import gradio as gr import openai 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): if len(prompt) > 200: prompt = prompt[:200] 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 = 5 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", track 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[0], music[1] def get_prompts(uploaded_image, track_duration, gen_intensity, gen_mode, openai_api_key): 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) musical_prompt = 'You did not use any OpenAI API key to pimp your result :)' if openai_api_key != None: gpt_adaptation = try_api(prompt, openai_api_key) if gpt_adaptation[0] != "oups": musical_prompt = gpt_adaptation[0] music_result = get_results(musical_prompt, track_duration, gen_intensity, gen_mode) else: music_result = get_results(prompt, track_duration, gen_intensity, gen_mode) else: music_result = get_results(prompt, track_duration, gen_intensity, gen_mode) show_prompts = f""" CLIP Interrogator Caption: '{prompt}' — OpenAI Musical Adaptation: '{musical_prompt}' — Audio file link: {music_result[2]} """ #wave_file = convert_mp3_to_wav(music_result[1]) time.sleep(1) return gr.Textbox.update(value=show_prompts, visible=True), music_result[1], gr.update(visible=True), gr.update(visible=True), gr.update(visible=True) def try_api(message, openai_api_key): try: response = call_api(message, openai_api_key) return response, "no error" except openai.error.Timeout as e: #Handle timeout error, e.g. retry or log #print(f"OpenAI API request timed out: {e}") return "oups", f"OpenAI API request timed out:
{e}" except openai.error.APIError as e: #Handle API error, e.g. retry or log #print(f"OpenAI API returned an API Error: {e}") return "oups", f"OpenAI API returned an API Error:
{e}" except openai.error.APIConnectionError as e: #Handle connection error, e.g. check network or log #print(f"OpenAI API request failed to connect: {e}") return "oups", f"OpenAI API request failed to connect:
{e}" except openai.error.InvalidRequestError as e: #Handle invalid request error, e.g. validate parameters or log #print(f"OpenAI API request was invalid: {e}") return "oups", f"OpenAI API request was invalid:
{e}" except openai.error.AuthenticationError as e: #Handle authentication error, e.g. check credentials or log #print(f"OpenAI API request was not authorized: {e}") return "oups", f"OpenAI API request was not authorized:
{e}" except openai.error.PermissionError as e: #Handle permission error, e.g. check scope or log #print(f"OpenAI API request was not permitted: {e}") return "oups", f"OpenAI API request was not permitted:
{e}" except openai.error.RateLimitError as e: #Handle rate limit error, e.g. wait or log #print(f"OpenAI API request exceeded rate limit: {e}") return "oups", f"OpenAI API request exceeded rate limit:
{e}" def call_api(message, openai_api_key): instruction = "Convert in less than 200 characters this image caption to a very concise musical description with musical terms, as if you wanted to describe a musical ambiance, stricly in English" print("starting open ai") augmented_prompt = f"{instruction}: '{message}'." openai.api_key = openai_api_key response = openai.Completion.create( model="text-davinci-003", prompt=augmented_prompt, temperature=0.5, max_tokens=2048, top_p=1, frequency_penalty=0, presence_penalty=0.6 ) #print(response) #return str(response.choices[0].text).split("\n",2)[2] return str(response.choices[0].text).lstrip('\n') 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): wave_file="file.wav" sound = AudioSegment.from_mp3(mp3_filepath) 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") prompts_out = gr.Textbox(label="Text Captions", visible=False, info="If player do not work, try to copy/paste the link in a new browser window") music_output = gr.Audio(label="Result", type="filepath", elem_id="music-output").style(height="5rem") #music_url = gr.Textbox(max_lines=1, info="If player do not work, try to copy/paste the link in a new browser window") #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): openai_api_key = gr.Textbox(type="password", label="🔐 Your OpenAI API Key (optional)", placeholder="sk-123abc...", info="You can use your OpenAI key to adapt CLIP Interrogator caption to a musical translation.") 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="loop") generate = gr.Button("Generate Music from Image") gr.HTML(article) generate.click(get_prompts, inputs=[input_img,track_duration,gen_intensity,gen_mode, openai_api_key], outputs=[prompts_out, music_output, share_button, community_icon, loading_icon], api_name="i2m") share_button.click(None, [], [], _js=share_js) demo.queue(max_size=32).launch()