init commit

app.py

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+
+import json
+
+from altair import value
+from matplotlib.streamplot import OutOfBounds
+from sympy import substitution, viete
+from extract_audio import VideoHelper
+from helpers.srt_generator import SRTGenerator
+from moderator import DetoxifyModerator
+from shorts_generator import ShortsGenerator
+from subtitles import SubtitlesRenderer
+from transcript_detect import *
+from translation import *
+import gradio as gr
+from dotenv import load_dotenv
+
+
+
+def translate_segments(segments,translator: TranslationModel,from_lang,to_lang):
+    transalted_segments = []
+    for segment in segments:
+        translated_segment_text = translator.translate_text(segment['text'],from_lang,to_lang)
+        transalted_segments.append({'text':translated_segment_text,'start':segment['start'],'end':segment['end'],'id':segment['id']})
+    
+    return transalted_segments
+
+
+def main(file,translate_to_lang):
+
+    #Extracting the audio from video
+    video_file_path = file
+    audio_file_path = 'extracted_audio.mp3'
+    video_helper = VideoHelper()
+    print('Extracting audio from video...')
+    video_helper.extract_audio(video_file_path, audio_file_path)
+
+
+    whisper_model = WhisperModel('base')
+
+    print('Transcriping audio file....')
+    transcription = whisper_model.transcribe_audio(audio_file_path)
+
+    print('Generating transctipt text...')
+    transcript_text = whisper_model.get_text(transcription)
+
+    print('Detecting audio language....')
+    detected_language = whisper_model.get_detected_language(transcription)
+
+    print('Generating transcript segments...')
+    transcript_segments = whisper_model.get_segments(transcription)
+    
+
+    # Write the transcription to a text file
+    print('Writing transcript into text file...')
+    transcript_file_path = "transcript.txt"
+    with open(transcript_file_path, "w",encoding="utf-8") as file:
+        file.write(transcript_text)
+
+    # Translate transcript
+    translation_model = TranslationModel()
+    target_language = supported_languages[translate_to_lang]
+
+    print(f'Translating transcript text from {detected_language} to {target_language}...')
+    transalted_text = translation_model.translate_text(transcript_text,detected_language,target_language)
+
+    # print(f'Translating transcript segments from {detected_language} to {target_language}...')
+    # transalted_segments = translate_segments(transcript_segments,translation_model,detected_language,target_language)
+
+    # Write the translation to a text file
+    print('Writing translation text file...')
+    translation_file_path = "translation.txt"
+    with open(translation_file_path, "w",encoding="utf-8") as file:
+        file.write(transalted_text)
+
+    print('Writing transcsript segments and translated segments to json file...')
+    segments_file_path = "segments.json"
+    with open(segments_file_path, "w",encoding="utf-8") as file:
+        json.dump(transcript_segments, file,ensure_ascii=False)
+
+    # print('Writing transcsript segments and translated segments to json file...')
+    # translated_segments_file_path = "translated_segments.json"
+    # with open(translated_segments_file_path, "w",encoding="utf-8") as file:
+    #     json.dump(transalted_segments, file,ensure_ascii=False)
+
+    #Run Moderator to detect toxicity
+    print('Analyzing and detecing toxicity levels...')
+    detoxify_moderator = DetoxifyModerator()
+    result = detoxify_moderator.detect_toxicity(transcript_text)
+    df = detoxify_moderator.format_results(result)
+
+
+    #Render subtitles on video
+    renderer = SubtitlesRenderer()
+    subtitles_file_path = 'segments.json'
+    output_file_path = 'subtitled_video.mp4'
+    subtitled_video = renderer.add_subtitles(video_file_path,subtitles_file_path,output_file_path)
+
+
+    
+    # Generate short videos from video
+    output_srt_file = 'subtitles.srt'
+    print('Generating SRT file...')
+    #Generate srt file
+    SRTGenerator.generate_srt(transcript_segments,output_srt_file)
+    shorts_generator = ShortsGenerator()
+    print('Generating shorts from important scenes...')
+    selected_scenes = shorts_generator.execute(output_srt_file)
+    shorts_path_list = shorts_generator.extract_video_scenes( video_file_path, shorts_generator.extract_scenes(selected_scenes.content))
+
+    return_shorts_list = shorts_path_list + [""] * (3 - len(shorts_path_list))
+    
+
+    return transcript_text, transalted_text, df, subtitled_video, return_shorts_list[0], return_shorts_list[1], return_shorts_list[2]
+
+
+
+def interface_function(file,translate_to_lang,with_transcript=False,with_translations=False,with_subtitles=False,with_shorts=False):
+
+    return main(file,translate_to_lang)
+
+supported_languages = {
+    "Spanish": "es",
+    "French": "fr",
+    "German": "de",
+    "Russian": "ru",
+    "Arabic": "ar",
+    "Hindi": "hi"
+}
+
+
+# Load environment variables from .env file
+load_dotenv()
+
+
+inputs = [gr.Video(label='Content Video'),gr.Dropdown(list(supported_languages.keys()), label="Target Language"),gr.Checkbox(label="Generate Transcript"),
+        gr.Checkbox(label="Translate Transcript"),gr.Checkbox(label="Generate Subtitles"),gr.Checkbox(label="Generate Shorts")]
+
+outputs = [gr.Textbox(label="Transcript"), gr.Textbox(label="Translation"),gr.DataFrame(label="Moderation Results"),gr.Video(label='Output Video with Subtitles')]
+short_outputs = [gr.Video(label=f"Short {i+1}") for i in range(3)]
+outputs.extend(short_outputs)
+demo = gr.Interface(
+fn=interface_function,
+inputs=inputs,
+outputs=outputs,
+title="Rosetta AI",
+description="Content Creation Customization"
+)
+    # with gr.Blocks() as demo:
+    #     file_output = gr.File()
+    #     upload_button = gr.UploadButton("Click to Upload a Video", file_types=["video"], file_count="single")
+    #     upload_button.upload(main, upload_button, ['text','text'])
+
+
+
+demo.launch()
+