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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)
##ββββββββββββββββββββββββββββββββββββ
MUBERT_LICENSE = os.environ.get('MUBERT_LICENSE')
MUBERT_TOKEN = os.environ.get('MUBERT_TOKEN')
##ββββββββββββββββββββββββββββββββββββ
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 is not None:
gpt_adaptation = try_api(prompt, openai_api_key)
if gpt_adaptation[0] != "oups":
musical_prompt = gpt_adaptation[0]
print(f"misical adapt: {musical_prompt}")
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}'
β
OpenAI Musical Adaptation: '{musical_prompt}'
β
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, "<span class='openai_clear'>no error</span>"
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"<span class='openai_error'>OpenAI API request timed out: <br />{e}</span>"
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"<span class='openai_error'>OpenAI API returned an API Error: <br />{e}</span>"
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"<span class='openai_error'>OpenAI API request failed to connect: <br />{e}</span>"
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"<span class='openai_error'>OpenAI API request was invalid: <br />{e}</span>"
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"<span class='openai_error'>OpenAI API request was not authorized: <br />{e}</span>"
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"<span class='openai_error'>OpenAI API request was not permitted: <br />{e}</span>"
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"<span class='openai_error'>OpenAI API request exceeded rate limit: <br />{e}</span>"
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 = """
<div class="footer">
<p>
Follow <a href="https://twitter.com/fffiloni" target="_blank">Sylvain Filoni</a> for future updates π€
</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/π€ Spaces-Riffusion-blue" src="https://img.shields.io/badge/π€ 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")
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="π 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=55, ustep=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() |