img2musicで、画像から音楽を生成する

cedro

2年前

1.はじめに

　今回ご紹介するのは、画像からテキストを生成するモデルとテキストから音楽を生成するモデルを組み合わせた、画像から音楽を生成する img2music です。

　実はこれ、Hugging Face （Webサービス）で動かせるんですが、人気のためか処理速度が遅い。なので今回は Google Colab で動かしてみます。

2.img2musicとは？

　img2music は画像からテキストを生成するモデルとテキストから音楽を生成するモデル、これら２つのモデルから構成されていますので順番に見て行きましょう。

　画像からテキストを生成するモデルは、画像からStable Diffusionのテキストを探索する CLIP Interrogator を使っています。これは、従来からの画像キャプションの生成結果に、CLIPを用いた様々な属性に関する結果を加えて、より適切なテキストを生成します。

　テキストから音楽を生成するモデルは、AIで音楽生成するサービスを行なっているMubert社のAPIを使っています。具体的には、テキストからタグを抽出してサーバーへ送り、音楽を受け取る形で音楽を生成します。

3.コード

　コードはGoogle Colabで動かす形にしてGithubに上げてありますので、それに沿って説明して行きます。自分で動かしてみたい方は、この「リンク」をクリックし表示されたノートブックの先頭にある「Open in Colab」ボタンをクリックすると動かせます。

　まず、img2textをセットアップします。

#@title **setup img2text (CLIP Interrogator)**

# install library
!pip3 install ftfy regex tqdm transformers==4.15.0 timm==0.4.12 fairscale==0.4.4
!pip3 install git+https://github.com/openai/CLIP.git
!git clone -b v1 https://github.com/pharmapsychotic/clip-interrogator.git
!git clone https://github.com/salesforce/BLIP
%cd /content/BLIP


# import library
import clip
import gc
import numpy as np
import os
import pandas as pd
import requests
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF

from IPython.display import display
from PIL import Image
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from models.blip import blip_decoder

device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

blip_image_eval_size = 384
blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth'        
blip_model = blip_decoder(pretrained=blip_model_url, image_size=blip_image_eval_size, vit='base')
blip_model.eval()
blip_model = blip_model.to(device)


# difine function
def generate_caption(pil_image):
    gpu_image = transforms.Compose([
        transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),
        transforms.ToTensor(),
        transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
    ])(image).unsqueeze(0).to(device)

    with torch.no_grad():
        caption = blip_model.generate(gpu_image, sample=False, num_beams=3, max_length=20, min_length=5)
    return caption[0]

def load_list(filename):
    with open(filename, 'r', encoding='utf-8', errors='replace') as f:
        items = [line.strip() for line in f.readlines()]
    return items

def rank(model, image_features, text_array, top_count=1):
    top_count = min(top_count, len(text_array))
    text_tokens = clip.tokenize([text for text in text_array]).cuda()
    with torch.no_grad():
        text_features = model.encode_text(text_tokens).float()
    text_features /= text_features.norm(dim=-1, keepdim=True)

    similarity = torch.zeros((1, len(text_array))).to(device)
    for i in range(image_features.shape[0]):
        similarity += (100.0 * image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1)
    similarity /= image_features.shape[0]

    top_probs, top_labels = similarity.cpu().topk(top_count, dim=-1)  
    return [(text_array[top_labels[0][i].numpy()], (top_probs[0][i].numpy()*100)) for i in range(top_count)]

def interrogate(image, models):
    caption = generate_caption(image)
    if len(models) == 0:
        print(f"\n\n{caption}")
        return

    table = []
    bests = [[('',0)]]*5
    for model_name in models:
        print(f"Interrogating with {model_name}...")
        model, preprocess = clip.load(model_name)
        model.cuda().eval()

        images = preprocess(image).unsqueeze(0).cuda()
        with torch.no_grad():
            image_features = model.encode_image(images).float()
        image_features /= image_features.norm(dim=-1, keepdim=True)

        ranks = [
            rank(model, image_features, mediums),
            rank(model, image_features, ["by "+artist for artist in artists]),
            rank(model, image_features, trending_list),
            rank(model, image_features, movements),
            rank(model, image_features, flavors, top_count=3)
        ]

        for i in range(len(ranks)):
            confidence_sum = 0
            for ci in range(len(ranks[i])):
                confidence_sum += ranks[i][ci][1]
            if confidence_sum > sum(bests[i][t][1] for t in range(len(bests[i]))):
                bests[i] = ranks[i]

