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| import time | |
| import base64 | |
| import gradio as gr | |
| from sentence_transformers import SentenceTransformer | |
| import httpx | |
| import json | |
| import os | |
| import requests | |
| import urllib | |
| from os import path | |
| from pydub import AudioSegment | |
| #img_to_text = gr.Blocks.load(name="spaces/pharma/CLIP-Interrogator") | |
| img_to_text = gr.Blocks.load(name="spaces/fffiloni/CLIP-Interrogator-2") | |
| from share_btn import community_icon_html, loading_icon_html, share_js | |
| def get_prompts(uploaded_image, track_duration, gen_intensity, gen_mode): | |
| print("calling clip interrogator") | |
| #prompt = img_to_text(uploaded_image, "ViT-L (best for Stable Diffusion 1.*)", "fast", fn_index=1)[0] | |
| prompt = img_to_text(uploaded_image, 'fast', 4, fn_index=1)[0] | |
| print(prompt) | |
| music_result = generate_track_by_prompt(prompt, track_duration, gen_intensity, gen_mode) | |
| print(music_result) | |
| return music_result[0], gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) | |
| from utils import get_tags_for_prompts, get_mubert_tags_embeddings, get_pat | |
| minilm = SentenceTransformer('all-MiniLM-L6-v2') | |
| mubert_tags_embeddings = get_mubert_tags_embeddings(minilm) | |
| def get_track_by_tags(tags, pat, duration, gen_intensity, gen_mode, maxit=20): | |
| r = httpx.post('https://api-b2b.mubert.com/v2/RecordTrackTTM', | |
| json={ | |
| "method": "RecordTrackTTM", | |
| "params": { | |
| "pat": pat, | |
| "duration": duration, | |
| "format": "wav", | |
| "intensity":gen_intensity, | |
| "tags": tags, | |
| "mode": gen_mode | |
| } | |
| }) | |
| rdata = json.loads(r.text) | |
| assert rdata['status'] == 1, rdata['error']['text'] | |
| trackurl = rdata['data']['tasks'][0]['download_link'] | |
| print('Generating track ', end='') | |
| for i in range(maxit): | |
| r = httpx.get(trackurl) | |
| if r.status_code == 200: | |
| return trackurl | |
| time.sleep(1) | |
| def generate_track_by_prompt(prompt, duration, gen_intensity, gen_mode): | |
| try: | |
| pat = get_pat("prodia@prodia.com") | |
| _, tags = get_tags_for_prompts(minilm, mubert_tags_embeddings, [prompt, ])[0] | |
| result = get_track_by_tags(tags, pat, int(duration), gen_intensity, gen_mode) | |
| print(result) | |
| return result, ",".join(tags), "Success" | |
| except Exception as e: | |
| return None, "", str(e) | |
| def convert_mp3_to_wav(mp3_filepath): | |
| url = mp3_filepath | |
| save_as = "file.mp3" | |
| data = urllib.request.urlopen(url) | |
| f = open(save_as,'wb') | |
| f.write(data.read()) | |
| f.close() | |
| wave_file="file.wav" | |
| sound = AudioSegment.from_mp3(save_as) | |
| sound.export(wave_file, format="wav") | |
| return wave_file | |
| article = """ | |
| <div class="footer"> | |
| <p> | |
| Follow <a href="https://twitter.com/fffiloni" target="_blank">Sylvain Filoni</a> for future updates 🤗 | |
| </p> | |
| </div> | |
| <div id="may-like-container" style="display: flex;justify-content: center;flex-direction: column;align-items: center;margin-bottom: 30px;"> | |
| <p style="font-size: 0.8em;margin-bottom: 4px;">You may also like: </p> | |
| <div id="may-like" style="display: flex;flex-wrap: wrap;align-items: center;height: 20px;"> | |
| <svg height="20" width="122" style="margin-left:4px;margin-bottom: 6px;"> | |
| <a href="https://huggingface.co/spaces/fffiloni/spectrogram-to-music" target="_blank"> | |
| <image href="https://img.shields.io/badge/🤗 Spaces-Riffusion-blue" src="https://img.shields.io/badge/🤗 Spaces-Riffusion-blue.png" height="20"/> | |
| </a> | |
| </svg> | |
| </div> | |
| </div> | |
| """ | |
| with gr.Blocks(css="style.css") as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;"> | |
| <div | |
| style=" | |
| display: inline-flex; | |
| align-items: center; | |
| gap: 0.8rem; | |
| font-size: 1.75rem; | |
| " | |
| > | |
| <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;"> | |
| Image to Music | |
| </h1> | |
| </div> | |
| <p style="margin-bottom: 10px; font-size: 94%"> | |
| Sends an image in to <a href="https://huggingface.co/spaces/pharma/CLIP-Interrogator" target="_blank">CLIP Interrogator</a> | |
| to generate a text prompt which is then run through | |
| <a href="https://huggingface.co/Mubert" target="_blank">Mubert</a> text-to-music to generate music from the input image! | |
| </p> | |
| </div>""") | |
| input_img = gr.Image(type="filepath", elem_id="input-img") | |
| music_output = gr.Audio(label="Result", type="filepath", elem_id="music-output").style(height="5rem") | |
| with gr.Group(elem_id="share-btn-container"): | |
| community_icon = gr.HTML(community_icon_html, visible=False) | |
| loading_icon = gr.HTML(loading_icon_html, visible=False) | |
| share_button = gr.Button("Share to community", elem_id="share-btn", visible=False) | |
| with gr.Accordion(label="Music Generation Options", open=False): | |
| track_duration = gr.Slider(minimum=20, maximum=120, value=30, step=5, label="Track duration", elem_id="duration-inp") | |
| with gr.Row(): | |
| gen_intensity = gr.Dropdown(choices=["low", "medium", "high"], value="medium", label="Intensity") | |
| gen_mode = gr.Radio(label="mode", choices=["track", "loop"], value="track") | |
| generate = gr.Button("Generate Music from Image") | |
| gr.HTML(article) | |
| generate.click(get_prompts, inputs=[input_img,track_duration,gen_intensity,gen_mode], outputs=[music_output, share_button, community_icon, loading_icon], api_name="i2m") | |
| share_button.click(None, [], [], _js=share_js) | |
| demo.queue(max_size=32, concurrency_count=20).launch() |