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import argparse
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import atexit
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import json
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import logging
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import os
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import signal
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import sys
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import time
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import webbrowser
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), 'App_Function_Libraries')))
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from App_Function_Libraries.Book_Ingestion_Lib import ingest_folder, ingest_text_file
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from App_Function_Libraries.Chunk_Lib import semantic_chunk_long_file
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from App_Function_Libraries.Gradio_Related import launch_ui
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from App_Function_Libraries.Local_LLM_Inference_Engine_Lib import cleanup_process, local_llm_function
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from App_Function_Libraries.Local_Summarization_Lib import summarize_with_llama, summarize_with_kobold, \
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summarize_with_oobabooga, summarize_with_tabbyapi, summarize_with_vllm, summarize_with_local_llm
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from App_Function_Libraries.Summarization_General_Lib import summarize_with_openai, summarize_with_anthropic, \
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summarize_with_cohere, summarize_with_groq, summarize_with_openrouter, summarize_with_deepseek, \
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summarize_with_huggingface, perform_transcription, perform_summarization
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from App_Function_Libraries.Audio_Transcription_Lib import convert_to_wav, speech_to_text
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from App_Function_Libraries.Local_File_Processing_Lib import read_paths_from_file, process_local_file
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from App_Function_Libraries.SQLite_DB import add_media_to_database, is_valid_url
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from App_Function_Libraries.System_Checks_Lib import cuda_check, platform_check, check_ffmpeg
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from App_Function_Libraries.Utils import load_and_log_configs, sanitize_filename, create_download_directory, extract_text_from_segments
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from App_Function_Libraries.Video_DL_Ingestion_Lib import download_video, extract_video_info
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import requests
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log_level = "DEBUG"
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logging.basicConfig(level=getattr(logging, log_level), format='%(asctime)s - %(levelname)s - %(message)s')
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os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
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custom_prompt_input = ("Above is the transcript of a video. Please read through the transcript carefully. Identify the "
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"main topics that are discussed over the course of the transcript. Then, summarize the key points about each main "
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"topic in bullet points. The bullet points should cover the key information conveyed about each topic in the video, "
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"but should be much shorter than the full transcript. Please output your bullet point summary inside <bulletpoints> "
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"tags.")
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whisper_models = ["small", "medium", "small.en", "medium.en", "medium", "large", "large-v1", "large-v2", "large-v3",
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"distil-large-v2", "distil-medium.en", "distil-small.en"]
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server_mode = False
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share_public = False
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abc_xyz = """
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Database Setup
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Config Loading
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System Checks
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DataBase Functions
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Processing Paths and local file handling
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Video Download/Handling
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Audio Transcription
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Diarization
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Chunking-related Techniques & Functions
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Tokenization-related Techniques & Functions
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Summarizers
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Gradio UI
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Main
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"""
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os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
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whisper_models = ["small", "medium", "small.en", "medium.en", "medium", "large", "large-v1", "large-v2", "large-v3",
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"distil-large-v2", "distil-medium.en", "distil-small.en"]
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source_languages = {
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"en": "English",
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"zh": "Chinese",
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"de": "German",
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"es": "Spanish",
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"ru": "Russian",
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"ko": "Korean",
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"fr": "French"
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}
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source_language_list = [key[0] for key in source_languages.items()]
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def print_hello():
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print(r"""_____ _ ________ _ _
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|_ _|| | / /| _ \| | | | _
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| | | | / / | | | || | | |(_)
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| | | | / / | | | || |/\| |
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| | | |____ / / | |/ / \ /\ / _
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\_/ \_____//_/ |___/ \/ \/ (_)
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_ _
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| | | |
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| |_ ___ ___ | | ___ _ __ __ _
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| __| / _ \ / _ \ | | / _ \ | '_ \ / _` |
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| |_ | (_) || (_) | | || (_) || | | || (_| | _
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\__| \___/ \___/ |_| \___/ |_| |_| \__, |( )
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__/ ||/
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|___/
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_ _ _ _ _ _ _
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| |(_) | | ( )| | | | | |
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__| | _ __| | _ __ |/ | |_ __ __ __ _ | |_ ___ | |__
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/ _` || | / _` || '_ \ | __| \ \ /\ / / / _` || __| / __|| '_ \
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| (_| || || (_| || | | | | |_ \ V V / | (_| || |_ | (__ | | | |
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\__,_||_| \__,_||_| |_| \__| \_/\_/ \__,_| \__| \___||_| |_|
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""")
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time.sleep(1)
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return
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def main(input_path, api_name=None, api_key=None,
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num_speakers=2,
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whisper_model="small.en",
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offset=0,
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vad_filter=False,
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download_video_flag=False,
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custom_prompt=None,
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overwrite=False,
