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fabiogra
commited on
Commit
·
b0a9f8f
1
Parent(s):
2e3ca25
feat: add separate examples, logs and improvements
Browse files- app/helpers.py +10 -17
- app/pages/Separate.py +117 -58
- app/style.py +6 -0
- requirements.in +1 -0
- requirements.txt +7 -5
- scripts/inference.py +23 -2
- scripts/prepare_samples.sh +18 -0
- scripts/separate_songs.json +8 -0
app/helpers.py
CHANGED
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@@ -1,5 +1,4 @@
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import json
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-
import logging
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import os
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import random
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from base64 import b64encode
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@@ -8,7 +7,6 @@ from pathlib import Path
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import matplotlib.pyplot as plt
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import numpy as np
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import requests
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import streamlit as st
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from PIL import Image
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from pydub import AudioSegment
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@@ -20,7 +18,7 @@ extensions = ["mp3", "wav", "ogg", "flac"] # we will look for all those file ty
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def check_file_availability(url):
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exit_status = os.system(f"wget --spider {url}")
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return exit_status == 0
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@@ -33,18 +31,6 @@ def url_is_valid(url):
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st.error("Extension not supported.")
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return False
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try:
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r = requests.get(url)
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r.raise_for_status()
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return True
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except requests.exceptions.HTTPError as err:
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msg = (
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"requests get failed with status code "
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+ str(err.response.status_code)
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+ " for url "
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+ url
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+ ". Try wget spider."
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)
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logging.error(msg)
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return check_file_availability(url)
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except Exception:
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st.error("URL is not valid.")
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@@ -79,12 +65,19 @@ def plot_audio(_audio_segment: AudioSegment, *args, **kwargs) -> Image.Image:
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@st.cache_data(show_spinner=False)
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def load_list_of_songs():
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-
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def get_random_song():
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sample_songs = load_list_of_songs()
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name, url = random.choice(list(sample_songs.items()))
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return name, url
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import json
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import os
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import random
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from base64 import b64encode
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import matplotlib.pyplot as plt
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import numpy as np
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import streamlit as st
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from PIL import Image
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from pydub import AudioSegment
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def check_file_availability(url):
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exit_status = os.system(f"wget -o --spider {url}")
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return exit_status == 0
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st.error("Extension not supported.")
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return False
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try:
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return check_file_availability(url)
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except Exception:
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st.error("URL is not valid.")
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@st.cache_data(show_spinner=False)
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def load_list_of_songs(path="sample_songs.json"):
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if os.environ.get("PREPARE_SAMPLES"):
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return json.load(open(path))
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else:
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st.error(
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"No examples available. You need to set the environment variable `PREPARE_SAMPLES=true`"
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)
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def get_random_song():
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sample_songs = load_list_of_songs()
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if sample_songs is None:
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return None, None
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name, url = random.choice(list(sample_songs.items()))
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return name, url
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app/pages/Separate.py
CHANGED
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@@ -1,21 +1,22 @@
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import os
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from pathlib import Path
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import streamlit as st
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from
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from service.demucs_runner import separator
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from helpers import (
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load_audio_segment,
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plot_audio,
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st_local_audio,
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url_is_valid,
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)
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from service.vocal_remover.runner import separate, load_model
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from footer import footer
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from header import header
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label_sources = {
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"no_vocals.mp3": "🎶 Instrumental",
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@@ -27,28 +28,104 @@ label_sources = {
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"other.mp3": "🎶 Other",
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}
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-
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out_path = Path("/tmp")
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in_path = Path("/tmp")
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def reset_execution():
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st.session_state.executed = False
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def body():
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filename = None
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cols = st.columns([1, 3, 2, 1])
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with cols[1]:
