#update import os OPENAI_API_KEY = os.environ['OPENAI_API_KEY'] ELEVEN_LABS_API = os.environ['ELEVEN_LABS_API'] PASSWORD_AUTH = os.environ['PASSWORD_AUTH'] from elevenlabs import clone, generate, play, save from elevenlabs import set_api_key set_api_key(ELEVEN_LABS_API) def process_video_custom_voice(uploaded_file, prompt_user, prompt_input, custom_audio, voice_prompt): if type(uploaded_file) == str: video_filename = uploaded_file else: video_filename = uploaded_file.name print("video", video_filename) base64Frames, video_filename, video_duration = video_to_frames(video_filename) final_prompt = prompt_type(prompt_user, prompt_input, video_duration) print(final_prompt) text = frames_to_story(base64Frames, final_prompt, video_duration) if type(custom_audio) == str: custom_audio_filename = custom_audio else: custom_audio_filename = custom_audio.name print("custom audio", custom_audio_filename) voice = clone( name="Custom Voice", description=f"{voice_prompt}", # Optional files=[custom_audio_filename], ) audio = generate(text=text, voice=voice) save(audio, custom_audio_filename) audio_filename = custom_audio_filename # Merge audio and video output_video_filename = os.path.splitext(video_filename)[0] + '_output.mp4' final_video_filename = merge_audio_video(video_filename, audio_filename, output_video_filename) print("final", final_video_filename) if type(uploaded_file) != str: os.unlink(video_filename) os.unlink(audio_filename) return final_video_filename, text import openai import requests import os from moviepy.editor import VideoFileClip, AudioFileClip, CompositeAudioClip from moviepy.audio.io.AudioFileClip import AudioFileClip import cv2 # We're using OpenCV to read video import base64 import time import io import tempfile import numpy as np import gradio as gr # Set your OpenAI API key here openai.api_key = OPENAI_API_KEY def video_to_frames(video_file_path): if type(video_file_path) == str: video_filename = video_file_path else: video_filename = video_file_path.name video_duration = VideoFileClip(video_filename).duration video = cv2.VideoCapture(video_filename) base64Frames = [] frame_count = 0 while video.isOpened(): success, frame = video.read() if not success: break _, buffer = cv2.imencode(".jpg", frame) base64Frames.append(base64.b64encode(buffer).decode("utf-8")) frame_count += 1 if frame_count % 30 == 0: print("30 frames added.") video.release() print(len(base64Frames), "frames read.") return base64Frames, video_filename, video_duration def text_to_speech(text, video_filename, voice_type="feminine-american", API_KEY = ELEVEN_LABS_API): CHUNK_SIZE = 2048 voice_id = '21m00Tcm4TlvDq8ikWAM' BASE_URL = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}" headers = { "Accept": "audio/mpeg", "Content-Type": "application/json", "xi-api-key": API_KEY } if voice_type == "masculine-american": MODEL_ID = "eleven_monolingual_v1" voice_id = 'VR6AewLTigWG4xSOukaG' BASE_URL = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}" chunk = text data = { "text": chunk, "model_id": MODEL_ID, "voice_settings": { "stability": 0.5, "similarity_boost": 0.5 } } elif voice_type == "feminine-british": MODEL_ID = "eleven_monolingual_v1" voice_id = 'ThT5KcBeYPX3keUQqHPh' BASE_URL = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}" chunk = text data = { "text": chunk, "model_id": MODEL_ID, "voice_settings": { "stability": 0.5, "similarity_boost": 0.5 } } elif voice_type == "masculine-british": MODEL_ID = "eleven_monolingual_v1" voice_id = 'Yko7PKHZNXotIFUBG7I9' BASE_URL = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}" chunk = text data = { "text": chunk, "model_id": MODEL_ID, "voice_settings": { "stability": 0.5, "similarity_boost": 0.5 } } else: MODEL_ID = "eleven_monolingual_v1" voice_id = 'jsCqWAovK2LkecY7zXl4' BASE_URL = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}" chunk = text data = { "text": chunk, "model_id": MODEL_ID, "voice_settings": { "stability": 0.3, "similarity_boost": 0.5 } } # Send the POST