\", on_progressbar_drag_stop)\n\n # Stop the previous update thread if it exists\n stop_update_thread()\n\n # Start the progress update timer\n self.update_timer = threading.Thread(target=update_progress, daemon=True)\n self.update_timer.start()\n\nif __name__ == \"__main__\":\n app = MISSTapp()\n MISSThelpers.update_rpc(app, Ltext=\"Idle\", Dtext=\"Nothing is playing\")\n app.mainloop()"
},
{
"path": "MISST/MISSThelpers.py",
"content": "import ctypes\nimport os\nimport platform\nimport re\nimport shutil\nimport sys\nimport threading\nimport time\nimport tkinter\nimport uuid\nfrom colorsys import hls_to_rgb, rgb_to_hls\n\nimport customtkinter\nimport demucs\nimport music_tag\nimport psutil\nimport requests\nimport torch\nfrom MISSTplayer import MISSTplayer\nfrom MISSTsettings import MISSTsettings\nfrom vcolorpicker import getColor, hex2rgb, rgb2hex, useLightTheme\n\n\nclass MISSTconsole():\n \"\"\"\n A class to handle the console output of MISST\n \"\"\"\n def __init__(self, terminal:customtkinter.CTkTextbox, ogText:str) -> None:\n \"\"\"\n Parameters\n\n terminal : tkinter.Text\n The tkinter.Text widget to be used as the console\n ogText : str\n The original text to be displayed in the console\n \"\"\"\n self.consoleText = ogText\n self.terminal = terminal\n self.curThread = None\n self.terminal.delete(\"0.0\", \"end\") # delete all text\n self.terminal.insert(\"0.0\", self.consoleText)\n self.terminal.configure(state=\"disabled\")\n\n def updateThread(self, text:str) -> None:\n \"\"\"\n A thread to update the console text\n\n Args:\n text (str): The text to be added to the console\n \"\"\"\n t = 0\n while True:\n time.sleep(0.5)\n if t > 3:\n t -= t\n periods = [\"\", \".\", \"..\", \"...\"]\n self.terminal.configure(state=\"normal\")\n self.terminal.delete(\"0.0\", \"end\")\n self.terminal.insert(\"0.0\", f\"{self.consoleText}{text}{periods[t]}\")\n self.terminal.configure(state=\"disabled\")\n t += 1\n\n def update(self, text:str) -> None:\n \"\"\"\n Update the console text\n\n Args:\n text (str): The text to be added to the console\n \"\"\"\n self.curThread = threading.Thread(target=self.updateThread, args=(text,), daemon=True)\n self.curThread.start()\n\n def endUpdate(self) -> None:\n \"\"\"\n End the update thread\n\n Args:\n text (str): The text to be added to the console\n \"\"\"\n MISSThelpers.terminate_thread(self, self.curThread)\n self.terminal.configure(state=\"normal\")\n self.terminal.delete(\"0.0\", \"end\")\n self.terminal.insert(\"0.0\", self.consoleText)\n self.terminal.configure(state=\"disabled\")\n\n def addLine(self, text:str) -> None:\n \"\"\"\n Add a line to the console\n\n Args:\n text (str): The text to be added to the console\n \"\"\"\n self.consoleText += f\"{text}\"\n self.terminal.configure(state=\"normal\")\n self.terminal.delete(\"0.0\", \"end\")\n self.terminal.insert(\"0.0\", self.consoleText)\n self.terminal.configure(state=\"disabled\")\n\n def editLine(self, text:str, line_number:int) -> None:\n \"\"\"\n Edit a line in the console\n\n Args:\n text (str): The text to be added to the console\n line_number (int): The line number to be edited\n \"\"\"\n self.consoleText = text\n self.terminal.configure(state=\"normal\")\n self.terminal.delete(f\"{line_number + 1}.0\", f\"end\")\n self.terminal.insert(f\"{line_number + 1}.0\", text)\n self.terminal.configure(state=\"disabled\")\n\nclass MISSThelpers():\n \"\"\"\n A class filled with helper methods for MISST\n \"\"\"\n def update_rpc(\n self,\n Ltext:str = None,\n Dtext:str = None,\n image:str = \"icon-0\",\n large_text:str = \"MISST\",\n end_time:int = None,\n small_image:str = None,\n ) -> None:\n \"\"\"\n Update the Discord Rich Presence\n\n Args:\n Ltext (str, optional): The large text to be displayed. Defaults to None.\n Dtext (str, optional): The details text to be displayed. Defaults to None.\n image (str, optional): The image to be displayed. Defaults to \"icon-0\".\n large_text (str, optional): The large text to be displayed. Defaults to \"MISST\".\n end_time (int, optional): The end time of the activity. Defaults to None.\n small_image (str, optional): The small image to be displayed. Defaults to None.\n \"\"\"\n start_time = time.time()\n if self.RPC_CONNECTED:\n try:\n self.RPC.update(\n large_image=image,\n small_image=small_image,\n start=start_time,\n end=end_time,\n large_text=large_text,\n state=Ltext,\n details=Dtext,\n )\n except:\n return\n return\n \n def apple_music(url:str, outdir:str) -> None:\n \"\"\"\n Download an Apple Music song\n\n Args:\n url (str): The Apple Music song URL\n outdir (str): The output directory\n \"\"\"\n host = 'https://api.fabdl.com'\n info = requests.get(host + '/apple-music/get?url=', params={'url': url}).json()['result']\n convert_task = requests.get(host + f'/apple-music/mp3-convert-task/{info[\"gid\"]}/{info[\"id\"]}')\n tid = convert_task.json()['result']['tid']\n convert_task = requests.get(host + f'/apple-music/mp3-convert-progress/{tid}')\n r = requests.get(host + convert_task.json()['result']['download_url']) \n with open(f\"{outdir}/{info['artists'] + ' - ' + info['name']}.mp3\", 'wb') as f:\n f.write(r.content)\n try:\n audiofile = music_tag.load_file(f\"{outdir}/{info['artists'] + ' - ' + info['name']}.mp3\")\n audiofile['artwork'] = requests.get(info['image']).content\n audiofile.save()\n except:\n pass\n\n def change_theme(theme:str) -> None:\n \"\"\"\n Change the theme of the application\n\n Args:\n theme (str): The theme to be changed to\n \"\"\"\n customtkinter.set_appearance_mode(theme)\n\n def checkbox_event(checkbox:customtkinter.CTkCheckBox, export_slider:customtkinter.CTkSlider, sound:str, player:MISSTplayer, slider:customtkinter.CTkSlider) -> None:\n \"\"\"\n Change the volume of a sound\n\n Args:\n checkbox (tkinter.Checkbutton): The checkbox\n sound (str): The sound to be changed\n player (MISSTplayer): The sound player\n slider (tkinter.Scale): The volume slider\n \"\"\"\n if checkbox.get() == \"on\":\n player.set_volume(sound, slider.get())\n else:\n slider.set(0)\n player.set_volume(sound, slider.get())\n\n MISSThelpers.slider_event(slider.get(), export_slider, sound, player, checkbox)\n\n def slider_event(value:int, export_slider:customtkinter.CTkSlider, sound:str, player:MISSTplayer, checkbox:customtkinter.CTkCheckBox) -> None:\n \"\"\"\n Change the volume of a sound\n\n Args:\n value (int): The volume value\n sound (str): The sound to be changed\n