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PierreHanna
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Parent(s):
Duplicate from PierreHanna/AudioSimilarity
Browse files- .gitattributes +36 -0
- README.md +13 -0
- app.py +106 -0
- dict_bmg.pickle +3 -0
- index.filenames +3 -0
- index.index +3 -0
- requirements.txt +4 -0
.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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index.filenames filter=lfs diff=lfs merge=lfs -text
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index.index filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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title: AudioSimilarity
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emoji: 🐢
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colorFrom: green
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colorTo: blue
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sdk: gradio
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sdk_version: 3.28.0
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app_file: app.py
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pinned: false
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duplicated_from: PierreHanna/AudioSimilarity
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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 json
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import os
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import openl3
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import librosa
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import pickle
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import gradio as gr
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import numpy as np
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import faiss
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from pytube import YouTube
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embed_html1 = '<iframe width="560" height="315" src="https://www.youtube.com/embed/'
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embed_html2 = '" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>'
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def download_audio(id_video):
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video = YouTube(id_video,use_oauth=True, allow_oauth_cache=True)
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id = id_video.split("?v=")[-1][:11]
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print("ID youtube ", id)
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audio_streams = video.streams.filter(only_audio=True)
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audio_stream = audio_streams[0]
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audio_file = audio_stream.download()
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audio_parts = audio_file.split('/')[-1]
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audio_title = '.'.join(audio_parts.split('.')[:-1])
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embed_html_all = embed_html1 + id +embed_html2
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return audio_file, audio_file, embed_html_all
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def process_url(input_path):
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# setup the client
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audio_file, audio_file, embed_html_all = download_audio(input_path)
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return process(audio_file, embed_html_all)
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def process_file(input_path):
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return process(input_path, '')
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def process(audio_file, embed_html_all):
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model = openl3.models.load_audio_embedding_model(input_repr="linear", content_type="music",embedding_size=512)
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audio, sr = librosa.load(audio_file, mono=True)
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emb, ts = openl3.get_audio_embedding(audio, sr, model=model)
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emb = np.mean(emb, axis=0)
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print("SHAPE ", emb.shape)
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ind = faiss.read_index("index.index")
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ind_filenames = pickle.load(open('index.filenames','rb'))
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dict_bmg = pickle.load(open('dict_bmg.pickle','rb')) # filename to url
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D, I = ind.search(emb.reshape((1,512)), 5)
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top1 = dict_bmg[ind_filenames[I[0][0]]]
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top2 = dict_bmg[ind_filenames[I[0][1]]]
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top3 = dict_bmg[ind_filenames[I[0][2]]]
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top4 = dict_bmg[ind_filenames[I[0][3]]]
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top5 = dict_bmg[ind_filenames[I[0][4]]]
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return top1, top2, top3, top4, top5
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with gr.Blocks() as demo:
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with gr.Row():
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with gr.Column():
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with gr.Row():
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#gr.HTML(embed_html)
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html = gr.HTML()
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with gr.Row():
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with gr.Column():
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audio_url_input = gr.Textbox(placeholder='YouTube video URL', label='YouTube video URL')
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analyze_url_btn = gr.Button('Search from URL')
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with gr.Row():
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with gr.Column():
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audio_input_file = gr.Audio(type="filepath", label='Audio Input')
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analyze_file_btn = gr.Button('Search from file')
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with gr.Row():
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with gr.Column():
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'''
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gr.HTML("<h3>Top 1</h3>")
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top1 = gr.Textbox(label="top1", show_label=False)
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gr.HTML("<h3>Top 2</h3>")
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top2 = gr.Textbox(label="top2", show_label=False)
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gr.HTML("<h3>Top 3</h3>")
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top3 = gr.Textbox(label="top3", show_label=False)
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gr.HTML("<h3>Top 4</h3>")
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top4 = gr.Textbox(label="top4", show_label=False)
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gr.HTML("<h3>Top 5</h3>")
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top5 = gr.Textbox(label="top5", show_label=False)
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'''
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top1 = gr.Audio(label="top1", show_label=True)
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top2 = gr.Audio(label="top2", show_label=True)
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top3 = gr.Audio(label="top3", show_label=True)
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top4 = gr.Audio(label="top4", show_label=True)
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top5 = gr.Audio(label="top5", show_label=True)
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analyze_url_btn.click(process_url, inputs=[audio_url_input],
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outputs=[top1, top2, top3, top4, top5])
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analyze_file_btn.click(process_file, inputs=[audio_input_file],
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outputs=[top1, top2, top3, top4, top5])
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demo.launch(debug=True)
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dict_bmg.pickle
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version https://git-lfs.github.com/spec/v1
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oid sha256:3e67645329cc091ba56c358ba3dc61032a457878f3b879f0e2e720294d5337b1
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size 15953609
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index.filenames
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version https://git-lfs.github.com/spec/v1
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oid sha256:4d4b8ec64b35965b7ca3d202d767f4c9549934683bf1b854a2efc4aa12d1634d
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size 1987986
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index.index
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version https://git-lfs.github.com/spec/v1
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oid sha256:8db1af12dc9323081564f89052ea8cd331a826b5066ec6bbe8943b8b5c7f094b
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size 117442605
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requirements.txt
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pytube
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faiss-cpu
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librosa
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openl3
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