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rolling_summarization=False,
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detail=0.01,
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keywords=None,
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llm_model=None,
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time_based=False,
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set_chunk_txt_by_words=False,
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set_max_txt_chunk_words=0,
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set_chunk_txt_by_sentences=False,
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set_max_txt_chunk_sentences=0,
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set_chunk_txt_by_paragraphs=False,
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set_max_txt_chunk_paragraphs=0,
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set_chunk_txt_by_tokens=False,
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set_max_txt_chunk_tokens=0,
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ingest_text_file=False,
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chunk=False,
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max_chunk_size=2000,
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chunk_overlap=100,
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chunk_unit='tokens',
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summarize_chunks=None,
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diarize=False
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):
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global detail_level_number, summary, audio_file, transcription_text, info_dict
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detail_level = detail
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print(f"Keywords: {keywords}")
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if not input_path:
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return []
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start_time = time.monotonic()
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paths = [input_path] if not os.path.isfile(input_path) else read_paths_from_file(input_path)
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results = []
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for path in paths:
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try:
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if path.startswith('http'):
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info_dict, title = extract_video_info(path)
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download_path = create_download_directory(title)
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video_path = download_video(path, download_path, info_dict, download_video_flag)
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if video_path:
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if diarize:
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audio_file, segments = perform_transcription(video_path, offset, whisper_model, vad_filter, diarize=True)
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transcription_text = {'audio_file': audio_file, 'transcription': segments}
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else:
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audio_file, segments = perform_transcription(video_path, offset, whisper_model, vad_filter)
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transcription_text = {'audio_file': audio_file, 'transcription': segments}
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if rolling_summarization == True:
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pass
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elif api_name:
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summary = perform_summarization(api_name, transcription_text, custom_prompt_input, api_key)
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else:
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summary = None
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if summary:
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summary_file_path = os.path.join(download_path, f"{transcription_text}_summary.txt")
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with open(summary_file_path, 'w') as file:
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file.write(summary)
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add_media_to_database(path, info_dict, segments, summary, keywords, custom_prompt_input, whisper_model)
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else:
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logging.error(f"Failed to download video: {path}")
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elif chunk and path.lower().endswith('.txt'):
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chunks = semantic_chunk_long_file(path, max_chunk_size, chunk_overlap)
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if chunks:
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chunks_data = {
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"file_path": path,
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"chunk_unit": chunk_unit,
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"max_chunk_size": max_chunk_size,
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"chunk_overlap": chunk_overlap,
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"chunks": []
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}
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summaries_data = {
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"file_path": path,
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"summarization_method": summarize_chunks,
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"summaries": []
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}
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for i, chunk_text in enumerate(chunks):
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chunk_info = {
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"chunk_id": i + 1,
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"text": chunk_text
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}
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chunks_data["chunks"].append(chunk_info)
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if summarize_chunks:
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summary = None
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if summarize_chunks == 'openai':
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summary = summarize_with_openai(api_key, chunk_text, custom_prompt)
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elif summarize_chunks == 'anthropic':
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summary = summarize_with_anthropic(api_key, chunk_text, custom_prompt)
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elif summarize_chunks == 'cohere':
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summary = summarize_with_cohere(api_key, chunk_text, custom_prompt)
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elif summarize_chunks == 'groq':
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summary = summarize_with_groq(api_key, chunk_text, custom_prompt)
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elif summarize_chunks == 'local-llm':
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summary = summarize_with_local_llm(chunk_text, custom_prompt)
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if summary:
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summary_info = {
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"chunk_id": i + 1,
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"summary": summary
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}
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summaries_data["summaries"].append(summary_info)
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else:
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logging.warning(f"Failed to generate summary for chunk {i + 1}")
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chunks_file_path = f"{path}_chunks.json"
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with open(chunks_file_path, 'w', encoding='utf-8') as f:
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json.dump(chunks_data, f, ensure_ascii=False, indent=2)
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logging.info(f"All chunks saved to {chunks_file_path}")
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if summarize_chunks:
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summaries_file_path = f"{path}_summaries.json"
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with open(summaries_file_path, 'w', encoding='utf-8') as f:
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json.dump(summaries_data, f, ensure_ascii=False, indent=2)
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logging.info(f"All summaries saved to {summaries_file_path}")
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logging.info(f"File {path} chunked into {len(chunks)} parts using {chunk_unit} as the unit.")
