import gradio as gr from gradio_client import Client import os import json import re from moviepy.editor import VideoFileClip from moviepy.audio.AudioClip import AudioClip hf_token = os.environ.get("HF_TKN") 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://fffilon-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" ) 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): client = Client("https://declare-lab-tango.hf.space/") 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 infer(image_in, chosen_model): caption = get_caption(image_in) if chosen_model == "MAGNet" : magnet_result = get_magnet(caption) return magnet_result elif chosen_model == "AudioLDM-2" : audioldm_result = get_audioldm(caption) return audioldm_result elif chosen_model == "AudioGen" : audiogen_result = get_audiogen(caption) return audiogen_result elif chosen_model == "Tango" : tango_result = get_tango(caption) return tango_result 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("""

Image to SFX

Compare MAGNet, AudioLDM2 and AudioGen sound effects generation from image caption.

""") with gr.Column(): image_in = gr.Image(sources=["upload"], type="filepath", label="Image input", value="oiseau.png") with gr.Row(): chosen_model = gr.Dropdown(label="Choose a model", choices=["MAGNet", "AudioLDM-2", "AudioGen", "Tango"], value="AudioLDM-2") submit_btn = gr.Button("Submit") with gr.Column(): audio_o = gr.Audio(label="Audio output") submit_btn.click( fn=infer, inputs=[image_in, chosen_model], outputs=[audio_o], concurrency_limit = 2 ) demo.queue(max_size=10).launch(debug=True)