        row = [model_name]
        for r in ranks:
            row.append(', '.join([f"{x[0]} ({x[1]:0.1f}%)" for x in r]))

        table.append(row)

        del model
        gc.collect()
    display(pd.DataFrame(table, columns=["Model", "Medium", "Artist", "Trending", "Movement", "Flavors"]))

    flaves = ', '.join([f"{x[0]}" for x in bests[4]])
    medium = bests[0][0][0]
    if caption.startswith(medium):
        text = f"{caption} {bests[1][0][0]}, {bests[2][0][0]}, {bests[3][0][0]}, {flaves}" 
    else:
        text = f"{caption}, {medium} {bests[1][0][0]}, {bests[2][0][0]}, {bests[3][0][0]}, {flaves}" 
    return text


# setting
data_path = "../clip-interrogator/data/"

artists = load_list(os.path.join(data_path, 'artists.txt'))
flavors = load_list(os.path.join(data_path, 'flavors.txt'))
mediums = load_list(os.path.join(data_path, 'mediums.txt'))
movements = load_list(os.path.join(data_path, 'movements.txt'))

sites = ['Artstation', 'behance', 'cg society', 'cgsociety', 'deviantart', 'dribble', 'flickr', 'instagram', 'pexels', 'pinterest', 'pixabay', 'pixiv', 'polycount', 'reddit', 'shutterstock', 'tumblr', 'unsplash', 'zbrush central']
trending_list = [site for site in sites]
trending_list.extend(["trending on "+site for site in sites])
trending_list.extend(["featured on "+site for site in sites])
trending_list.extend([site+" contest winner" for site in sites])


# download sample pics
import gdown
gdown.download('https://drive.google.com/uc?id=1Mjwnr_m3pgxTPB7kusePjmKDHktGeV2w', 'pics.zip', quiet=False)
! unzip pics.zip

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#@title **setup img2text (CLIP Interrogator)**

# install library

!pip3 install ftfy regex tqdm transformers==4.15.0 timm==0.4.12 fairscale==0.4.4

!pip3 install git+https://github.com/openai/CLIP.git

!git clone -b v1 https://github.com/pharmapsychotic/clip-interrogator.git

!git clone https://github.com/salesforce/BLIP

%cd /content/BLIP

# import library

import clip

import gc

import numpy as np

import os

import pandas as pd

import requests

import torch

import torchvision.transforms as T

import torchvision.transforms.functional as TF

from IPython.display import display

from PIL import Image

from torch import nn

from torch.nn import functional as F

from torchvision import transforms

from torchvision.transforms.functional import InterpolationMode

from models.blip import blip_decoder

device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

blip_image_eval_size = 384

blip_model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth'

blip_model = blip_decoder(pretrained=blip_model_url, image_size=blip_image_eval_size, vit='base')

blip_model.eval()

blip_model = blip_model.to(device)

# difine function

def generate_caption(pil_image):

gpu_image = transforms.Compose([

transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC),

transforms.ToTensor(),

transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))

])(image).unsqueeze(0).to(device)

with torch.no_grad():

caption = blip_model.generate(gpu_image, sample=False, num_beams=3, max_length=20, min_length=5)

return caption[0]

def load_list(filename):

with open(filename, 'r', encoding='utf-8', errors='replace') as f:

items = [line.strip() for line in f.readlines()]

return items

def rank(model, image_features, text_array, top_count=1):

top_count = min(top_count, len(text_array))

text_tokens = clip.tokenize([text for text in text_array]).cuda()

with torch.no_grad():

text_features = model.encode_text(text_tokens).float()

text_features /= text_features.norm(dim=-1, keepdim=True)

similarity = torch.zeros((1, len(text_array))).to(device)

for i in range(image_features.shape[0]):

similarity += (100.0 * image_features[i].unsqueeze(0) @ text_features.T).softmax(dim=-1)

similarity /= image_features.shape[0]

top_probs, top_labels = similarity.cpu().topk(top_count, dim=-1)

return [(text_array[top_labels[0][i].numpy()], (top_probs[0][i].numpy()*100)) for i in range(top_count)]

def interrogate(image, models):

caption = generate_caption(image)

if len(models) == 0:

print(f"\n\n{caption}")

return

table = []

bests = [[('',0)]]*5

for model_name in models:

print(f"Interrogating with {model_name}...")