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else:
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logging.error(f"Failed to chunk file {path}")
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else:
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download_path, info_dict, urls_or_media_file = process_local_file(path)
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if isinstance(urls_or_media_file, list):
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for url in urls_or_media_file:
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for item in urls_or_media_file:
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if item.startswith(('http://', 'https://')):
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info_dict, title = extract_video_info(url)
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download_path = create_download_directory(title)
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video_path = download_video(url, download_path, info_dict, download_video_flag)
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if video_path:
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if diarize:
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audio_file, segments = perform_transcription(video_path, offset, whisper_model, vad_filter, diarize=True)
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else:
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audio_file, segments = perform_transcription(video_path, offset, whisper_model, vad_filter)
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transcription_text = {'audio_file': audio_file, 'transcription': segments}
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if rolling_summarization:
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text = extract_text_from_segments(segments)
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elif api_name:
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summary = perform_summarization(api_name, transcription_text, custom_prompt_input, api_key)
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else:
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summary = None
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if summary:
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summary_file_path = os.path.join(download_path, f"{transcription_text}_summary.txt")
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with open(summary_file_path, 'w') as file:
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file.write(summary)
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add_media_to_database(url, info_dict, segments, summary, keywords, custom_prompt_input, whisper_model)
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else:
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logging.error(f"Failed to download video: {url}")
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else:
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media_path = urls_or_media_file
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if media_path.lower().endswith(('.txt', '.md')):
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if media_path.lower().endswith('.txt'):
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result = ingest_text_file(media_path)
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logging.info(result)
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elif media_path.lower().endswith(('.mp4', '.avi', '.mov')):
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if diarize:
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audio_file, segments = perform_transcription(media_path, offset, whisper_model, vad_filter, diarize=True)
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else:
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audio_file, segments = perform_transcription(media_path, offset, whisper_model, vad_filter)
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elif media_path.lower().endswith(('.wav', '.mp3', '.m4a')):
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if diarize:
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segments = speech_to_text(media_path, whisper_model=whisper_model, vad_filter=vad_filter, diarize=True)
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else:
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segments = speech_to_text(media_path, whisper_model=whisper_model, vad_filter=vad_filter)
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else:
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logging.error(f"Unsupported media file format: {media_path}")
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continue
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transcription_text = {'media_path': path, 'audio_file': media_path, 'transcription': segments}
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if rolling_summarization:
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pass
