import gradio as gr import numpy as np import time import base64 import ffmpeg from sentence_transformers import SentenceTransformer from audio2numpy import open_audio import httpx import json import os import requests import urllib import pydub from os import path from pydub import AudioSegment MUBERT_LICENSE = os.environ.get('MUBERT_LICENSE') MUBERT_TOKEN = os.environ.get('MUBERT_TOKEN') #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 from utils import get_tags_for_prompts, get_mubert_tags_embeddings minilm = SentenceTransformer('all-MiniLM-L6-v2') mubert_tags_embeddings = get_mubert_tags_embeddings(minilm) ##———————————————————————————————————— MUBERT_LICENSE = os.environ.get('MUBERT_LICENSE') MUBERT_TOKEN = os.environ.get('MUBERT_TOKEN') ##———————————————————————————————————— def get_pat_token(): r = httpx.post('https://api-b2b.mubert.com/v2/GetServiceAccess', json={ "method": "GetServiceAccess", "params": { "email":"mail@mail.com", "phone":"+11234567890", "license": MUBERT_LICENSE, "token": MUBERT_TOKEN, } }) rdata = json.loads(r.text) assert rdata['status'] == 1, "probably incorrect e-mail" pat = rdata['data']['pat'] print(f"pat: {pat}") return pat def get_music(pat, prompt, track_duration, gen_intensity, gen_mode): r = httpx.post('https://api-b2b.mubert.com/v2/TTMRecordTrack', json={ "method": "TTMRecordTrack", "params": { "text": prompt, "pat": pat, "mode":gen_mode, "duration":track_duration, "intensity": gen_intensity } }) rdata = json.loads(r.text) print(f"rdata: {rdata}") assert rdata['status'] == 1, rdata['error']['text'] track = rdata['data']['tasks'][0]['download_link'] print(track) local_file_path = "sample.mp3" # Download the MP3 file from the URL headers = { 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7; rv:93.0) Gecko/20100101 Firefox/93.0'} retries = 3 delay = 5 # in seconds while retries > 0: response = requests.get(track, headers=headers) if response.status_code == 200: break retries -= 1 time.sleep(delay) response = requests.get(track, headers=headers) print(f"{response}") # Save the downloaded content to a local file with open(local_file_path, 'wb') as f: f.write(response.content) return "sample.mp3" def get_results(text_prompt,track_duration,gen_intensity,gen_mode): pat_token = get_pat_token() music = get_music(pat_token, text_prompt, track_duration, gen_intensity, gen_mode) return pat_token, music 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, 'best', 4, fn_index=1)[0] print(prompt) music_result = get_results(prompt, track_duration, gen_intensity, gen_mode) wave_file = convert_mp3_to_wav(music_result[1]) #music_result = generate_track_by_prompt(pat, prompt, track_duration, gen_intensity, gen_mode) #print(pat time.sleep(1) return wave_file, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) 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) print(rdata) #assert rdata['status'] == 1, rdata['error']['text'] trackurl = rdata['data']['tasks'][0] 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(pat, prompt, duration, gen_intensity, gen_mode): try: _, 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 = """

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""" with gr.Blocks(css="style.css") as demo: with gr.Column(elem_id="col-container"): gr.HTML("""

Image to Music

Sends an image in to CLIP Interrogator to generate a text prompt which is then run through Mubert text-to-music to generate music from the input image!

""") 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") #text_status = gr.Textbox(label="status") 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()