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with st.columns([1,
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option = option_menu(
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menu_title=None,
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options=["Upload File", "From URL"],
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icons=["cloud-upload-fill", "link-45deg"],
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orientation="horizontal",
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styles={
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key="option_separate",
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)
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if option == "Upload File":
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filename = uploaded_file.name
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st_local_audio(in_path / filename, key="input_upload_file")
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elif option == "From URL":
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url = st.text_input(
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"Paste the URL of the audio file",
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key="url_input",
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help="Supported formats: mp3, wav, ogg, flac.",
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)
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if url != "":
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os.system(f"wget -O {in_path / filename} {url}")
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st_local_audio(in_path / filename, key="input_from_url")
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with cols[2]:
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separation_mode = st.selectbox(
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"Choose the separation mode",
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max_duration = 30
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else:
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max_duration = 15
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if filename is not None:
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song = load_audio_segment(in_path / filename, filename.split(".")[-1])
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st.session_state.executed = False
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if not st.session_state.executed:
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song.export(in_path / filename, format=filename.split(".")[-1])
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with st.spinner("Separating source audio, it will take a while..."):
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if
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model_name = "vocal_remover"
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model, device = load_model(pretrained_model="baseline.pth")
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separate(
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input=in_path / filename,
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@@ -137,13 +229,7 @@ def body():
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)
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else:
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stem = None
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if (
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separation_mode
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== "Vocal, Drums, Bass, Guitar, Piano & Other (Slowest)"
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):
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model_name = "htdemucs_6s"
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elif separation_mode == "Vocals & Instrumental (High Quality, Slower)":
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stem = "vocals"
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separator(
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start_time=start_time,
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end_time=end_time,
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)
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filename = None
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st.session_state.executed = True
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for file in [
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"no_vocals.mp3",
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"vocals.mp3",
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"drums.mp3",
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"bass.mp3",
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"guitar.mp3",
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"piano.mp3",
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"other.mp3",
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]:
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fullpath = path / file
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if fullpath.exists():
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sources[file] = fullpath
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return sources
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sources = get_sources(out_path / Path(model_name) / last_dir)
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tab_sources = st.tabs([f"**{label_sources.get(k)}**" for k in sources.keys()])
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for i, (file, pathname) in enumerate(sources.items()):
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with tab_sources[i]:
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cols = st.columns(2)
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with cols[0]:
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auseg = load_audio_segment(pathname, "mp3")
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st.image(
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plot_audio(auseg, title="", file=file),
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use_column_width="always",
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)
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with cols[1]:
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st_local_audio(pathname, key=f"output_{file}")
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if __name__ == "__main__":
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import os
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from pathlib import Path
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from typing import List
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from loguru import logger as log
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import streamlit as st
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from footer import footer
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from header import header
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from helpers import (
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load_audio_segment,
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load_list_of_songs,
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plot_audio,
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st_local_audio,
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url_is_valid,
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)
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from service.demucs_runner import separator
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from service.vocal_remover.runner import load_model, separate
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from streamlit_option_menu import option_menu
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label_sources = {
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"no_vocals.mp3": "🎶 Instrumental",
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"other.mp3": "🎶 Other",
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}
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separation_mode_to_model = {
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"Vocals & Instrumental (Faster)": ("vocal_remover", ["vocals.mp3", "no_vocals.mp3"]),
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"Vocals & Instrumental (High Quality, Slower)": ("htdemucs", ["vocals.mp3", "no_vocals.mp3"]),
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"Vocals, Drums, Bass & Other (Slower)": (
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"htdemucs",
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["vocals.mp3", "drums.mp3", "bass.mp3", "other.mp3"],