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elif api_name:
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summary = perform_summarization(api_name, transcription_text, custom_prompt_input, api_key)
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else:
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summary = None
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if summary:
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summary_file_path = os.path.join(download_path, f"{transcription_text}_summary.txt")
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with open(summary_file_path, 'w') as file:
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file.write(summary)
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add_media_to_database(path, info_dict, segments, summary, keywords, custom_prompt_input, whisper_model)
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except Exception as e:
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logging.error(f"Error processing {path}: {str(e)}")
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continue
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return transcription_text
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def signal_handler(sig, frame):
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logging.info('Signal handler called with signal: %s', sig)
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cleanup_process()
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sys.exit(0)
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if __name__ == "__main__":
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signal.signal(signal.SIGINT, signal_handler)
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signal.signal(signal.SIGTERM, signal_handler)
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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|
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loaded_config_data = load_and_log_configs()
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|
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if loaded_config_data:
|
|
logging.info("Main: Configuration loaded successfully")
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|
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else:
|
|
print("Failed to load configuration")
|
|
|
|
|
|
print_hello()
|
|
|
|
transcription_text = None
|
|
|
|
parser = argparse.ArgumentParser(
|
|
description='Transcribe and summarize videos.',
|
|
epilog='''
|
|
Sample commands:
|
|
1. Simple Sample command structure:
|
|
summarize.py <path_to_video> -api openai -k tag_one tag_two tag_three
|
|
|
|
2. Rolling Summary Sample command structure:
|
|
summarize.py <path_to_video> -api openai -prompt "custom_prompt_goes_here-is-appended-after-transcription" -roll -detail 0.01 -k tag_one tag_two tag_three
|
|
|
|
3. FULL Sample command structure:
|
|
summarize.py <path_to_video> -api openai -ns 2 -wm small.en -off 0 -vad -log INFO -prompt "custom_prompt" -overwrite -roll -detail 0.01 -k tag_one tag_two tag_three
|
|
|
|
4. Sample command structure for UI:
|
|
summarize.py -gui -log DEBUG
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|
''',
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|
formatter_class=argparse.RawTextHelpFormatter
|
|
)
|
|
parser.add_argument('input_path', type=str, help='Path or URL of the video', nargs='?')
|
|
parser.add_argument('-v', '--video', action='store_true', help='Download the video instead of just the audio')
|
|
parser.add_argument('-api', '--api_name', type=str, help='API name for summarization (optional)')
|
|
parser.add_argument('-key', '--api_key', type=str, help='API key for summarization (optional)')
|
|
parser.add_argument('-ns', '--num_speakers', type=int, default=2, help='Number of speakers (default: 2)')
|
|
parser.add_argument('-wm', '--whisper_model', type=str, default='small',
|
|
help='Whisper model (default: small)| Options: tiny.en, tiny, base.en, base, small.en, small, medium.en, '
|
|
'medium, large-v1, large-v2, large-v3, large, distil-large-v2, distil-medium.en, '
|
|
'distil-small.en')
|
|
parser.add_argument('-off', '--offset', type=int, default=0, help='Offset in seconds (default: 0)')
|
|
parser.add_argument('-vad', '--vad_filter', action='store_true', help='Enable VAD filter')
|
|
parser.add_argument('-log', '--log_level', type=str, default='INFO',
|
|
choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], help='Log level (default: INFO)')
|
|
parser.add_argument('-gui', '--user_interface', action='store_true', default=True, help="Launch the Gradio user interface")
|
|
parser.add_argument('-demo', '--demo_mode', action='store_true', help='Enable demo mode')
|
|
parser.add_argument('-prompt', '--custom_prompt', type=str,
|
|
help='Pass in a custom prompt to be used in place of the existing one.\n (Probably should just '
|
|
'modify the script itself...)')
