Artificial-superintelligence
commited on
Update app.py
Browse files
app.py
CHANGED
@@ -1,16 +1,14 @@
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import streamlit as st
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from moviepy.editor import VideoFileClip
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import whisper
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from
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from gtts import gTTS
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import tempfile
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import os
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# Initialize Whisper model
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whisper_model = whisper.load_model("base") # Ensure the model is installed from the correct Whisper library
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except AttributeError:
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st.error("Whisper model could not be loaded. Ensure that Whisper is installed from GitHub.")
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# Language options
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LANGUAGES = {
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@@ -46,15 +44,22 @@ if video_file:
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os.remove(temp_video_path)
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st.stop()
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# Transcribe audio using Whisper
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try:
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# Translate text to the target language
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translator = Translator(
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translated_text = translator.translate(original_text)
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st.write(f"Translated Text ({target_language}):", translated_text)
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# Convert translated text to speech
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@@ -72,3 +77,13 @@ if video_file:
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os.remove(temp_video_path)
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os.remove(audio_path)
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os.remove(audio_output_path)
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import streamlit as st
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from moviepy.editor import VideoFileClip
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import whisper
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from googletrans import Translator
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from gtts import gTTS
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import tempfile
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import os
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import numpy as np
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# Initialize Whisper model
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whisper_model = whisper.load_model("base")
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# Language options
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LANGUAGES = {
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os.remove(temp_video_path)
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st.stop()
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# Transcribe audio using Whisper in chunks
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try:
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# Load the audio file with Whisper
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audio = whisper.load_audio(audio_path)
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audio_segments = split_audio(audio, segment_length=30) # Split into 30-second segments
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original_text = ""
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for segment in audio_segments:
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result = whisper_model.transcribe(segment)
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original_text += result["text"] + " " # Concatenate transcriptions
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st.write("Original Transcription:", original_text.strip())
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# Translate text to the target language
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translator = Translator()
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translated_text = translator.translate(original_text.strip(), dest=LANGUAGES[target_language]).text
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st.write(f"Translated Text ({target_language}):", translated_text)
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# Convert translated text to speech
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os.remove(temp_video_path)
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os.remove(audio_path)
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os.remove(audio_output_path)
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def split_audio(audio, segment_length=30):
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"""Split audio into segments of specified length in seconds."""
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total_length = audio.shape[1] # Total length in seconds
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segments = []
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for start in range(0, total_length, segment_length):
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end = min(start + segment_length, total_length)
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segment = audio[:, start:end] # Append the segment
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segments.append(segment)
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return segments
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