player (MISSTplayer): The sound player\n checkbox (tkinter.Checkbutton): The checkbox\n \"\"\"\n settings = MISSTsettings()\n export_slider[0].set(value)\n if value >= 0.01:\n checkbox.set(\"on\")\n export_slider[1].configure(border_color=settings.getSetting(\"chosenLightColor\") if customtkinter.get_appearance_mode() == \"Light\" else settings.getSetting(\"chosenDarkColor\"))\n player.set_volume(sound, value)\n else:\n checkbox.set(\"off\")\n export_slider[1].configure(border_color=\"#3E454A\")\n player.set_volume(sound, value)\n \n def MISSTlistdir(self, directory:str) -> list:\n \"\"\"\n List all MISST folders in a directory\n\n Args:\n directory (str): The directory to be searched\n \"\"\"\n try:\n os_list = os.listdir(directory)\n misst_list = []\n for _ in os_list:\n required_files = [\"bass.flac\", \"drums.flac\", \"other.flac\", \"vocals.flac\", \".misst\"]\n found = 0\n for file in required_files:\n if os.path.isfile(f\"{directory}/{_}/{file}\"):\n found += 1\n if len(required_files) == found:\n misst_list.append(_)\n return misst_list\n except:\n return []\n \n def getsize(self, dir:str) -> int:\n \"\"\"\n Get the size of a directory\n\n Args:\n dir (str): The directory to be searched\n \"\"\"\n total = 0\n for entry in os.scandir(dir):\n if entry.is_file():\n total += entry.stat().st_size\n elif entry.is_dir():\n total += self.getsize(self, entry.path)\n return total\n\n def adjust_color_lightness(r:int, g:int, b:int, factor:int) -> str:\n \"\"\"\n Adjust the lightness of a color\n\n Args:\n r (int): The red value\n g (int): The green value\n b (int): The blue value\n factor (int): The factor to be adjusted by\n \"\"\"\n h, l, s = rgb_to_hls(r / 255.0, g / 255.0, b / 255.0)\n l = max(min(l * factor, 1.0), 0.0)\n r, g, b = hls_to_rgb(h, l, s)\n return f\"#{rgb2hex(int(r * 255), int(g * 255), int(b * 255))}\"\n \n def darken_color(r:int, g:int, b:int, factor:int = 0.1) -> str:\n \"\"\"\n Darken a color\n\n Args:\n r (int): The red value\n g (int): The green value\n b (int): The blue value\n factor (int, optional): The factor to be darkened by. Defaults to 0.1.\n \"\"\"\n return MISSThelpers.adjust_color_lightness(r, g, b, 1 - factor)\n\n def updateTheme(self, color:str) -> str: \n \"\"\"\n Update the theme of the application\n\n Args:\n color (str): The color to be changed\n \"\"\"\n if color == \"light\":\n cur_color = self.settings.getSetting(\"chosenLightColor\")\n old_color = (hex2rgb(cur_color.replace(\"#\", \"\")))\n useLightTheme(True if customtkinter.get_appearance_mode() == \"Light\" else False)\n self.withdraw() # Hide the window\n chosen_color = f\"#{rgb2hex(getColor(old_color))}\"\n self.deiconify() # Show the window\n rgb_chosen = hex2rgb(chosen_color.replace(\"#\", \"\"))\n self.settings.setSetting(\"chosenLightColor\", chosen_color)\n self.settings.setSetting(\"chosenLightHoverColor\", MISSThelpers.darken_color(rgb_chosen[0], rgb_chosen[1], rgb_chosen[2], 0.2))\n self.settings.setSetting(\"chosenLightDarker\", MISSThelpers.darken_color(rgb_chosen[0], rgb_chosen[1], rgb_chosen[2], 0.35))\n self.settings.applyThemeSettings(\"./Assets/Themes/MISST.json\", \"./Assets/Themes/maluableJSON\")\n self.button_light.configure(fg_color=chosen_color, hover_color=chosen_color)\n else:\n cur_color = self.settings.getSetting(\"chosenDarkColor\")\n old_color = (hex2rgb(cur_color.replace(\"#\", \"\")))\n useLightTheme(True if customtkinter.get_appearance_mode() == \"Light\" else False)\n self.withdraw() # Hide the window\n chosen_color = f\"#{rgb2hex(getColor(old_color))}\"\n self.deiconify() # Show the window\n rgb_chosen = hex2rgb(chosen_color.replace(\"#\", \"\"))\n self.settings.setSetting(\"chosenDarkColor\", chosen_color)\n self.settings.setSetting(\"chosenDarkHoverColor\", MISSThelpers.darken_color(rgb_chosen[0], rgb_chosen[1], rgb_chosen[2], 0.2))\n self.settings.setSetting(\"chosenDarkDarker\", MISSThelpers.darken_color(rgb_chosen[0], rgb_chosen[1], rgb_chosen[2], 0.35))\n self.settings.applyThemeSettings(\"./Assets/Themes/MISST.json\", \"./Assets/Themes/maluableJSON\")\n self.button_dark.configure(fg_color=chosen_color, hover_color=chosen_color)\n return chosen_color\n \n def resetSettings(self) -> None:\n \"\"\"\n Reset the settings of the application\n \"\"\"\n cuda = torch.cuda.is_available()\n self.settings.resetDefaultTheme(\"./Assets/Themes/MISST.json\", \"./Assets/Themes/maluableJSON\")\n self.settings.setSetting(\"rpc\", \"true\")\n self.settings.setSetting(\"autoplay\", \"true\")\n self.settings.setSetting(\"accelerate_on_gpu\", \"true\" if cuda else \"false\")\n self.rpc_box.select()\n self.autoplay_box.select()\n self.preprocess_method_box.select() if cuda else self.preprocess_method_box.deselect()\n self.button_light.configure(fg_color=self.settings.getSetting(\"defaultLightColor\"), hover_color=self.settings.getSetting(\"defaultLightColor\"))\n self.button_dark.configure(fg_color=self.settings.getSetting(\"defaultDarkColor\"), hover_color=self.settings.getSetting(\"defaultDarkColor\"))\n self.model_select.set(\"htdemucs\")\n self.settings.setSetting(\"chosen_model\", \"htdemucs\")\n self.change_model(self.model_select.get())\n\n def autoplay_event(self) -> None:\n \"\"\"\n Event for when the autoplay box is checked\n \"\"\"\n if self.autoplay_box.get() == 1:\n self.settings.setSetting(\"autoplay\", \"true\")\n else:\n self.settings.setSetting(\"autoplay\", \"false\")\n\n def rpc_event(self) -> None:\n \"\"\"\n Event for when the rpc box is checked\n \"\"\"\n if self.rpc_box.get() == 1:\n self.settings.setSetting(\"rpc\", \"true\")\n else:\n self.settings.setSetting(\"rpc\", \"false\")\n\n def accelerate_event(self) -> None:\n \"\"\"\n Event for when the accelerate box is checked\n \"\"\"\n if self.preprocess_method_box.get() == 1 and torch.cuda.is_available() == True:\n self.settings.setSetting(\"accelerate_on_gpu\", \"true\")\n elif self.preprocess_method_box.get() == 1 and torch.cuda.is_available() == False:\n self.preprocess_method_box.deselect()\n self.preprocess_method_box.place(relx=0.39, rely=0.85, anchor=tkinter.CENTER)\n self.preprocess_method_box.configure(text=\"CUDA Not Available\")\n self.settings.setSetting(\"accelerate_on_gpu\", \"false\")\n time.sleep(1.5)\n self.preprocess_method_box.place(relx=0.38, rely=0.85, anchor=tkinter.CENTER)\n self.preprocess_method_box.configure(text=\"Accelerate on GPU?