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),
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"Vocal, Drums, Bass, Guitar, Piano & Other (Slowest)": (
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"htdemucs_6s",
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["vocals.mp3", "drums.mp3", "bass.mp3", "guitar.mp3", "piano.mp3", "other.mp3"],
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),
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}
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extensions = ["mp3", "wav", "ogg", "flac"]
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out_path = Path("/tmp")
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in_path = Path("/tmp")
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@st.cache_data(show_spinner=False)
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def get_sources(path, file_sources):
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sources = {}
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for file in file_sources:
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fullpath = path / file
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if fullpath.exists():
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sources[file] = fullpath
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return sources
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def reset_execution():
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st.session_state.executed = False
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def show_results(model_name: str, dir_name_output: str, file_sources: List):
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sources = get_sources(out_path / Path(model_name) / dir_name_output, file_sources)
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tab_sources = st.tabs([f"**{label_sources.get(k)}**" for k in sources.keys()])
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for i, (file, pathname) in enumerate(sources.items()):
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with tab_sources[i]:
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cols = st.columns(2)
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with cols[0]:
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auseg = load_audio_segment(pathname, "mp3")
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st.image(
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plot_audio(
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auseg,
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title="",
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file=file,
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model_name=model_name,
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dir_name_output=dir_name_output,
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),
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use_column_width="always",
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)
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with cols[1]:
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st_local_audio(pathname, key=f"output_{file}_{dir_name_output}")
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log.info(f"Displaying results for {dir_name_output}")
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+
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def body():
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filename = None
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name_song = None
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st.markdown(
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"""
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<style>
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div[data-baseweb="tab-list"] {
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align-items: center !important;
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justify-content: center !important;
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}
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</style>""",
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unsafe_allow_html=True,
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)
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cols = st.columns([1, 3, 2, 1])
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with cols[1]:
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with st.columns([1, 8, 1])[1]:
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option = option_menu(
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menu_title=None,
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options=["Upload File", "From URL", "Examples"],
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icons=["cloud-upload-fill", "link-45deg", "music-note-list"],
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orientation="horizontal",
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styles={
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"container": {
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"width": "100%",
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"height": "3.5rem",
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"margin": "0px",
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"padding": "0px",
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},
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"icon": {"font-size": "1rem"},
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"nav-link": {
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"display": "flex",
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"height": "3rem",
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"justify-content": "center",
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"align-items": "center",
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"text-align": "center",
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"flex-direction": "column",
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"font-size": "1rem",
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"padding-left": "0px",
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"padding-right": "0px",
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},
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},
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key="option_separate",
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)
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| 131 |
if option == "Upload File":
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filename = uploaded_file.name
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| 142 |
st_local_audio(in_path / filename, key="input_upload_file")
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+
elif option == "From URL":
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url = st.text_input(
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| 146 |
"Paste the URL of the audio file",
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key="url_input",
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help="Supported formats: mp3, wav, ogg, flac.",
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)
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| 150 |
+
if url != "" and url_is_valid(url):
|
| 151 |
+
with st.spinner("Downloading audio..."):
|
| 152 |
+
filename = url.split("/")[-1]
|
| 153 |
+
os.system(f"wget -q -O {in_path / filename} {url}")
|
|
|
|
| 154 |
st_local_audio(in_path / filename, key="input_from_url")
|
| 155 |
+
elif option == "Examples":
|
| 156 |
+
samples_song = load_list_of_songs(path="separate_songs.json")
|
| 157 |
+
if samples_song is not None:
|
| 158 |
+
name_song = st.selectbox(
|
| 159 |
+
label="Select a song",
|
| 160 |
+
options=list(samples_song.keys()),
|
| 161 |
+
format_func=lambda x: x.replace("_", " "),
|
| 162 |
+
index=1,
|
| 163 |
+
key="select_example",
|
| 164 |
+
)
|
| 165 |
+
if (Path("/tmp") / name_song).exists():
|
| 166 |
+
st_local_audio(Path("/tmp") / name_song, key=f"input_from_sample_{name_song}")
|
| 167 |
+
else:
|
| 168 |
+
name_song = None
|
| 169 |
+
|
| 170 |
with cols[2]:
|
| 171 |
separation_mode = st.selectbox(
|
| 172 |
"Choose the separation mode",
|
|
|
|
| 183 |
max_duration = 30
|
| 184 |
else:
|
| 185 |
max_duration = 15
|
| 186 |
+
model_name, file_sources = separation_mode_to_model[separation_mode]
|
| 187 |
|
| 188 |
if filename is not None:
|
| 189 |
song = load_audio_segment(in_path / filename, filename.split(".")[-1])
|
|
|
|
| 216 |
st.session_state.executed = False
|
| 217 |
|
| 218 |
if not st.session_state.executed:
|
| 219 |
+
log.info(f"{option} - Separating {filename} with {separation_mode}...")