|
|
parser.add_argument('-overwrite', '--overwrite', action='store_true', help='Overwrite existing files')
|
|
parser.add_argument('-roll', '--rolling_summarization', action='store_true', help='Enable rolling summarization')
|
|
parser.add_argument('-detail', '--detail_level', type=float, help='Mandatory if rolling summarization is enabled, '
|
|
'defines the chunk size.\n Default is 0.01(lots '
|
|
'of chunks) -> 1.00 (few chunks)\n Currently '
|
|
'only OpenAI works. ',
|
|
default=0.01, )
|
|
parser.add_argument('-model', '--llm_model', type=str, default='',
|
|
help='Model to use for LLM summarization (only used for vLLM/TabbyAPI)')
|
|
parser.add_argument('-k', '--keywords', nargs='+', default=['cli_ingest_no_tag'],
|
|
help='Keywords for tagging the media, can use multiple separated by spaces (default: cli_ingest_no_tag)')
|
|
parser.add_argument('--log_file', type=str, help='Where to save logfile (non-default)')
|
|
parser.add_argument('--local_llm', action='store_true',
|
|
help="Use a local LLM from the script(Downloads llamafile from github and 'mistral-7b-instruct-v0.2.Q8' - 8GB model from Huggingface)")
|
|
parser.add_argument('--server_mode', action='store_true',
|
|
help='Run in server mode (This exposes the GUI/Server to the network)')
|
|
parser.add_argument('-share', '--share_public', action='store_true', help="This will use Gradio's built-in ngrok tunneling to share the server publicly on the internet."),
|
|
parser.add_argument('--port', type=int, default=7860, help='Port to run the server on')
|
|
parser.add_argument('--ingest_text_file', action='store_true',
|
|
help='Ingest .txt files as content instead of treating them as URL lists')
|
|
parser.add_argument('--text_title', type=str, help='Title for the text file being ingested')
|
|
parser.add_argument('--text_author', type=str, help='Author of the text file being ingested')
|
|
parser.add_argument('--diarize', action='store_true', help='Enable speaker diarization')
|
|
|
|
|
|
|
|
args = parser.parse_args()
|
|
|
|
|
|
set_chunk_txt_by_words = False
|
|
set_max_txt_chunk_words = 0
|
|
set_chunk_txt_by_sentences = False
|
|
set_max_txt_chunk_sentences = 0
|
|
set_chunk_txt_by_paragraphs = False
|
|
set_max_txt_chunk_paragraphs = 0
|
|
set_chunk_txt_by_tokens = False
|
|
set_max_txt_chunk_tokens = 0
|
|
|
|
if args.share_public or args.share:
|
|
share_public = args.share_public
|
|
else:
|
|
share_public = None
|
|
if args.server_mode:
|
|
server_mode = args.server_mode
|
|
else:
|
|
server_mode = None
|
|
if args.server_mode is True:
|
|
server_mode = True
|
|
if args.port:
|
|
server_port = args.port
|
|
else:
|
|
server_port = None
|
|
|
|
|
|
logger = logging.getLogger()
|
|
logger.setLevel(getattr(logging, args.log_level))
|
|
|
|
|
|
console_handler = logging.StreamHandler()
|
|
console_handler.setLevel(getattr(logging, args.log_level))
|
|
console_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
|
|
console_handler.setFormatter(console_formatter)
|
|
|
|
if args.log_file:
|
|
|
|
file_handler = logging.FileHandler(args.log_file)
|
|
file_handler.setLevel(getattr(logging, args.log_level))
|
|
file_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
|
|
file_handler.setFormatter(file_formatter)
|
|
logger.addHandler(file_handler)
|
|
logger.info(f"Log file created at: {args.log_file}")
|
|
|
|
|
|
custom_prompt_input = args.custom_prompt
|
|
|
|
if not args.custom_prompt:
|
|
logging.debug("No custom prompt defined, will use default")
|
|
args.custom_prompt_input = (
|
|
"\n\nabove is the transcript of a video. "
|
|
"Please read through the transcript carefully. Identify the main topics that are "
|
|
"discussed over the course of the transcript. Then, summarize the key points about each "
|
|
"main topic in a concise bullet point. The bullet points should cover the key "
|
|
"information conveyed about each topic in the video, but should be much shorter than "
|
|
"the full transcript. Please output your bullet point summary inside <bulletpoints> "
|
|
"tags."
|
|
)
|
|
print("No custom prompt defined, will use default")
|
|
|
|
custom_prompt_input = args.custom_prompt
|
|
else:
|
|
logging.debug(f"Custom prompt defined, will use \n\nf{custom_prompt_input} \n\nas the prompt")
|
|
print(f"Custom Prompt has been defined. Custom prompt: \n\n {args.custom_prompt}")
|
|
|
|
|
|
local_llm = args.local_llm
|
|
logging.info(f'Local LLM flag: {local_llm}')
|
|
|
|
|
|
if args.input_path is not None:
|
|
if os.path.isdir(args.input_path) and args.ingest_text_file:
|
|
results = ingest_folder(args.input_path, keywords=args.keywords)
|
|
for result in results:
|
|
print(result)
|
|
elif args.input_path.lower().endswith('.txt') and args.ingest_text_file:
|
|
result = ingest_text_file(args.input_path, title=args.text_title, author=args.text_author,
|
|
keywords=args.keywords)
|
|
print(result)
|
|
sys.exit(0)
|
|
|
|
|
|
if args.user_interface:
|
|
if local_llm:
|
|
local_llm_function()
|
|
time.sleep(2)
|
|
webbrowser.open_new_tab('http://127.0.0.1:7860')
|
|
launch_ui(share_public=False)
|
|
elif share_public is not None:
|
|
if local_llm:
|
|
local_llm_function()
|
|
time.sleep(2)
|
|
webbrowser.open_new_tab('http://127.0.0.1:7860')
|
|
else:
|
|
launch_ui(share_public=True)
|
|
elif not args.input_path:
|
|
parser.print_help()
|
|
sys.exit(1)
|
|
|
|
else:
|
|
logging.info('Starting the transcription and summarization process.')