\")\n else:\n self.settings.setSetting(\"accelerate_on_gpu\", \"false\")\n\n def clearDownloads(self) -> None:\n \"\"\"\n Clear the downloads folder\n \"\"\"\n self.confirmation_frame = customtkinter.CTkFrame(\n master=self.settings_window, width=350, height=350, corner_radius=0\n )\n self.confirmation_frame.place(relx=0.25, rely=0.5, anchor=tkinter.CENTER)\n\n self.confirmation_header = customtkinter.CTkLabel(\n master=self.confirmation_frame, text=\"Are you sure?\", font=(self.FONT, -16)\n )\n self.confirmation_header.place(relx=0.5, rely=0.45, anchor=tkinter.CENTER)\n self.confirmation_yes = customtkinter.CTkButton(\n master=self.confirmation_frame,\n text=\"Yes\",\n font=(self.FONT, -12),\n command=lambda: clear(),\n width=80,\n )\n self.confirmation_yes.place(relx=0.35, rely=0.55, anchor=tkinter.CENTER)\n self.confirmation_no = customtkinter.CTkButton(\n master=self.confirmation_frame,\n text=\"No\",\n font=(self.FONT, -12),\n command=lambda: self.confirmation_frame.destroy(),\n width=80,\n )\n self.confirmation_no.place(relx=0.65, rely=0.55, anchor=tkinter.CENTER)\n def clear():\n try:\n shutil.rmtree(self.importsDest)\n os.mkdir(self.importsDest)\n self.confirmation_frame.destroy()\n bytes = MISSThelpers.getsize(MISSThelpers, self.importsDest)\n gb = bytes / 1000000000\n gb = round(gb, 2)\n text = str(gb) + \" GB\"\n self.downloads_info.configure(text=text)\n except:\n pass\n\n def change_location(self) -> None:\n \"\"\"\n Change the location of the downloads folder\n \"\"\"\n try:\n importsdest_nocheck = tkinter.filedialog.askdirectory(\n initialdir=os.path.abspath(self.importsDest)\n )\n importsdest_nocheck = importsdest_nocheck.replace(\"\\\\\", \"/\")\n if importsdest_nocheck != \"\" and os.path.isdir(\n os.path.abspath(importsdest_nocheck)\n ):\n dummypath = os.path.join(importsdest_nocheck, str(uuid.uuid4()))\n try:\n with open(dummypath, \"w\"):\n pass\n os.remove(dummypath)\n importsdest = importsdest_nocheck\n except IOError:\n importsdest = os.path.abspath(importsdest)\n self.settings.setSetting(\"importsDest\", importsdest)\n self.importsDest = importsdest\n dir = os.path.abspath(self.importsDest)\n dirlen = len(dir)\n n = 20\n location_text = dir if dirlen <= n else \"...\" + dir[-(n - dirlen) :]\n self.storage_location_info.configure(text=location_text)\n return None\n else:\n self.importsDest = os.path.abspath(self.importsDest)\n return None\n except Exception as e:\n print(e)\n self.importsDest = os.path.abspath(self.importsDest)\n return None\n\n def loading_label(label:customtkinter.CTkLabel, text:str, og_text:str = \"\") -> None:\n \"\"\"\n Loading animation for the settings window\n\n Args:\n label (tkinter.Label): The label to animate\n text (str): The text to append to the label\n og_text (str, optional): The original text of the label. Defaults to \"\".\n \"\"\"\n t = 0\n try:\n while True:\n time.sleep(0.5)\n if label.cget(\"text\") == \"\":\n break\n if t > 3:\n t -= t\n periods = [\"\", \".\", \"..\", \"...\"]\n label.configure(text=f\"{og_text}{text}{periods[t]}\")\n t += 1\n except:\n pass\n return\n \n def GenerateSystemInfo(self) -> str:\n \"\"\"\n Generate system info for the settings window\n \"\"\"\n info = \"\"\n info += \"Python version:\\t%s\\n\" % sys.version\n info += \"System:\\t%s\\n\" % platform.platform()\n info += \"CPU:\\t%s\\n\" % platform.processor()\n info += \"Memory:\\t%.3fMB\\n\" % (psutil.virtual_memory().total / 1048576)\n info += \"PyTorch version:\\t%s\\n\" % torch.__version__\n info += \"CUDA available:\\t%s\\n\" % torch.cuda.is_available()\n if torch.cuda.is_available():\n for i in range(torch.cuda.device_count()):\n info += \"CUDA %d:\\t%s\\n\" % (i, re.findall(\"\\\\((.*)\\\\)\", str(torch.cuda.get_device_properties(i)))[0])\n info += \"Demucs version:\\t%s\\n\" % demucs.__version__\n info += \"FFMpeg available:\\t%s\\n\" % self.FFMpegAvailable\n info += \"MISST version:\\t%s\\n\" % self.version\n return info\n \n def freeimage_upload(self, img:str) -> str:\n \"\"\"\n Upload an image to freeimage.host\n\n Args:\n img (str): The path to the image\n \"\"\"\n key = \"6d207e02198a847aa98d0a2a901485a5\"\n\n response = requests.post(\n url=\"https://freeimage.host/api/1/upload\",\n data={\"key\": key},\n files={\"source\": img},\n )\n if not response.ok:\n raise Exception(\"Error uploading image\", response.json())\n\n return response.json()[\"image\"][\"url\"]\n \n def terminate_thread(self, thread:threading.Thread) -> None:\n \"\"\"\n Terminate a thread\n\n Args:\n thread (threading.Thread): The thread to terminate\n \"\"\"\n if not thread.is_alive():\n return\n\n exc = ctypes.py_object(SystemExit)\n res = ctypes.pythonapi.PyThreadState_SetAsyncExc(\n ctypes.c_long(thread.ident), exc)\n if res == 0:\n raise ValueError(\"nonexistent thread id\")\n elif res > 1:\n # \"\"\"if it returns a number greater than one, you're in trouble,\n # and you should call it again with exc=NULL to revert the effect\"\"\"\n ctypes.pythonapi.PyThreadState_SetAsyncExc(thread.ident, None)\n raise SystemError(\"PyThreadState_SetAsyncExc failed\")"
},
{
"path": "MISST/MISSTlogger.py",
"content": "import datetime\r\nimport logging\r\nimport sys\r\n\r\n\r\nclass MISSTlogger:\r\n \"\"\"\r\n MISSTlogger class\r\n \"\"\"\r\n def __init__(self) -> None:\r\n \"\"\"\r\n Initialize the logger\r\n \"\"\"\r\n loggerName = \"MISST\"\r\n logFormatter = logging.Formatter(fmt=\" %(name)s :: %(levelname)-8s :: %(message)s\")\r\n self.logger = logging.getLogger(loggerName)\r\n\r\n logging.basicConfig(\r\n filename=\"MISST.log\",\r\n filemode=\"a\",\r\n format=\" %(name)s :: %(levelname)-8s :: %(message)s\",\r\n level=logging.INFO,\r\n )\r\n\r\n consoleHandler = logging.StreamHandler()\r\n consoleHandler.setFormatter(logFormatter)\r\n\r\n self.logger.addHandler(consoleHandler)\r\n self.logger.info(f'Logger initialized ({str(datetime.datetime.now()).split(\".\")[0]})')\r\n\r\n sys.excepthook = self.handler\r\n \r\n def handler(self, type, value, tb) -> None:\r\n self.logger.exception(\"Uncaught exception: {0}\".format(str(value)))"
},
{
"path": "MISST/MISSTplayer.py",
