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| ##################################################################### | |
| #This application provides and esample of FASTAPI for | |
| # transcribing an mp4 video and summarizing the transcription | |
| ###################################################################### | |
| import os | |
| import subprocess | |
| from moviepy.video.io.VideoFileClip import VideoFileClip | |
| from moviepy.audio.io.AudioFileClip import AudioFileClip | |
| import whisper | |
| from openai import OpenAI | |
| from fastapi import FastAPI | |
| from transformers import pipeline | |
| #import os | |
| #os.system("apt-get update && apt-get install -y ffmpeg") | |
| #result = subprocess.run(["ffmpeg", "-version"], capture_output=True, text=True) | |
| #print(result.stdout) | |
| #input_file = "input.mp4" | |
| #output_file = "output.mp3" | |
| #subprocess.run(["ffmpeg", "-i", input_file, output_file]) | |
| ## create a new FASTAPI app instance | |
| app=FastAPI() | |
| # Initialize the text generation pipeline | |
| #pipe = pipeline("text2text-generation", model="google/flan-t5-small") | |
| # This is default function | |
| def home(): | |
| return {"message":"Hello World"} | |
| # Define a function to handle the GET request at `/generate` | |
| # | |
| def generate(text:str): | |
| ## use the pipeline to generate text from given input text | |
| output=pipe(text) | |
| ## return the generate text in Json reposne | |
| return {"output":output[0]['generated_text']} | |
| # This function | |
| # 1. takes mp4 video as input | |
| # 2. creates mp3 file from mp4 file | |
| # 3. creates wav file from mp3 file | |
| # 4. transcribes to text from the wav file | |
| def get_transcript_mp4(mp4_path): | |
| #path_to_check = r"C:\Program Files\ffmpeg\bin" | |
| #if path_to_check in os.environ["PATH"]: | |
| #print(f"{path_to_check} is in the PATH") | |
| #else: | |
| #print(f"{path_to_check} is NOT in the PATH") | |
| #os.environ["PATH"] += os.pathsep + path_to_check | |
| ffmpeg_version = subprocess.run(["ffmpeg", "-version"], capture_output=True, text=True) | |
| print(ffmpeg_version.stdout) | |
| # Assuming the mp4 file has a transcript available | |
| # This is a placeholder function. You would need to implement actual transcript extraction from mp4. | |
| # For now, we will return a dummy transcript. | |
| if os.path.exists(mp4_path): | |
| print("File exists!: ", mp4_path) | |
| # construct mp3 path | |
| # Replace the .mp4 extension with .mp3 | |
| # Get the file name without extension | |
| base_name = os.path.splitext(mp4_path)[0] | |
| mp3_path = f"{base_name}.mp3" # Append .mp3 extension | |
| #construct wav file path | |
| wav_path = f"{base_name}.wav" # Append .mp3 extension | |
| # construct the file to store the transcript | |
| transcript_path = f"{base_name}_transcript.txt" | |
| video = VideoFileClip(mp4_path) | |
| video.audio.write_audiofile(mp3_path) | |
| print("Conversion to mp3 complete!") | |
| audio = AudioFileClip(mp3_path) | |
| audio.write_audiofile(wav_path) | |
| print("Conversion to wav complete!") | |
| # Initialize the Whisper model (small model is a good balance for speed and accuracy) | |
| print("Transcribing audio...") | |
| model = whisper.load_model("tiny.en") # Options: "tiny", "base", "small", "medium", "large" | |
| # Transcribe the audio | |
| result = model.transcribe(wav_path) | |
| print("Transcription completed!") | |
| meet_data=result["text"] | |
| with open(transcript_path, "w") as file: | |
| file.write(meet_data) | |
| print("Txt written to file: ", transcript_path) | |
| return meet_data | |
| else: | |
| print("File NOT found! Check the path for: ", mp4_path) | |
| return "Test summary" | |
| # This function | |
| # 1. takes text as input | |
| # 2. summarizes the input text | |
| def summarize_text(text, lang='en'): | |
| OpenAI.api_key = os.getenv("OPENAI_API_KEY") | |
| client = OpenAI(api_key=OpenAI.api_key) | |
| prompt = f""" | |
| The following text is in its original language. Provide the output in this language: {lang}. | |
| Format the output as follows: | |
| Summary: | |
| short summary of the video | |
| Key Takeaways: | |
| succinct bullet point list of key takeaways | |
| Detailed important points: | |
| succinct bullet point list of feature details | |
| input text: {text} | |
| """ | |
| response = client.chat.completions.create( | |
| messages=[{"role": "user", "content": prompt}], | |
| model="gpt-4o", | |
| temperature=0, | |
| #model="gpt-4-turbo", # better performance, slower inference | |
| ) | |
| summary_text = response.to_dict()['choices'][0]['message']['content'] | |
| return summary_text | |