audio.export(final_filename, format='mp3')
return final_filename
def process_files():
return (gr.update(interactive=True,
elem_id='summary_button'),
gr.update(interactive = True, elem_id = 'summarization_method')
)
def get_summarization_method(option):
return option
def text_to_audio(text, model_name="facebook/fastspeech2-en-ljspeech"):
# Initialize the TTS pipeline
tts_pipeline = pipeline("text-to-speech", model=model_name)
# Generate the audio from text
audio = tts_pipeline(text)
# Save the audio to a file
audio_path = "output.wav"
with open(audio_path, "wb") as file:
file.write(audio["wav"])
return audio_path
def generate_output(method, file):
summary_text = summarize_file(method, file)
audio_summary = text_to_speech(summary_text)
return summary_text, audio_summary |