import gradio as gr from gradio_client import Client import os import json import re from moviepy.editor import * import cv2 hf_token = os.environ.get("HF_TKN") def extract_firstframe(video_in): vidcap = cv2.VideoCapture(video_in) success,image = vidcap.read() count = 0 while success: if count == 0: cv2.imwrite("first_frame.jpg", image) # save first extracted frame as jpg file named first_frame.jpg else: break # exit loop after saving first frame success,image = vidcap.read() print ('Read a new frame: ', success) count += 1 print ("Done extracted first frame!") return "first_frame.jpg" def extract_audio(video_in): input_video = video_in output_audio = 'audio.wav' # Open the video file and extract the audio video_clip = VideoFileClip(input_video) audio_clip = video_clip.audio # Save the audio as a .wav file audio_clip.write_audiofile(output_audio, fps=44100) # Use 44100 Hz as the sample rate for .wav files print("Audio extraction complete.") return 'audio.wav' def get_caption_from_kosmos(image_in): kosmos2_client = Client("https://ydshieh-kosmos-2.hf.space/") kosmos2_result = kosmos2_client.predict( image_in, # str (filepath or URL to image) in 'Test Image' Image component "Detailed", # str in 'Description Type' Radio component fn_index=4 ) print(f"KOSMOS2 RETURNS: {kosmos2_result}") with open(kosmos2_result[1], 'r') as f: data = json.load(f) reconstructed_sentence = [] for sublist in data: reconstructed_sentence.append(sublist[0]) full_sentence = ' '.join(reconstructed_sentence) #print(full_sentence) # Find the pattern matching the expected format ("Describe this image in detail:" followed by optional space and then the rest)... pattern = r'^Describe this image in detail:\s*(.*)$' # Apply the regex pattern to extract the description text. match = re.search(pattern, full_sentence) if match: description = match.group(1) print(description) else: print("Unable to locate valid description.") # Find the last occurrence of "." last_period_index = description.rfind('.') # Truncate the string up to the last period truncated_caption = description[:last_period_index + 1] # print(truncated_caption) print(f"\n—\nIMAGE CAPTION: {truncated_caption}") return truncated_caption def get_caption(image_in): client = Client("https://fffiloni-moondream1.hf.space/", hf_token=hf_token) result = client.predict( image_in, # filepath in 'image' Image component "Describe precisely the image in one sentence.", # str in 'Question' Textbox component #api_name="/answer_question" api_name="/predict" ) print(result) return result def get_magnet(prompt): amended_prompt = f"{prompt}" print(amended_prompt) client = Client("https://fffiloni-magnet.hf.space/") result = client.predict( "facebook/audio-magnet-medium", # Literal['facebook/magnet-small-10secs', 'facebook/magnet-medium-10secs', 'facebook/magnet-small-30secs', 'facebook/magnet-medium-30secs', 'facebook/audio-magnet-small', 'facebook/audio-magnet-medium'] in 'Model' Radio component "", # str in 'Model Path (custom models)' Textbox component amended_prompt, # str in 'Input Text' Textbox component 3, # float in 'Temperature' Number component 0.9, # float in 'Top-p' Number component 10, # float in 'Max CFG coefficient' Number component 1, # float in 'Min CFG coefficient' Number component 20, # float in 'Decoding Steps (stage 1)' Number component 10, # float in 'Decoding Steps (stage 2)' Number component 10, # float in 'Decoding Steps (stage 3)' Number component 10, # float in 'Decoding Steps (stage 4)' Number component "prod-stride1 (new!)", # Literal['max-nonoverlap', 'prod-stride1 (new!)'] in 'Span Scoring' Radio component api_name="/predict_full" ) print(result) return result[1] def get_audioldm(prompt): client = Client("https://haoheliu-audioldm2-text2audio-text2music.hf.space/") result = client.predict( prompt, # str in 'Input text' Textbox component "Low quality. Music.", # str in 'Negative prompt' Textbox component 10, # int | float (numeric value between 5 and 15) in 'Duration (seconds)' Slider component 3.5, # int | float (numeric value between 0 and 7) in 'Guidance scale' Slider component 45, # int | float in 'Seed' Number component 3, # int | float (numeric value between 1 and 5) in 'Number waveforms to generate' Slider component fn_index=1 ) print(result) audio_result = extract_audio(result) return audio_result def get_audiogen(prompt): client = Client("https://fffiloni-audiogen.hf.space/") result = client.predict( prompt, 10, api_name="/infer" ) return result def get_tango(prompt): try: client = Client("https://declare-lab-tango.hf.space/") except: raise gr.Error("Tango space API is not ready, please try again in few minutes ") result = client.predict( prompt, # str representing string value in 'Prompt' Textbox component 100, # int | float representing numeric value between 100 and 200 in 'Steps' Slider component 4, # int | float representing numeric value between 1 and 10 in 'Guidance Scale' Slider component api_name="/predict" ) print(result) return result def blend_vsfx(video_in, audio_result): audioClip = AudioFileClip(audio_result) print(f"AUD: {audioClip.duration}") clip = VideoFileClip(video_in) print(f"VID: {clip.duration}") if clip.duration < audioClip.duration : audioClip = audioClip.subclip((0.0), (clip.duration)) elif clip.duration > audioClip.duration : clip = clip.subclip((0.0), (audioClip.duration)) final_clip = clip.set_audio(audioClip) # Set the output codec codec = 'libx264' audio_codec = 'aac' final_clip.write_videofile('final_video_with_sound.mp4', codec=codec, audio_codec=audio_codec) return "final_video_with_sound.mp4" def infer(video_in, chosen_model): image_in = extract_firstframe(video_in) caption = get_caption(image_in) if chosen_model == "MAGNet" : audio_result = get_magnet(caption) elif chosen_model == "AudioLDM-2" : audio_result = get_audioldm(caption) elif chosen_model == "AudioGen" : audio_result = get_audiogen(caption) elif chosen_model == "Tango" : audio_result = get_tango(caption) final_res = blend_vsfx(video_in, audio_result) return audio_result, final_res css=""" #col-container{ margin: 0 auto; max-width: 800px; } """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.HTML("""
Get sound effects from a video shot while comparing audio models from image caption.
""") with gr.Row(): with gr.Column(): video_in = gr.Video(sources=["upload"], label="Video input") with gr.Row(): chosen_model = gr.Dropdown(label="Choose a model", choices=["MAGNet", "AudioLDM-2", "AudioGen", "Tango"], value="Tango") submit_btn = gr.Button("Submit") with gr.Column(): audio_o = gr.Audio(label="Audio output") video_o = gr.Video(label="Video with soundFX") gr.Examples( examples = [ ["examples/photoreal-train.mp4", "Tango"], ["examples/train-window.mp4", "Tango"], ["examples/chinese-new-year-dragon.mp4", "Tango"], ["examples/big-sur.mp4", "AudioLDM-2"] ], fn = infer, inputs = [video_in, chosen_model], outputs = [audio_o, video_o], cache_examples = False ) submit_btn.click( fn=infer, inputs=[video_in, chosen_model], outputs=[audio_o, video_o], concurrency_limit = 2 ) demo.queue(max_size=10).launch(debug=True, show_error=True)