request to the API response = requests.post(BASE_URL, json=data, headers=headers) # Check if the response is OK if response.status_code == 200: # Write the chunk to an mp3 file in the directory # Save audio to a specified file audio_filename = 'testing_file.mp3' with open(audio_filename, 'wb') as file: for chunk in response.iter_content(chunk_size=1024 * 1024): file.write(chunk) print(f'Saved {audio_filename}') else: print(f'Error: Received response code {response.status_code}') return audio_filename def frames_to_story(base64Frames, prompt, video_duration): fps = int(len(base64Frames) / video_duration) frame_cut_thres = fps print("Cutting at", frame_cut_thres) list_of_dictionaries = list(map(lambda x: { "type": "image_url", "image_url": { "url": f"data:image/jpeg;base64,{x}", "detail": "low" } }, base64Frames[0::frame_cut_thres])) PROMPT_MESSAGES = [ { "role": "user", "content": [ prompt, *list_of_dictionaries, ], }, ] params = { "model": "gpt-4-vision-preview", "messages": PROMPT_MESSAGES, #"api_key": OPENAI_API_KEY, #"headers": {"Openai-Version": "2020-11-07"}, "max_tokens": 500, } result = openai.chat.completions.create(**params) print(result.choices[0].message.content) return result.choices[0].message.content def prompt_type(prompt_user, prompt_input, video_duration): prompt_documentary = ''' You are a world class documentary narration script writer. Based on the frames in the video, write a captivating voiceover for it. Write it with close observation of each frame. Observe the suddent change in movement of each frame and narrate about it. ''' prompt_how_to = ''' You are an expert narrator that specializes in writing narration scripts for "how-to" videos. Your goal is to write a script so that the audience can follow instructions from the video. Pay attention to where the mouse and tap cursor is and navigate based on the sequence of each frame. Remember to narrate something useful. Narrate something that the audience can understand to take an action. ''' prompt_sports_commentator = ''' You are a professional sports commentator that can comment for all kinds of sports including e-sports. Your goal is to write a script that is exciting and make the audience's heart beat fast. Pay attention to what the characters of the players are doing in each frame and narrate their actions. Remember to narrate something exciting and nail-biting. Keep the audience on their toes and wanting to know more. Add a lot of exclamation mark and emotions into the voiceover script. ''' if prompt_input == "how-to": prompt_input = prompt_how_to mul_factor = 1.6 elif prompt_input == "documentary": prompt_input = prompt_documentary mul_factor = 2 elif prompt_input == "sports-commentator": prompt_input = prompt_sports_commentator mul_factor = 1.5 elif prompt_input == "custom-prompt": prompt_input = prompt_user mul_factor = 2 else: prompt_input = "" mul_factor = 2 est_word_count = int(video_duration * mul_factor) word_lim_prompt = f'''This video is EXACTLY {video_duration} seconds long, make sure the voiceover narration script to be EXACTLY {est_word_count} words. Do not go over {est_word_count} for the output script. ''' initial_prompt = ''' These are a sequence of frames for a short video. You are an expert voiceover script writer. The voiceover is to help the audience and viewer. Write a voiceover for the video by carefully analyzing each frame. Make sure there is coherence between each frame. ''' final_prompt = word_lim_prompt + initial_prompt + prompt_user + prompt_input + "\n" + word_lim_prompt return(final_prompt) def merge_audio_video(video_filename, audio_filename, output_filename, original_audio_volume=0.3): print("Merging audio and video...") print("Video filename:", video_filename) print("Audio filename:", audio_filename) # Load the video file video_clip = VideoFileClip(video_filename) try:# Reduce the volume of the original audio original_audio = video_clip.audio.volumex(original_audio_volume) # Load the new audio file