|
| 220 |
song.export(in_path / filename, format=filename.split(".")[-1])
|
| 221 |
with st.spinner("Separating source audio, it will take a while..."):
|
| 222 |
+
if model_name == "vocal_remover":
|
|
|
|
| 223 |
model, device = load_model(pretrained_model="baseline.pth")
|
| 224 |
separate(
|
| 225 |
input=in_path / filename,
|
|
|
|
| 229 |
)
|
| 230 |
else:
|
| 231 |
stem = None
|
| 232 |
+
if separation_mode == "Vocals & Instrumental (High Quality, Slower)":
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 233 |
stem = "vocals"
|
| 234 |
|
| 235 |
separator(
|
|
|
|
| 248 |
start_time=start_time,
|
| 249 |
end_time=end_time,
|
| 250 |
)
|
| 251 |
+
dir_name_output = ".".join(filename.split(".")[:-1])
|
| 252 |
filename = None
|
| 253 |
st.session_state.executed = True
|
| 254 |
+
show_results(model_name, dir_name_output, file_sources)
|
| 255 |
+
elif name_song is not None and option == "Examples":
|
| 256 |
+
show_results(model_name, name_song, file_sources)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 257 |
|
| 258 |
|
| 259 |
if __name__ == "__main__":
|
app/style.py
CHANGED
|
@@ -124,6 +124,12 @@ CSS = (
|
|
| 124 |
gap: 0rem;
|
| 125 |
}
|
| 126 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 127 |
|
| 128 |
</style>
|
| 129 |
|
|
|
|
| 124 |
gap: 0rem;
|
| 125 |
}
|
| 126 |
|
| 127 |
+
/* center the audio player in Separate page */
|
| 128 |
+
.css-keje6w.e1tzin5v1 {
|
| 129 |
+
display: flex;
|
| 130 |
+
justify-content: center;
|
| 131 |
+
align-items: center;
|
| 132 |
+
}
|
| 133 |
|
| 134 |
</style>
|
| 135 |
|
requirements.in
CHANGED
|
@@ -14,3 +14,4 @@ resampy==0.4.2
|
|
| 14 |
stqdm==0.0.5
|
| 15 |
streamlit_option_menu==0.3.6
|
| 16 |
htbuilder==0.6.1
|
|
|
|
|
|
| 14 |
stqdm==0.0.5
|
| 15 |
streamlit_option_menu==0.3.6
|
| 16 |
htbuilder==0.6.1
|
| 17 |
+
loguru==0.7.0
|
requirements.txt
CHANGED
|
@@ -38,7 +38,7 @@ contourpy==1.1.0
|
|
| 38 |
# via matplotlib
|
| 39 |
cycler==0.11.0
|
| 40 |
# via matplotlib
|
| 41 |
-
cython==0.29.
|
| 42 |
# via diffq
|
| 43 |
decorator==5.1.1
|
| 44 |
# via
|
|
@@ -91,14 +91,16 @@ kaleido==0.2.1
|
|
| 91 |
# via -r requirements.in
|
| 92 |
kiwisolver==1.4.4
|
| 93 |
# via matplotlib
|
| 94 |
-
lameenc==1.5.
|
| 95 |
# via demucs
|
| 96 |
-
lazy-loader==0.
|
| 97 |
# via librosa
|
| 98 |
librosa==0.10.0.post2
|
| 99 |
# via -r requirements.in
|
| 100 |
llvmlite==0.40.1
|
| 101 |
# via numba
|
|
|
|
|
|
|
| 102 |
markdown-it-py==3.0.0
|
| 103 |
# via rich
|
| 104 |
markupsafe==2.1.3
|
|
@@ -152,7 +154,7 @@ pandas==1.5.3
|
|
| 152 |
# -r requirements.in
|
| 153 |
# altair
|
| 154 |
# streamlit
|
| 155 |
-
pillow==
|
| 156 |
# via
|
| 157 |
# matplotlib
|
| 158 |
# streamlit
|
|
@@ -271,7 +273,7 @@ tqdm==4.65.0
|
|
| 271 |
# stqdm
|
| 272 |
treetable==0.2.5
|
| 273 |
# via dora-search
|
| 274 |
-
typing-extensions==4.7.