"content": "import base64\nimport io\nimport threading\nfrom typing import List\n\nimport music_tag # for exporting song metadata\nimport numpy as np\nimport pyaudio\nimport soundfile as sf\nfrom MISSTsettings import MISSTsettings\nfrom scipy.signal import butter, fftconvolve, filtfilt\n\n\nclass MISSTplayer:\n \"\"\"\n MISSTplayer class\n \"\"\"\n def __init__(self, files:list, volumes:list) -> None:\n \"\"\"\n Initialize the player\n\n Args:\n files (list): List of file paths\n volumes (list): List of volume values\n \"\"\"\n self.files = files\n self.p = pyaudio.PyAudio()\n self.streams = []\n self.paused = False\n self.positions = [0] * len(files)\n self.volumes = volumes\n self.chunk_size = 1024\n self.effects = False\n self.settings = MISSTsettings()\n self.bands = lambda: [self.settings.getSetting(f\"eq_{n}\") for n in range(1,10)] # lambda so the player can access the latest information \n self.eq = lambda: True if self.settings.getSetting(\"eq\") == \"true\" else False\n self.center_freqs = [62, 125, 250, 500, 1_000, 2_500, 4_000, 8_000, 16_000] # 62 Hz, 125 Hz, 250 Hz, 500 Hz, 1 KHz, 2.5 KHz, 4 KHz, 8 KHz, 16 KHz\n\n for file in self.files:\n data, self.frame_rate = sf.read(file, dtype='int16')\n self.duration = len(data) / self.frame_rate\n self.channels = data.shape[1]\n stream = self.p.open(format=self.p.get_format_from_width(2),\n channels=self.channels,\n rate=self.frame_rate,\n output=True)\n self.streams.append(stream)\n\n reverb_length = int(self.frame_rate * 0.3)\n self.impulse_response = self.generate_impulse_response(reverb_length, 0.5)\n self.reverb_tails = [np.zeros(self.chunk_size, dtype=np.float32) for _ in self.streams]\n\n def play(self) -> None:\n \"\"\"\n Play the audio\n \"\"\"\n self.paused = False\n while not self.paused:\n for i, stream in enumerate(self.streams):\n data = self.get_data(i)\n if data:\n stream.write(data)\n else:\n break\n \n def get_data(self, stream_index:int) -> bytes:\n \"\"\"\n Get the audio data\n\n Args:\n stream_index (int): Index of the stream\n \"\"\"\n data, _ = sf.read(self.files[stream_index], dtype='int16', start=self.positions[stream_index], frames=self.chunk_size)\n if len(data) > 0:\n self.positions[stream_index] += len(data)\n data = self.adjust_volume(data, self.volumes[stream_index])\n if self.effects == True:\n try:\n data = self.apply_effects(data, float(self.settings.getSetting(\"speed\")),\n float(self.settings.getSetting(\"reverb\")),\n float(self.settings.getSetting(\"pitch\")),\n stream_index)\n except:\n # json error\n pass\n try:\n if self.eq() == True:\n data = self.apply_eq(data) # apply eq last so it can be applied to nightcore data as well\n except:\n # json error\n pass\n return data\n \n def get_position(self, stream_index:int) -> float:\n \"\"\"\n Get the position of the audio\n\n Args:\n stream_index (int): Index of the stream\n \"\"\"\n return self.positions[stream_index] / float(self.frame_rate)\n \n def adjust_volume(self, data:bytes, volume:float) -> bytes:\n \"\"\"\n Adjust the volume of the audio\n\n Args:\n data (bytes): Audio data\n volume (float): Volume value\n \"\"\"\n data = bytearray(data)\n for i in range(0, len(data), 2):\n sample = int.from_bytes(data[i:i+2], byteorder='little', signed=True)\n sample = int(sample * volume)\n data[i:i+2] = sample.to_bytes(2, byteorder='little', signed=True)\n return bytes(data)\n \n def apply_effects(self, data: bytes, speed_factor: float, reverb_factor: float, pitch_shift: float, stream_index:int) -> bytes:\n \"\"\"\n Modify the audio\n\n Args:\n data (bytes): Audio data\n speed_factor (float): Speed value between 0.5 and 2.0 (0.5 = half speed, 2.0 = double speed)\n reverb_factor (float): Reverb value between 0.0 and 1.0 (0.0 = no reverb, 1.0 = full reverb)\n delay_time (float): Delay time in seconds\n stream_index (int): Index of the stream\n \"\"\"\n # Convert bytes to numpy array\n samples = np.frombuffer(data, dtype=np.int16)\n samples = samples.astype(np.float32)\n samples = samples.reshape((len(samples) // 2, 2)).T\n samples = samples.mean(axis=0)\n\n # Modify speed\n samples = self.modify_speed(samples, speed_factor)\n\n # Modify pitch\n samples = self.modify_pitch(samples, pitch_shift)\n\n # Add reverb\n samples = self.apply_reverb(samples, reverb_factor, stream_index)\n\n # Convert back to bytes\n samples = samples.astype(np.int16)\n samples = np.repeat(samples, 2)\n\n return samples.tobytes()\n \n def modify_speed(self, samples:np.ndarray, speed_factor:float) -> bytes:\n \"\"\"\n Modify the speed of the audio\n\n Args:\n samples (np.ndarray): Audio data\n speed_factor (float): Speed value between 0.5 and 2.0 (0.5 = half speed, 2.0 = double speed)\n \"\"\"\n if speed_factor == 1.0:\n return samples\n num_samples = len(samples)\n new_num_samples = int(num_samples / speed_factor)\n\n # Resample the audio to the new length\n resampled = np.interp(\n np.linspace(0, num_samples, new_num_samples, endpoint=False),\n np.arange(num_samples),\n samples\n )\n\n return resampled\n\n def apply_antialiasing_filter(self, samples: np.ndarray, cutoff_freq: float, frame_rate: int) -> np.ndarray:\n \"\"\"\n Apply an anti-aliasing filter to the audio\n\n Args:\n samples (np.ndarray): Audio data\n cutoff_freq (float): Cutoff frequency in Hz\n frame_rate (int): Frame rate in Hz\n \"\"\"\n # Create a low-pass Butterworth filter\n nyquist = 0.5 * frame_rate\n normal_cutoff = cutoff_freq / nyquist\n b, a = butter(8, normal_cutoff, btype='low', analog=False)\n \n # Apply the filter to the samples\n filtered_samples = filtfilt(b, a, samples, axis=0)\n return filtered_samples\n\n def modify_pitch(self, samples:np.ndarray, pitch_shift:float) -> bytes:\n \"\"\"\n Modify the pitch of the audio\n\n Args:\n samples (np.ndarray): Audio data\n pitch_shift (float): Pitch value between -12.0 and 12.0 (-12.0 = one octave down, 12.0 = one octave up)\n \"\"\"\n if pitch_shift == 0:\n return samples\n\n pitch_shift *= 12 # Convert pitch shift from octaves to semitones\n samples = samples.astype(np.float32) / np.iinfo(np.int16).max # Convert samples to float32 between -1.0 and 1.0\n\n # Continue with the pitch shift as before\n shift_ratio = 2 ** (pitch_shift / 12) # Shift by half-steps (semitones)\n time_axis = np.arange(samples.shape[0]) / self.frame_rate\n shifted_audio = np.interp(time_axis * shift_ratio, time_axis, samples)\n\n # Apply an anti-aliasing filter to attenuate high frequencies\n cutoff_freq = 0.9 * self.frame_rate / 2.0 # You can adjust the cutoff frequency as needed\n shifted_audio = self.apply_antialiasing_filter(shifted_audio, cutoff_freq, self.frame_rate)\n\n shifted_audio = np.clip(shifted_audio, -1.0, 1.0) # Clip samples to prevent overflow\n \n # Convert samples back to 16-bit integer range\n return shifted_audio * np.iinfo(np.int16).max\n \n def generate_impulse_response(self, length:int, decay_factor:float) -> np.ndarray:\n \"\"\"\n Generate impulse response\n\n Args:\n length (int): Length of the impulse response in samples (44100 samples = 1 second)\n decay_factor (float): Decay factor between 0.0 and 1.0 (0.0 = no decay, 1.0 = full decay)\n \"\"\"\n impulse_response = np.zeros(length, dtype=np.float32)\n impulse_response[0] = 1.0\n\n # Generate decaying exponential tail\n for i in range(1, length):\n impulse_response[i] = impulse_response[i - 1] * decay_factor\n\n # Add some random noise for early reflections\n impulse_response += np.random.normal(0, 0.05, length)\n return impulse_response\n\n def apply_reverb(self, samples: np.ndarray, reverb_factor: float, stream_index: int) -> bytes:\n \"\"\"\n Add reverb to the audio\n\n Args:\n samples (np.ndarray): Audio data\n reverb_factor (float): Reverb value between 0.0 and 1.0 (0.0 = no reverb, 1.0 = full reverb)\n stream_index (int): Index of the stream\n \"\"\"\n if reverb_factor == 0:\n return samples\n\n samples = samples.astype(np.float32) / np.iinfo(np.int16).max # Convert samples to float32 between -1.0 and 1.0\n\n # Apply reverb impulse response to the current chunk\n reverb_audio = fftconvolve(samples, self.impulse_response, mode='full')\n\n # Concatenate previous reverb tail with the current reverb tail\n reverb_audio[:len(self.reverb_tails[stream_index])] += self.reverb_tails[stream_index]\n\n # Store the current reverb tail for the next chunk\n self.reverb_tails[stream_index] = reverb_audio[len(samples):]\n\n mixed_audio = samples * (1 - reverb_factor) + reverb_audio[:len(samples)] * reverb_factor # Mix original and reverb audio\n mixed_audio = np.clip(mixed_audio, -1.0, 1.0) # Clip samples to prevent overflow\n\n # Convert samples back to 16-bit integer range\n return mixed_audio * np.iinfo(np.int16).max\n\n def apply_eq(self, data:bytes) -> bytes:\n \"\"\"\n Apply the equalizer effect to the audio\n\n Args:\n data (bytes): Audio data\n \"\"\"\n # Convert data to float32\n audio_data = np.frombuffer(data, dtype=np.int16)\n\n try:\n gains = np.array(self.bands(), dtype=np.float32) # Gain values in dB\n except:\n gains = np.array([0] * 9, dtype=np.float32)\n\n # Number of frequency bands\n num_bands = len(gains)\n\n # Generate frequency range (20Hz to 20kHz)\n frequencies = np.fft.rfftfreq(len(audio_data), d=1 / 44100)\n\n # Create equalizer response\n response = np.zeros_like(frequencies)\n\n # Apply gain to each frequency band\n for i in range(num_bands):\n lower_cutoff = self.center_freqs[i] / np.sqrt(2) # Lower cutoff frequency\n upper_cutoff = self.center_freqs[i] * np.sqrt(2) # Upper cutoff frequency\n\n # Find indices within the frequency range\n indices = np.where((frequencies >= lower_cutoff) & (frequencies <= upper_cutoff))[0]\n\n # Apply gain to the corresponding indices\n response[indices] += gains[i]\n\n # Convert response to linear scale and normalize\n response_linear = 10 ** (response / 20)\n\n # Apply equalization to audio data\n audio_fft = np.fft.rfft(audio_data)\n audio_fft *= response_linear[:len(audio_fft)]\n equalized_audio = np.fft.irfft(audio_fft).astype(np.int16)\n\n # Clip the audio to avoid distortion\n equalized_audio = np.clip(equalized_audio, -32768, 32767)\n\n return equalized_audio.tobytes()\n \n def set_effects(self, effects:bool) -> None:\n \"\"\"\n Set the effects of the audio\n\n Args:\n effects (bool): Effects value\n \"\"\"\n self.effects = effects\n\n def set_volume(self, stream_index:int, volume:float) -> None:\n \"\"\"\n Set the volume of the audio\n\n Args:\n stream_index (int): Index of the stream\n volume (float): Volume value\n \"\"\"\n self.volumes[stream_index] = volume\n \n def set_position(self, stream_index:int, position:float) -> None:\n \"\"\"\n Set the position of the audio\n\n Args:\n stream_index (int): Index of the stream\n position (float): Position value\n \"\"\"\n self.positions[stream_index] = position\n\n def save(self, files:List[str], volumes:List[int], filename:str, cover_art:bytes=None) -> None:\n \"\"\"\n Save the audio\n\n Args:\n files (List[str]): List of audio files\n volumes (List[int]): List of volume values\n filename (str): Name of the file\n \"\"\"\n # Load the audio files and get the sample rate\n audio_data = []\n sample_rate = None\n for file in files:\n data, sr = sf.read(file)\n audio_data.append(data)\n if sample_rate is None:\n sample_rate = sr\n\n adjusted_data = []\n for i in range(len(audio_data)):\n adjusted_data.append(audio_data[i] * volumes[i])\n\n overlapped_data = np.sum(adjusted_data, axis=0)\n\n sf.write(filename, overlapped_data, sample_rate)\n if cover_art is not None and cover_art != \"null\":\n byte_data = base64.b64decode(cover_art)\n byte_stream = io.BytesIO(byte_data)\n byte_stream.seek(0)\n f = music_tag.load_file(filename)\n f['artwork'] = byte_stream.read() \n f.save()\n\n def pause(self) -> None:\n \"\"\"\n Pause the audio\n \"\"\"\n self.paused = True\n\n def resume(self) -> None:\n \"\"\"\n Resume the audio\n \"\"\"\n if self.paused:\n self.paused = False\n threading.Thread(target=self.play, daemon=True).start()\n \n def stop(self) -> None:\n \"\"\"\n Stop the audio\n \"\"\"\n for i, stream in enumerate(self.streams):\n stream.stop_stream()\n stream.close()\n self.p.terminate()\n\n def change_files(self, new_files:list, volumes:list) -> None:\n \"\"\"\n Change the files of the audio\n\n Args:\n new_files (list): List of new files\n volumes (list): List of volumes\n \"\"\"\n self.paused = True\n self.volumes = volumes\n\n for i, stream in enumerate(self.streams):\n stream.stop_stream()\n stream.close()\n\n self.streams.clear()\n self.files = new_files\n self.positions = [0] * len(new_files)\n\n for file in self.files:\n data, self.frame_rate = sf.read(file, dtype='int16')\n self.duration = len(data) / self.frame_rate\n self.channels = data.shape[1]\n stream = self.p.open(format=self.p.get_format_from_width(2),\n channels=self.channels,\n rate=self.frame_rate,\n output=True)\n self.streams.append(stream)\n\n self.paused = False\n threading.Thread(target=self.play, daemon=True).start()"
},
{
"path": "MISST/MISSTpreprocess.py",
