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Parent(s):
Duplicate from OneAfterlife/MubertTTM
Browse filesCo-authored-by: Vitaly Annor <OneAfterlife@users.noreply.huggingface.co>
- .gitattributes +34 -0
- README.md +14 -0
- app.py +98 -0
- constants.py +7 -0
- requirements.txt +2 -0
- utils.py +50 -0
.gitattributes
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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title: MubertTTM
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emoji: 😻
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colorFrom: green
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colorTo: indigo
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sdk: gradio
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sdk_version: 3.9
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app_file: app.py
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pinned: false
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license: osl-3.0
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duplicated_from: OneAfterlife/MubertTTM
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import time
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import gradio as gr
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from sentence_transformers import SentenceTransformer
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import httpx
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import json
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from utils import get_tags_for_prompts, get_mubert_tags_embeddings, get_pat
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minilm = SentenceTransformer('all-MiniLM-L6-v2')
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mubert_tags_embeddings = get_mubert_tags_embeddings(minilm)
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def get_track_by_tags(tags, pat, duration, maxit=20, loop=False):
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if loop:
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mode = "loop"
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else:
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mode = "track"
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r = httpx.post('https://api-b2b.mubert.com/v2/RecordTrackTTM',
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json={
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"method": "RecordTrackTTM",
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"params": {
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"pat": pat,
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"duration": duration,
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"tags": tags,
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"mode": mode
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}
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})
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rdata = json.loads(r.text)
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assert rdata['status'] == 1, rdata['error']['text']
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trackurl = rdata['data']['tasks'][0]['download_link']
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print('Generating track ', end='')
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for i in range(maxit):
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r = httpx.get(trackurl)
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if r.status_code == 200:
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return trackurl
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time.sleep(1)
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def generate_track_by_prompt(email, prompt, duration, loop=False):
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try:
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pat = get_pat(email)
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_, tags = get_tags_for_prompts(minilm, mubert_tags_embeddings, [prompt, ])[0]
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return get_track_by_tags(tags, pat, int(duration), loop=loop), "Success", ",".join(tags)
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except Exception as e:
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return None, str(e), ""
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block = gr.Blocks()
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with block:
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gr.HTML(
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"""
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<div style="text-align: center; max-width: 700px; margin: 0 auto;">
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<div
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style="
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display: inline-flex;
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align-items: center;
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gap: 0.8rem;
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font-size: 1.75rem;
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"
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>
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<h1 style="font-weight: 900; margin-bottom: 7px;">
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Mubert Text to Music
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</h1>
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</div>
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<p style="margin-bottom: 10px; font-size: 94%">
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All music is generated by Mubert API – <a href="https://mubert.com" style="text-decoration: underline;" target="_blank">www.mubert.com</a>
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</p>
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</div>
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"""
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)
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with gr.Group():
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with gr.Box():
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email = gr.Textbox(label="Enter your email (for API token)")
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prompt = gr.Textbox(label="Key prompts to generate a track (genre, theme, etc.)")
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duration = gr.Slider(label="Duration (seconds)", value=60, maximum=300)
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is_loop = gr.Checkbox(label="Generate loop")
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out = gr.Audio()
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result_msg = gr.Text(label="Result message")
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tags = gr.Text(label="Interpreted tags from your key prompts")
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btn = gr.Button("Submit").style(full_width=True)
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btn.click(fn=generate_track_by_prompt, inputs=[email, prompt, duration, is_loop], outputs=[out, result_msg, tags])
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gr.HTML('''
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<div class="footer" style="text-align: center; max-width: 700px; margin: 0 auto;">
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</div>
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</div>
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<p style="margin-bottom: 10px; font-size: 94%">
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if you put anything over 250 seconds, you will need to wait 10 or 30 second after it is done processing.
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</div>
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''')
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block.launch()
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constants.py
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import numpy as np
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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'
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MUBERT_TAGS = np.array(MUBERT_TAGS_STRING.split(','))
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MUBERT_LICENSE = "ttmmubertlicense#f0acYBenRcfeFpNT4wpYGaTQIyDI4mJGv5MfIhBFz97NXDwDNFHmMRsBSzmGsJwbTpP1A6i07AXcIeAHo5"
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MUBERT_MODE = "loop"
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MUBERT_TOKEN = "4951f6428e83172a4f39de05d5b3ab10d58560b8"
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requirements.txt
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httpx
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sentence-transformers
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utils.py
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import json
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import numpy as np
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import httpx
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from constants import MUBERT_TAGS, MUBERT_LICENSE, MUBERT_MODE, MUBERT_TOKEN
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def get_mubert_tags_embeddings(w2v_model):
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return w2v_model.encode(MUBERT_TAGS)
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def get_pat(email: str):
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r = httpx.post('https://api-b2b.mubert.com/v2/GetServiceAccess',
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json={
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"method": "GetServiceAccess",
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"params": {
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"email": email,
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"license": MUBERT_LICENSE,
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"token": MUBERT_TOKEN,
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"mode": MUBERT_MODE,
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}
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})
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rdata = json.loads(r.text)
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assert rdata['status'] == 1, "probably incorrect e-mail"
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pat = rdata['data']['pat']
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return pat
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def find_similar(em, embeddings, method='cosine'):
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scores = []
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for ref in embeddings:
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if method == 'cosine':
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scores.append(1 - np.dot(ref, em) / (np.linalg.norm(ref) * np.linalg.norm(em)))
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if method == 'norm':
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scores.append(np.linalg.norm(ref - em))
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return np.array(scores), np.argsort(scores)
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def get_tags_for_prompts(w2v_model, mubert_tags_embeddings, prompts, top_n=3, debug=False):
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prompts_embeddings = w2v_model.encode(prompts)
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ret = []
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for i, pe in enumerate(prompts_embeddings):
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scores, idxs = find_similar(pe, mubert_tags_embeddings)
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top_tags = MUBERT_TAGS[idxs[:top_n]]
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top_prob = 1 - scores[idxs[:top_n]]
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if debug:
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print(f"Prompt: {prompts[i]}\nTags: {', '.join(top_tags)}\nScores: {top_prob}\n\n\n")
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ret.append((prompts[i], list(top_tags)))
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return ret
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