model, preprocess = clip.load(model_name)

model.cuda().eval()

images = preprocess(image).unsqueeze(0).cuda()

with torch.no_grad():

image_features = model.encode_image(images).float()

image_features /= image_features.norm(dim=-1, keepdim=True)

ranks = [

rank(model, image_features, mediums),

rank(model, image_features, ["by "+artist for artist in artists]),

rank(model, image_features, trending_list),

rank(model, image_features, movements),

rank(model, image_features, flavors, top_count=3)

]

for i in range(len(ranks)):

confidence_sum = 0

for ci in range(len(ranks[i])):

confidence_sum += ranks[i][ci][1]

if confidence_sum > sum(bests[i][t][1] for t in range(len(bests[i]))):

bests[i] = ranks[i]

row = [model_name]

for r in ranks:

row.append(', '.join([f"{x[0]} ({x[1]:0.1f}%)" for x in r]))

table.append(row)

del model

gc.collect()

display(pd.DataFrame(table, columns=["Model", "Medium", "Artist", "Trending", "Movement", "Flavors"]))

flaves = ', '.join([f"{x[0]}" for x in bests[4]])

medium = bests[0][0][0]

if caption.startswith(medium):

text = f"{caption} {bests[1][0][0]}, {bests[2][0][0]}, {bests[3][0][0]}, {flaves}"

else:

text = f"{caption}, {medium} {bests[1][0][0]}, {bests[2][0][0]}, {bests[3][0][0]}, {flaves}"

return text

# setting

data_path = "../clip-interrogator/data/"

artists = load_list(os.path.join(data_path, 'artists.txt'))

flavors = load_list(os.path.join(data_path, 'flavors.txt'))

mediums = load_list(os.path.join(data_path, 'mediums.txt'))

movements = load_list(os.path.join(data_path, 'movements.txt'))

sites = ['Artstation', 'behance', 'cg society', 'cgsociety', 'deviantart', 'dribble', 'flickr', 'instagram', 'pexels', 'pinterest', 'pixabay', 'pixiv', 'polycount', 'reddit', 'shutterstock', 'tumblr', 'unsplash', 'zbrush central']

trending_list = [site for site in sites]

trending_list.extend(["trending on "+site for site in sites])

trending_list.extend(["featured on "+site for site in sites])

trending_list.extend([site+" contest winner" for site in sites])

# download sample pics

import gdown

gdown.download('https://drive.google.com/uc?id=1Mjwnr_m3pgxTPB7kusePjmKDHktGeV2w', 'pics.zip', quiet=False)

! unzip pics.zip

　次に、text2musicをセットアップします。このとき、emailには自分のemailアドレス（事前申請は不要）を記入して実行します。

#@title **setup text2music (Mubert)**

# install library
! 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


# import library
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


# difine function
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}\n\n\n")
        ret.append((prompts[i], list(top_tags)))
    return ret


# get token
email = "" #@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}')

#@title **setup text2music (Mubert)**

# install library

! 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

# import library

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

# difine function

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}\n\n\n")

ret.append((prompts[i], list(top_tags)))

return ret

# get token

email = "" #@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}')

　それでは、画像からテキストを生成します。imgで画像ファイルを指定して実行します。ここでは、サンプル画像 01.jpg〜04.jpg の中から 02.jpgを指定します。

　自分の用意した画像を使いたい場合は、事前に BLIP/pics フォルダその画像をアップロードしておいて下さい。

#@title **img2text**

img = "02.jpg" #@param {type:"string"}
image_path ='pics/'+ img
image = Image.open(image_path).convert('RGB')
thumb = image.copy()
thumb.thumbnail([blip_image_eval_size, blip_image_eval_size])
display(thumb)

text = interrogate(image, models=['ViT-L/14'])
print('text = ', text)

#@title **img2text**

img = "02.jpg" #@param {type:"string"}

image_path ='pics/'+ img

image = Image.open(image_path).convert('RGB')

thumb = image.copy()

thumb.thumbnail([blip_image_eval_size, blip_image_eval_size])

display(thumb)

text = interrogate(image, models=['ViT-L/14'])

print('text = ', text)

　５つの属性（Medium, Artist, Trending, Movement, Flavors）に関して、類似性トップの内容が表示され、それを踏まえてテキストが生成されています。

　そして、テキストから音楽を生成します。duration に曲の長さ（秒）、曲をループしたい場合には loop にチェックを入れて実行します。

#@title **text2music🎵**
import time

print('text = ', text)
prompt = text
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]
  print('tags = ', tags)
  
  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)