"content": "import concurrent.futures\nimport logging\nimport os\nimport pathlib\nimport time\nimport traceback\nimport wave\n\nimport julius\nimport numpy as np\nimport soundfile\nimport soundfile as sf\nimport torch\nfrom demucs.apply import BagOfModels, apply_model\nfrom demucs.pretrained import get_model\nfrom MISSThelpers import MISSTconsole\n\n# Modified functions from https://github.com/facebookresearch/demucs, \n# https://pytorch.org/audio/main/tutorials/hybrid_demucs_tutorial.html \n# and https://github.com/CarlGao4/Demucs-Gui\n\nclass MISSTpreprocess():\n \"\"\"\n MISSTpreprocess class\n \"\"\"\n def __init__(self) -> None:\n pass\n\n def LoadModel(self, name:str, repo:str = None, device:str = \"cuda\" if torch.cuda.is_available() else \"cpu\",) -> BagOfModels:\n \"\"\"\n Load the model\n\n Args:\n name (str): Name of the model\n repo (str): Repository of the model\n device (str): Device to use\n \"\"\"\n model = get_model(name=name, repo=repo)\n model.to(device)\n model.eval()\n return model\n\n def GetData(self, model:BagOfModels) -> dict:\n \"\"\"\n Get the data from the model\n\n Args:\n model (demucs.pretrained.BagOfModels): Model to get the data from\n \"\"\"\n res = {}\n res[\"channels\"] = model.audio_channels\n res[\"samplerate\"] = model.samplerate\n if isinstance(model, BagOfModels):\n res[\"models\"] = len(model.models)\n else:\n res[\"models\"] = 1\n res[\"sources\"] = model.sources\n return res\n\n def Apply(self, model:BagOfModels, wav:np.ndarray, shifts:int = 1) -> dict:\n \"\"\"\n Apply the model to the audio\n\n Args:\n model (demucs.pretrained.BagOfModels): Model to apply\n wav (numpy.ndarray): Audio data\n shifts (int): Number of shifts\n \"\"\"\n audio = wav\n ref = audio.mean(0)\n audio -= ref.mean()\n audio /= ref.std()\n sources = apply_model(model, audio[None], shifts=shifts, split=False, overlap=0.25, progress=False)[0]\n sources *= ref.std()\n sources += ref.mean()\n return dict(zip(model.sources, sources))\n\n def convert_audio_channels(self, wav:np.ndarray, channels:int = 2) -> np.ndarray:\n \"\"\"\n Convert the audio channels\n\n Args:\n wav (numpy.ndarray): Audio data\n channels (int): Number of channels\n \"\"\"\n *shape, src_channels, length = wav.shape\n if src_channels == channels:\n pass\n elif channels == 1:\n wav = wav.mean(dim=-2, keepdim=True)\n elif src_channels == 1:\n wav = wav.expand(*shape, channels, length)\n elif src_channels >= channels:\n wav = wav[..., :channels, :]\n else:\n raise Exception(\"Error changing audio dims\")\n return wav\n\n def write_wav(self, wav:np.ndarray, filename:str, samplerate:int) -> None:\n \"\"\"\n Write the audio to a WAV file\n\n Args:\n wav (numpy.ndarray): Audio data\n filename (str): Filename\n samplerate (int): Samplerate\n \"\"\"\n if wav.dtype.is_floating_point:\n wav = (wav.clamp_(-1, 1) * (2**15 - 1)).short()\n with wave.open(filename, \"wb\") as f:\n f.setnchannels(wav.shape[1])\n f.setsampwidth(2)\n f.setframerate(samplerate)\n f.writeframes(bytearray(wav.numpy()))\n\n def compress_wav_to_flac(self, wav_file:str, flac_file:str) -> None:\n \"\"\"\n Compress the WAV file to a FLAC file\n\n Args:\n wav_file (str): WAV filename\n flac_file (str): FLAC filename\n \"\"\"\n # Read the WAV file\n data, samplerate = sf.read(wav_file)\n\n # Write the FLAC file and delete the WAV file\n sf.write(flac_file, data, samplerate, format='FLAC')\n os.remove(wav_file)\n\n def convert_audio(self, wav:np.ndarray, from_samplerate:int, to_samplerate:int, channels:int) -> np.ndarray:\n \"\"\"\n Convert the audio\n\n Args:\n wav (numpy.ndarray): Audio data\n from_samplerate (int): From samplerate\n to_samplerate (int): To samplerate\n channels (int): Number of channels\n \"\"\"\n wav = self.convert_audio_channels(wav, channels)\n return julius.resample_frac(wav, from_samplerate, to_samplerate)\n\n def load_audio(self, fn:str, sr:int) -> np.ndarray:\n \"\"\"\n Load the audio\n\n Args:\n fn (str): Filename\n sr (int): Samplerate\n \"\"\"\n audio, raw_sr = soundfile.read(fn, dtype=\"float32\")\n if len(audio.shape) == 1:\n audio = np.atleast_2d(audio).transpose()\n converted = self.convert_audio(torch.from_numpy(audio.transpose()), raw_sr, sr, 2)\n return converted.numpy()\n \n def apply_fade_in_out(self, input_file:str, output_file:str, fade_duration:float) -> None:\n \"\"\"\n Apply fade in and out to the audio\n\n Args:\n input_file (str): Input filename\n output_file (str): Output filename\n fade_duration (float): Fade duration\n \"\"\"\n # Read the audio file\n audio_data, sample_rate = sf.read(input_file)\n \n # Calculate the number of samples for the fade duration\n fade_samples = int(fade_duration * sample_rate)\n \n # Apply fade-in\n fade_in_curve = np.linspace(0.0, 1.0, fade_samples)\n audio_data[:fade_samples] *= fade_in_curve[:, np.newaxis]\n \n # Apply fade-out\n fade_out_curve = np.linspace(1.0, 0.0, fade_samples)\n audio_data[-fade_samples:] *= fade_out_curve[:, np.newaxis]\n \n # Save the modified audio to a new file\n sf.write(output_file, audio_data, sample_rate)\n\n def process(\n self, \n model:BagOfModels, \n infile:str, \n write:bool = True, \n outpath:pathlib.Path = pathlib.Path(\"\"), \n split:float = 5.0, \n overlap:float = 0.25, \n sample_rate:int = 44100, \n device:str = \"cuda\" if torch.cuda.is_available() else \"cpu\", \n logger:logging.Logger = logging.getLogger(\"MISST\"),\n console:MISSTconsole = None,\n ) -> None:\n \"\"\"\n Process the audio\n\n Args:\n model (demucs.pretrained.BagOfModels): Model to apply\n infile (str): Input filename\n write (bool): Write the output\n outpath (pathlib.Path): Output path\n split (float): Split (seconds)\n overlap (float): Overlap\n sample_rate (int): Sample rate\n device (str): Device\n logger (logging.Logger): Logger\n console (MISSTConsole): Console\n \"\"\"\n # Start the timer\n start_time = time.time()\n split = int(split * sample_rate)\n overlap = int(overlap * split)\n logger.info(\"Loading file\")\n audio = self.load_audio(str(infile), sample_rate)\n logger.info(f\"Loaded audio of shape {audio.shape}\")\n orig_len = audio.shape[1]\n n = int(np.ceil((orig_len - overlap) / (split - overlap)))\n audio = np.pad(audio, [(0, 0), (0, n * (split - overlap) + overlap - orig_len)])\n logger.info(\"Loading model to device %s\" % device)\n model.to(device)\n stems = self.GetData(model)[\"sources\"]\n new_audio = np.zeros((len(stems), 2, audio.shape[1]))\n total = np.zeros(audio.shape[1])\n logger.info(\"Total splits of '%s' : %d\" % (str(infile), n))\n\n def separation(i):\n logger.info(\"Separation %d/%d\" % (i + 1, n))\n console.editLine(f\"MISST Preprocessor\\nCopyright (C) @MISST App.