new_audio_clip = AudioFileClip(audio_filename) # Mix the adjusted original audio with the new audio mixed_audio = CompositeAudioClip([original_audio, new_audio_clip]) # Set the mixed audio as the audio of the video clip final_clip = video_clip.set_audio(mixed_audio) # Write the result to a file final_clip.write_videofile(output_filename, codec='libx264', audio_codec='aac') # Close the clips video_clip.close() new_audio_clip.close() except: print("No volume") # Set the audio of the video clip final_clip = video_clip.set_audio(audio_filename) # Write the result to a file final_clip.write_videofile(output_filename, codec='libx264', audio_codec='aac') # Close the clips video_clip.close() new_audio_clip.close() # Return the path to the new video file return output_filename # Rest of your imports and functions remain the same def process_video(uploaded_file, prompt_user, prompt_input, voice_type="feminine-american"): if type(uploaded_file) == str: video_filename = uploaded_file else: video_filename = uploaded_file.name print("video", video_filename) base64Frames, video_filename, video_duration = video_to_frames(video_filename) final_prompt = prompt_type(prompt_user, prompt_input, video_duration) print(final_prompt) text = frames_to_story(base64Frames, final_prompt, video_duration) audio_filename = text_to_speech(text, video_filename, voice_type) print("audio", audio_filename) # Merge audio and video output_video_filename = os.path.splitext(video_filename)[0] + '_output.mp4' final_video_filename = merge_audio_video(video_filename, audio_filename, output_video_filename) print("final", final_video_filename) if type(uploaded_file) != str: os.unlink(video_filename) os.unlink(audio_filename) return final_video_filename, text # Rest of your imports and functions remain the same def regenerate(uploaded_file, edited_script, voice_type="feminine-american"): if type(uploaded_file) == str: video_filename = uploaded_file else: video_filename = uploaded_file.name print("video", video_filename) # Generate audio from text audio_filename = text_to_speech(edited_script, video_filename, voice_type) print("audio", audio_filename) # Merge audio and video output_video_filename = os.path.splitext(video_filename)[0] + '_output.mp4' final_video_filename = merge_audio_video(video_filename, audio_filename, output_video_filename) print("final", final_video_filename) if type(uploaded_file) != str: os.unlink(video_filename) os.unlink(audio_filename) return final_video_filename, edited_script with gr.Blocks() as demo: gr.Markdown( """ # Auto Narrator Upload a video and provide a prompt to generate a narration. """) with gr.Row(): with gr.Column(): video_input = gr.Video(label="Upload Video") prompt_user = gr.Textbox(label="Enter your prompt") prompt_input = gr.Dropdown(['how-to', 'documentary', 'sports-commentator', 'custom-prompt'], label="Choose Your Narration") voice_type = gr.Dropdown(['masculine-american', 'masculine-british', 'feminine-american', 'feminine-british'], label="Choose Your Voice") generate_btn = gr.Button(value="Generate") voice_sample = gr.File(label="Use custom made voice.") voice_prompt = gr.Textbox(label="Enter voice prompt.") #render_btn = gr.Button(value="Render") #print_btn = gr.Button(value="Print") with gr.Column(): output_file = gr.Video(label="Ouput video file.") output_voiceover = gr.Textbox(label="Generated Text") regenerate_btn = gr.Button(value="Re-generate") custom_voice_btn = gr.Button(value="Use Custom Voice") #print_text = gr.Text(label="Printing") generate_btn.click(process_video, inputs=[video_input, prompt_user, prompt_input, voice_type], outputs=[output_file,output_voiceover]) regenerate_btn.click(regenerate, inputs=[video_input, output_voiceover, voice_type], outputs=[output_file,output_voiceover]) custom_voice_btn.click(process_video_custom_voice, inputs=[video_input, prompt_user, prompt_input, voice_sample, voice_prompt], outputs=[output_file,output_voiceover]) demo.launch(auth=("admin", PASSWORD_AUTH))