|
|
logging.info(f'Input path: {args.input_path}')
|
|
logging.info(f'API Name: {args.api_name}')
|
|
logging.info(f'Number of speakers: {args.num_speakers}')
|
|
logging.info(f'Whisper model: {args.whisper_model}')
|
|
logging.info(f'Offset: {args.offset}')
|
|
logging.info(f'VAD filter: {args.vad_filter}')
|
|
logging.info(f'Log Level: {args.log_level}')
|
|
logging.info(f'Demo Mode: {args.demo_mode}')
|
|
logging.info(f'Custom Prompt: {args.custom_prompt}')
|
|
logging.info(f'Overwrite: {args.overwrite}')
|
|
logging.info(f'Rolling Summarization: {args.rolling_summarization}')
|
|
logging.info(f'User Interface: {args.user_interface}')
|
|
logging.info(f'Video Download: {args.video}')
|
|
|
|
|
|
|
|
global api_name
|
|
api_name = args.api_name
|
|
|
|
summary = None
|
|
if args.detail_level == None:
|
|
args.detail_level = 0.01
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
elif args.api_name:
|
|
logging.info(f'MAIN: API used: {args.api_name}')
|
|
logging.info('MAIN: Summarization (not rolling) will be performed.')
|
|
|
|
else:
|
|
logging.info('No API specified. Summarization will not be performed.')
|
|
|
|
logging.debug("Platform check being performed...")
|
|
platform_check()
|
|
logging.debug("CUDA check being performed...")
|
|
cuda_check()
|
|
processing_choice = "cpu"
|
|
logging.debug("ffmpeg check being performed...")
|
|
check_ffmpeg()
|
|
|
|
|
|
llm_model = args.llm_model or None
|
|
|
|
args.time_based = False
|
|
|
|
try:
|
|
results = main(args.input_path, api_name=args.api_name, api_key=args.api_key,
|
|
num_speakers=args.num_speakers, whisper_model=args.whisper_model, offset=args.offset,
|
|
vad_filter=args.vad_filter, download_video_flag=args.video, custom_prompt=args.custom_prompt_input,
|
|
overwrite=args.overwrite, rolling_summarization=args.rolling_summarization,
|
|
detail=args.detail_level, keywords=args.keywords, llm_model=args.llm_model,
|
|
time_based=args.time_based, set_chunk_txt_by_words=set_chunk_txt_by_words,
|
|
set_max_txt_chunk_words=set_max_txt_chunk_words,
|
|
set_chunk_txt_by_sentences=set_chunk_txt_by_sentences,
|
|
set_max_txt_chunk_sentences=set_max_txt_chunk_sentences,
|
|
set_chunk_txt_by_paragraphs=set_chunk_txt_by_paragraphs,
|
|
set_max_txt_chunk_paragraphs=set_max_txt_chunk_paragraphs,
|
|
set_chunk_txt_by_tokens=set_chunk_txt_by_tokens,
|
|
set_max_txt_chunk_tokens=set_max_txt_chunk_tokens)
|
|
|
|
logging.info('Transcription process completed.')
|
|
atexit.register(cleanup_process)
|
|
except Exception as e:
|
|
logging.error('An error occurred during the transcription process.')
|
|
logging.error(str(e))
|
|
sys.exit(1)
|
|
|
|
finally:
|
|
cleanup_process()
|
|
|