\\n\\nMISST> Split {i + 1}/{n} ({((i + 1)/n) * 100:.1f}%)\", 0)\n l = i * (split - overlap)\n r = l + split\n result = self.Apply(model, torch.from_numpy(audio[:, l:r]).to(device))\n for (j, stem) in enumerate(stems):\n new_audio[j, :, l:r] += result[stem].cpu().numpy()\n total[l:r] += 1\n\n workers = 1 if device == \"cuda\" else (os.cpu_count() // 2) # CPU parallelization on half the cores available, CUDA kernels are already parallelized\n\n with concurrent.futures.ThreadPoolExecutor(max_workers=workers) as executor:\n futures = [executor.submit(separation, i) for i in range(n)]\n concurrent.futures.wait(futures)\n\n if write:\n console.endUpdate()\n console.addLine(\"\\nMISST> Preprocessed.\")\n console.update(\"\\nMISST> Writing to file\")\n outpath.mkdir(exist_ok=True)\n for i in range(len(stems)):\n if np.all(total == 0):\n stem = np.zeros_like(new_audio[i])\n else:\n stem = (new_audio[i] / total)[:, :orig_len]\n self.write_wav(torch.from_numpy(stem.transpose()), str(outpath / f\"{stems[i]}.wav\"), sample_rate)\n self.compress_wav_to_flac(str(outpath / f\"{stems[i]}.wav\"), str(outpath / f\"{stems[i]}.flac\"))\n self.apply_fade_in_out(str(outpath / f\"{stems[i]}.flac\"), str(outpath / f\"{stems[i]}.flac\"), 3.5)\n logger.info(\"Wrote to %s\" % str(outpath))\n logger.info(\"Done in %.2f seconds\" % (time.time() - start_time))\n console.endUpdate()\n else:\n pass\n del model # Free up memory\n\n def preprocess(self, file:str, outDir:str, chosen_model:str, device:str = \"cuda\") -> None:\n \"\"\"\n Preprocess the audio\n\n Args:\n file (str): Input filename\n outDir (str): Output directory\n device (str): Device\n \"\"\"\n self.logger.info(f\"Preprocessing {file}...\")\n console = MISSTconsole(self.preprocess_terminal_text, \"MISST Preprocessor\\nCopyright (C) @MISST App.\\n\")\n try:\n savename = os.path.basename(file).replace('.mp3', '').replace('.wav', '').replace('.flac', '')\n console.update(\"\\nMISST> Loading model\")\n processor = MISSTpreprocess()\n model = processor.LoadModel(name=chosen_model, repo=pathlib.Path(\"Pretrained\"))\n console.endUpdate()\n console.addLine(\"\\nMISST> Model loaded.\")\n console.update(\"\\nMISST> Preprocessing\")\n processor.process(model, infile=pathlib.Path(file), outpath=pathlib.Path(f\"{outDir}/{savename}\"), device=device, console=console)\n del model # Free up memory\n except Exception as e:\n self.logger.error(e)\n self.logger.error(traceback.format_exc())\n console.addLine(\"\\nMISST> Error.\")\n pass\n console.addLine(\"\\nMISST> Done.\")\n self.import_file_button.configure(state='normal')\n self.import_button.configure(state='normal')\n return"
},
{
"path": "MISST/MISSTsettings.py",
"content": "import json\nimport os\nimport shutil\n\nimport torch\n\n\nclass MISSTsettings():\n \"\"\"\n Class for handling the settings of MISST\n \"\"\"\n def __init__(self) -> None:\n \"\"\"\n Initialize the settings\n \"\"\"\n # Check if the config file exists, if not create it\n # Path: MISST\\MISSTsettings.py\n if not os.path.isfile(\"config.json\"):\n self.createSettings()\n return\n \n def getSetting(self, setting:str) -> str:\n \"\"\"\n Get the setting from the saved config file\n\n Args:\n setting (str): The setting to be retrieved\n \"\"\"\n # Get the setting from the saved config file\n # Path: MISST\\MISSTsettings.py\n # setting: The setting to be retrieved\n with open(\"config.json\", \"r\") as f:\n data = json.load(f)\n return data[setting]\n \n def setSetting(self, setting:str, value:str) -> None:\n \"\"\"\n Set the setting in the saved config file\n\n Args:\n setting (str): The setting to be set\n value (str): The value to be set\n \"\"\"\n # Set the setting in the saved config file\n # Path: MISST\\MISSTsettings.py\n # setting: The setting to be set\n # value: The value to be set\n with open(\"config.json\", \"r\") as f:\n data = json.load(f)\n data[setting] = value\n with open(\"config.json\", \"w\") as f:\n json.dump(data, f, indent=4)\n\n def applyThemeSettings(self, themeFile:str, baseTheme:str) -> None:\n \"\"\"\n Apply the chosen colorways to the theme file\n\n Args:\n themeFile (str): The theme file to be modified\n baseTheme (str): The base theme file\n \"\"\"\n # Apply the chosen colorways to the theme file\n # Path: MISST\\MISSTsettings.py\n # themeFile: The theme file to be modified\n # colorways: The colorways to be applied\n with open(baseTheme, \"r\") as f:\n data = f.read()\n colorways = [\"defaultLightColor\", \"defaultDarkColor\", \"defaultLightHoverColor\", \"defaultDarkHoverColor\", \"defaultLightDarker\", \"defaultDarkDarker\"]\n for colorway in colorways:\n data = data.replace(colorway, self.getSetting(colorway.replace('default', 'chosen')))\n with open(themeFile, \"w\") as f:\n f.write(data)\n\n def resetDefaultTheme(self, themeFile:str, baseTheme:str) -> None:\n \"\"\"\n Reset the theme file to the default theme\n\n Args:\n themeFile (str): The theme file to be modified\n baseTheme (str): The base theme file\n \"\"\"\n # Reset the theme file to the default theme\n # Path: MISST\\MISSTsettings.py\n # themeFile: The theme file to be modified\n # colorways: The colorways to be applied\n with open(baseTheme, \"r\") as f:\n data = f.read()\n colorways = [\"defaultLightColor\", \"defaultDarkColor\", \"defaultLightHoverColor\", \"defaultDarkHoverColor\", \"defaultLightDarker\", \"defaultDarkDarker\"]\n for colorway in colorways:\n self.setSetting(colorway.replace('default', 'chosen'), self.getSetting(colorway))\n data = data.replace(colorway, self.getSetting(colorway))\n with open(themeFile, \"w\") as f:\n f.write(data)\n\n def createSettings(self) -> None:\n \"\"\"\n Create the config file\n \"\"\"\n # Creates a new config file in the apps directory with all the default settings.\n shutil.copy(\"Assets/config_base.json\", \"config.json\")\n self.setSetting(\"accelerate_on_gpu\", \"true\" if torch.cuda.is_available() else \"false\") #Automatically set GPU acceleration to true if available.