|
| 275 |
# via
|
| 276 |
# librosa
|
| 277 |
# rich
|
|
|
|
| 38 |
# via matplotlib
|
| 39 |
cycler==0.11.0
|
| 40 |
# via matplotlib
|
| 41 |
+
cython==0.29.36
|
| 42 |
# via diffq
|
| 43 |
decorator==5.1.1
|
| 44 |
# via
|
|
|
|
| 91 |
# via -r requirements.in
|
| 92 |
kiwisolver==1.4.4
|
| 93 |
# via matplotlib
|
| 94 |
+
lameenc==1.5.1
|
| 95 |
# via demucs
|
| 96 |
+
lazy-loader==0.3
|
| 97 |
# via librosa
|
| 98 |
librosa==0.10.0.post2
|
| 99 |
# via -r requirements.in
|
| 100 |
llvmlite==0.40.1
|
| 101 |
# via numba
|
| 102 |
+
loguru==0.7.0
|
| 103 |
+
# via -r requirements.in
|
| 104 |
markdown-it-py==3.0.0
|
| 105 |
# via rich
|
| 106 |
markupsafe==2.1.3
|
|
|
|
| 154 |
# -r requirements.in
|
| 155 |
# altair
|
| 156 |
# streamlit
|
| 157 |
+
pillow==10.0.0
|
| 158 |
# via
|
| 159 |
# matplotlib
|
| 160 |
# streamlit
|
|
|
|
| 273 |
# stqdm
|
| 274 |
treetable==0.2.5
|
| 275 |
# via dora-search
|
| 276 |
+
typing-extensions==4.7.1
|
| 277 |
# via
|
| 278 |
# librosa
|
| 279 |
# rich
|
scripts/inference.py
CHANGED
|
@@ -1,7 +1,9 @@
|
|
| 1 |
import argparse
|
|
|
|
| 2 |
|
| 3 |
import warnings
|
| 4 |
from app.service.vocal_remover.runner import load_model, separate
|
|
|
|
| 5 |
|
| 6 |
warnings.simplefilter("ignore", UserWarning)
|
| 7 |
warnings.simplefilter("ignore", FutureWarning)
|
|
@@ -14,16 +16,35 @@ def main():
|
|
| 14 |
p.add_argument("--pretrained_model", "-P", type=str, default="baseline.pth")
|
| 15 |
p.add_argument("--input", "-i", required=True)
|
| 16 |
p.add_argument("--output_dir", "-o", type=str, default="")
|
|
|
|
| 17 |
args = p.parse_args()
|
| 18 |
|
|
|
|
|
|
|
| 19 |
model, device = load_model(pretrained_model=args.pretrained_model)
|
| 20 |
separate(
|
| 21 |
-
input=
|
| 22 |
model=model,
|
| 23 |
device=device,
|
| 24 |
output_dir=args.output_dir,
|
| 25 |
-
only_no_vocals=
|
| 26 |
)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 27 |
|
| 28 |
|
| 29 |
if __name__ == "__main__":
|
|
|
|
| 1 |
import argparse
|
| 2 |
+
from pathlib import Path
|
| 3 |
|
| 4 |
import warnings
|
| 5 |
from app.service.vocal_remover.runner import load_model, separate
|
| 6 |
+
from app.service.demucs_runner import separator
|
| 7 |
|
| 8 |
warnings.simplefilter("ignore", UserWarning)
|
| 9 |
warnings.simplefilter("ignore", FutureWarning)
|
|
|
|
| 16 |
p.add_argument("--pretrained_model", "-P", type=str, default="baseline.pth")
|
| 17 |
p.add_argument("--input", "-i", required=True)
|
| 18 |
p.add_argument("--output_dir", "-o", type=str, default="")
|
| 19 |
+
p.add_argument("--only_no_vocals", "-n", action="store_true")
|
| 20 |
args = p.parse_args()
