AI that turns text into music enables collaboration between Beatles and Queen
I tried MubertAI, which turns text into music.
The finished product
Please take a look.
The Beatles and the Queen
Justin Bieber and Madonna
John Lennon and Bob Dylan
Chopin and Beethoven
BTS and Blackpink
Elvis Presley and Elton John
Eminem and Taylor Swift
Nirvana and Oasis
Stray Kids and Beyoncé
Ed Sheeran and Charlie Puth
try
Environment
- Python
- Google Colab
Import and Installation
import subprocess, time
print("Setting up environment... ")
start_time = time.time()
all_process = [
['pip', 'install', 'torch==1.12.1+cu113', 'torchvision==0.13.1+cu113', '--extra-index-url', 'https:// download.pytorch.org/whl/cu113'],
['pip', 'install', '-U', 'sentence-transformers'],
['pip', 'install', 'httpx'],
]
for process in all_process:
running = subprocess.run(process,stdout=subprocess. PIPE).stdout.decode('utf-8')
end_time = time.time()
print(f"Environment set up in {end_time-start_time:.0f} seconds")API Settings
import numpy as np
from sentence_transformers import SentenceTransformer
minilm = SentenceTransformer('all-MiniLM-L6-v2')
mubert_tags_string = 'tribal,action,kids,neo-classic,run 130,pumped,jazz / funk,ethnic,dubtechno,reggae,acid jazz,liquidfunk,funk,witch house,tech house,underground, artists,mystical,disco,sensorium,r&b,agender,psychedelic trance / psytrance,peaceful,run 140,piano,run 160,setting,meditation,christmas,ambient,horror,cinematic, electro house,idm,bass,minimal,underscore,drums,glitchy,beautiful,technology,tribal house,country pop,jazz & funk,documentary,space,classical,valentines,chillstep, experimental,trap,new jack swing,drama,post-rock,tense,corporate,neutral,happy,analog,funky,spiritual,sberzvuk special,chill hop,dramatic,catchy,holidays,fitness 90, optimistic,orchestra,acid techno,energizing,romantic,minimal house,breaks,hyper pop,warm up,dreamy,dark,urban,microfunk,dub,nu disco,vogue,keys,hardcore,aggressive,indie, electro funk,beauty,relaxing,trance,pop,hiphop,soft,acoustic,chillrave / ethno-house,deep techno,angry,dance,fun,dubstep,tropical,latin pop,heroic,world music,inspirational ,uplifting,atmosphere,art,epic,advertising,chillout,scary,spooky,slow ballad,saxophone,summer,erotic,jazzy,energy 100,kara mar,xmas,atmospheric,indie pop,hip-hop,yoga, reggaeton,lounge,travel,running,folk,chillrave & ethno-house,detective,darkambient,chill,fantasy,minimal techno,special,night,tropical house,downtempo,lullaby, meditative,upbeat,glitch hop,fitness,neurofunk,sexual,indie rock,future pop,jazz,cyberpunk,melancholic,happy hardcore,family / kids,synths,electric guitar,comedy,psychedelic trance & psytrance,edm,psychedelic rock,calm,zen,bells,podcast,melodic house,ethnic percussion,nature,heavy,bassline,indie dance,techno,drumnbass,synth pop,vaporwave,sad ,8-bit,chillgressive,deep,orchestral,futuristic,hardtechno,nostalgic,big room,sci-fi,tutorial,joyful,pads,minimal 170,drill,ethnic 108,amusing,sleepy ambient,psychill, italo disco,lofi,house,acoustic guitar,bassline house,rock,k-pop,synthwave,deep house,electronica,gabber,nightlife,sport & fitness,road trip,celebration,electro,disco house,electronic'
mubert_tags = np.array(mubert_tags_string.split(','))
mubert_tags_embeddings = minilm.encode(mubert_tags)
from IPython.display import Audio, display
import httpx
import json
def get_track_by_tags(tags, pat, duration, maxit=20, autoplay=False, loop=False):
if loop:
mode = "loop"
else:
mode = "track"
r = httpx.post('https://api-b2b.mubert.com/v2/RecordTrackTTM',
json={
"method":"RecordTrackTTM",
"params": {
"pat": pat,
"duration": duration,
"tags": tags,
"mode": 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:
display(Audio(trackurl, autoplay=autoplay))
break
time.sleep(1)
print('.', end='')
def find_similar(em, embeddings, method='cosine'):
scores = []
for ref in embeddings:
if method == 'cosine':
scores.append(1 - np.dot(ref, em)/(np.linalg.norm(ref)*np.linalg.norm(em)))
if method == 'norm':
scores.append(np.linalg.norm(ref - em))
return np.array(scores), np.argsort(scores)
def get_tags_for_prompts(prompts, top_n=3, debug=False):
prompts_embeddings = minilm.encode(prompts)
ret = []
for i, pe in enumerate(prompts_embeddings):
scores, idxs = find_similar(pe, mubert_tags_embeddings)
top_tags = mubert_tags[idxs[:top_n]]
top_prob = 1 - scores[idxs[:top_n]]
if debug:
print(f"Prompt: {prompts[i]}nTags: {', '.join(top_tags)}nScores: {top_prob}nnn")
ret.append((prompts[i], list(top_tags)))
return retTap API (*Please change the email)
email = "deeprecommend@gmail.com" #@param {type:"string"}
r = httpx.post('https://api-b2b.mubert.com/v2/GetServiceAccess',
json={
"method":"GetServiceAccess",
"params": {
"email": email,
"license":"ttmmubertlicense#f0acYBenRcfeFpNT4wpYGaTQIyDI4mJGv5MfIhBFz97NXDwDNFHmMRsBSzmGsJwbTpP1A6i07AXcIeAHo5",
"token":"4951f6428e83172a4f39de05d5b3ab10d58560b8",
"mode": "loop"
}
})
rdata = json.loads(r.text)
assert rdata['status'] == 1, "probably incorrect e-mail"
pat = rdata['data']['pat']
print(f'Got token: {pat}')generation
prompt = 'Beatles with Queen' #@param {type:"string"}
duration = 30 #@param {type:"number"}
loop = False #@param {type:"boolean"}
def generate_track_by_prompt(prompt, duration, loop=False):
_, tags = get_tags_for_prompts([prompt,])[0]
try:
get_track_by_tags(tags, pat, duration, autoplay=True, loop=loop)
except Exception as e:
print(str(e))
print('n')
generate_track_by_prompt(prompt, duration, loop)Conclusion
I would like to set up a big data Bayes quantum neural network algorithm. — Jin Sugimoto (@Jin__Sugimoto) October 21, 2022
AI is like Chinese products, and it produces only bad things now, but
it will probably produce cheaper,
faster,
and reasonably good things someday. — Jin Sugimoto (@Jin__Sugimoto) October 21, 2022
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