|
| 21 |
|
| 22 |
+
input_file = args.input
|
| 23 |
+
|
| 24 |
model, device = load_model(pretrained_model=args.pretrained_model)
|
| 25 |
separate(
|
| 26 |
+
input=input_file,
|
| 27 |
model=model,
|
| 28 |
device=device,
|
| 29 |
output_dir=args.output_dir,
|
| 30 |
+
only_no_vocals=args.only_no_vocals,
|
| 31 |
)
|
| 32 |
+
if not args.only_no_vocals:
|
| 33 |
+
for stem, model_name in [("vocals", "htdemucs"), (None, "htdemucs"), (None, "htdemucs_6s")]:
|
| 34 |
+
separator(
|
| 35 |
+
tracks=[Path(input_file)],
|
| 36 |
+
out=Path(args.output_dir),
|
| 37 |
+
model=model_name,
|
| 38 |
+
shifts=1,
|
| 39 |
+
overlap=0.5,
|
| 40 |
+
stem=stem,
|
| 41 |
+
int24=False,
|
| 42 |
+
float32=False,
|
| 43 |
+
clip_mode="rescale",
|
| 44 |
+
mp3=True,
|
| 45 |
+
mp3_bitrate=320,
|
| 46 |
+
verbose=False,
|
| 47 |
+
)
|
| 48 |
|
| 49 |
|
| 50 |
if __name__ == "__main__":
|
scripts/prepare_samples.sh
CHANGED
|
@@ -22,3 +22,21 @@ for name in $(echo "${json}" | jq -r 'keys[]'); do
|
|
| 22 |
python inference.py --input /tmp/${name} --output /tmp
|
| 23 |
echo "Done separating ${name}"
|
| 24 |
done
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 22 |
python inference.py --input /tmp/${name} --output /tmp
|
| 23 |
echo "Done separating ${name}"
|
| 24 |
done
|
| 25 |
+
|
| 26 |
+
|
| 27 |
+
# Read JSON file into a variable
|
| 28 |
+
json_separate=$(cat separate_songs.json)
|
| 29 |
+
|
| 30 |
+
# Iterate through keys and values
|
| 31 |
+
for name in $(echo "${json_separate}" | jq -r 'keys[]'); do
|
| 32 |
+
url=$(echo "${json_separate}" | jq -r --arg name "${name}" '.[$name]')
|
| 33 |
+
echo "Separating ${name} from ${url}"
|
| 34 |
+
|
| 35 |
+
# Download with pytube
|
| 36 |
+
yt-dlp ${url} -o "/tmp/${name}" --format "bestaudio/best" --download-sections "*45-110"
|
| 37 |
+
mkdir -p "/tmp/vocal_remover"
|
| 38 |
+
|
| 39 |
+
# Run inference
|
| 40 |
+
python inference.py --input /tmp/${name} --output /tmp --only_no_vocals false
|
| 41 |
+
echo "Done separating ${name}"
|
| 42 |
+
done
|
scripts/separate_songs.json
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"ABBA_-_Dancing_Queen": "https://www.youtube.com/watch?v=3qiMJt-JBb4",
|
| 3 |
+
"Queen_–_Bohemian_Rhapsody": "https://www.youtube.com/watch?v=yk3prd8GER4",
|
| 4 |
+
"Backstreet_Boys_-_I_Want_It_That_Way": "https://www.youtube.com/watch?v=qjlVAsvQLM8",
|
| 5 |
+
"The_Beatles_-_Let_It_Be": "https://www.youtube.com/watch?v=FIV73iG_e5I",
|
| 6 |
+
"Coldplay_-_Viva_La_Vida": "https://www.youtube.com/watch?v=a1EYnngNHIA",
|
| 7 |
+
"The_Cranberries_-_Zombie": "https://www.youtube.com/watch?v=8sM-rm4lFZg"
|
| 8 |
+
}
|