\n\n\nclass MISSTconfig:\n \"\"\"\n Class for handling the metadata of the current song\n \"\"\"\n def __init__(self, configPath:str) -> None:\n \"\"\"\n Initialize the config for the current song\n\n Args:\n configPath (str): The path to the config file\n \"\"\"\n # Check if the config file exists, if not create it\n # Path: MISST\\MISSTsettings.py\n if not os.path.isfile(f\"{configPath}/.misst\"):\n self.createConfig(configPath)\n return\n \n def getConfig(self, configPath:str) -> str:\n \"\"\"\n Get the setting from the saved config file\n\n Args:\n configPath (str): The path to the config file\n \"\"\"\n # Get the setting from the saved config file\n # Path: MISST\\MISSTsettings.py\n # setting: The setting to be retrieved\n with open(f\"{configPath}/.misst\", \"r\") as f:\n data = json.load(f)\n return data\n \n def setConfig(self, configPath:str, setting:str, value:str) -> None:\n \"\"\"\n Set the setting in the saved config file\n\n Args:\n configPath (str): The path to the config file\n setting (str): The setting to be set\n value (str): The value to be set\n \"\"\"\n # Set the setting in the saved config file\n # Path: MISST\\MISSTsettings.py\n # setting: The setting to be set\n # value: The value to be set\n with open(f\"{configPath}/.misst\", \"r\") as f:\n data = json.load(f)\n data[setting] = value\n with open(f\"{configPath}/.misst\", \"w\") as f:\n json.dump(data, f, indent=4)\n\n def createConfig(self, configPath:str) -> None:\n \"\"\"\n Create the config file\n\n Args:\n configPath (str): The path to the config file\n \"\"\"\n # Creates a new config file in the apps directory with all the default settings.\n with open(f\"{configPath}/.misst\", \"w\") as f:\n json.dump({\"image_url\": \"null\", \"image_raw\": \"null\", \"lyrics\": \"null\"}, f, indent=4)\n f.close()"
},
{
"path": "MISST/Pretrained/files.txt",
"content": "# MDX Models\nroot: mdx_final/\n0d19c1c6-0f06f20e.th\n5d2d6c55-db83574e.th\n7d865c68-3d5dd56b.th\n7ecf8ec1-70f50cc9.th\na1d90b5c-ae9d2452.th\nc511e2ab-fe698775.th\ncfa93e08-61801ae1.th\ne51eebcc-c1b80bdd.th\n6b9c2ca1-3fd82607.th\nb72baf4e-8778635e.th\n42e558d4-196e0e1b.th\n305bc58f-18378783.th\n14fc6a69-a89dd0ee.th\n464b36d7-e5a9386e.th\n7fd6ef75-a905dd85.th\n83fc094f-4a16d450.th\n1ef250f1-592467ce.th\n902315c2-b39ce9c9.th\n9a6b4851-03af0aa6.th\nfa0cb7f9-100d8bf4.th\n# Hybrid Transformer models\nroot: hybrid_transformer/\n955717e8-8726e21a.th\nf7e0c4bc-ba3fe64a.th\nd12395a8-e57c48e6.th\n92cfc3b6-ef3bcb9c.th\n04573f0d-f3cf25b2.th\n75fc33f5-1941ce65.th"
},
{
"path": "MISST/Pretrained/hdemucs_mmi.yaml",
"content": "models: ['75fc33f5']\nsegment: 44\n"
},
{
"path": "MISST/Pretrained/htdemucs.yaml",
"content": "models: ['955717e8']\n"
},
{
"path": "MISST/Pretrained/htdemucs_ft.yaml",
"content": "models: ['f7e0c4bc', 'd12395a8', '92cfc3b6', '04573f0d']\nweights: [\n [1., 0., 0., 0.],\n [0., 1., 0., 0.],\n [0., 0., 1., 0.],\n [0., 0., 0., 1.],\n]"
},
{
"path": "MISST/Pretrained/mdx.yaml",
"content": "models: ['0d19c1c6', '7ecf8ec1', 'c511e2ab', '7d865c68']\nweights: [\n [1., 1., 0., 0.],\n [0., 1., 0., 0.],\n [1., 0., 1., 1.],\n [1., 0., 1., 1.],\n]\nsegment: 44\n"
},
{
"path": "MISST/Pretrained/mdx_extra.yaml",
"content": "models: ['e51eebcc', 'a1d90b5c', '5d2d6c55', 'cfa93e08']\nsegment: 44"
},
{
"path": "MISST/Pretrained/mdx_extra_q.yaml",
"content": "models: ['83fc094f', '464b36d7', '14fc6a69', '7fd6ef75']\nsegment: 44\n"
},
{
"path": "MISST/Pretrained/mdx_q.yaml",
"content": "models: ['6b9c2ca1', 'b72baf4e', '42e558d4', '305bc58f']\nweights: [\n [1., 1., 0., 0.],\n [0., 1., 0., 0.],\n [1., 0., 1., 1.],\n [1., 0., 1., 1.],\n]\nsegment: 44\n"
},
{
"path": "MISST/Pretrained/repro_mdx_a.yaml",
"content": "models: ['9a6b4851', '1ef250f1', 'fa0cb7f9', '902315c2']\nsegment: 44\n"
},
{
"path": "MISST/Pretrained/repro_mdx_a_hybrid_only.yaml",
"content": "models: ['fa0cb7f9', '902315c2', 'fa0cb7f9', '902315c2']\nsegment: 44\n"
},
{
"path": "MISST/Pretrained/repro_mdx_a_time_only.yaml",
"content": "models: ['9a6b4851', '9a6b4851', '1ef250f1', '1ef250f1']\nsegment: 44\n"
},
{
"path": "MISST/__version__.py",
"content": "__version__ = '3.1.0'"
},
{
"path": "README.md",
"content": "\n\n[](https://github.com/Frikallo/MISST)\n[](https://github.com/Frikallo/MISST/releases/latest) [](https://github.com/Frikallo/MISST/releases/latest) [](https://github.com/Frikallo/MISST/blob/main/LICENSE) [](https://github.com/Frikallo/MISST/graphs/contributors)\n\n
\n\n---\n\n\n| _`MISST` on Windows 11 with Dark mode and 'Blue' theme with 'Steve Lacy's Infrunami' playing_\n\n\n| _`MISST` on Windows 11 with Light mode and 'Blue' theme with 'Frank Ocean's Ivy' playing_\n\n\n| _`MISST` on Windows 11 Showcasing how versatile and personal you can be with MISST!_\n\n\n| _`MISST` on Windows 11 Showcasing how importing audios is as easy as two clicks!_\n\n###\n\nOriginal Repository of MISST : **M**usic/**I**nstrumental **S**tem **S**eparation **T**ool.\n\nThis application uses state-of-the-art [demucs](https://github.com/facebookresearch/demucs) source separation models to extract the 4 core stems from audio files (Bass, Drums, Other Instrumentals and Vocals). But it is not limited to this. MISST acts as a developped music player aswell, fit to enjoy and medal with your audio files as you see fit. MISST even comes prepared to import songs and playlists directly from your music library.\n\nThis project is OpenSource, feel free to use, study and/or send pull request.\n\n## Objectives:\n- [x] Import songs and playlists from your music library\n- [x] Easy to use UI\n- [x] Play your songs and playlists\n- [x] Extract and manipulate the 4 core stems from your audio files as they play\n- [x] Discord rich presence to show off your music to your friends\n- [x] Save your stems as audio files\n- [x] If imported from your music library, view lyrics and metadata just as you would in your old music player\n- [x] Minimal memory usage\n- [x] Customizable themes\n- [x] Additional Efects like nightcore\n- [x] Easy to use equalizer\n- [x] Preprocessing service available on both CPU and GPU\n- [x] Ability to change the pre-trained model used for separation\n- [x] Small save size, comparable to the size of the inputted audio\n- [ ] Docker image (WIP)\n- [ ] Stable on Windows, Linux and MacOS (WIP)\n- [ ] Proper installer/updater (Not a priority)\n\n## Installation\nAs of version 3.1.0, MISST is only available on windows with guaranteed compatibility. \n\nUntil a later release : \n- if you are **not on a windows device** please refer to [Manual Installation](https://github.com/Frikallo/MISST/#manual-installation).\n- if you are **using conda on MacOs** device please refer to [Manual Installation - MacOS](https://github.com/CAprogs/MISST/blob/main/Installation%20Guide%20-%20MacOS.md)\n- Otherwise, refer to the latest [Release](https://github.com/Frikallo/MISST/releases/latest)\n\n## Manual Installation\nThese instructions are for those installing MISST v3.1.0 **manually** only.\n\n1. Download & install Python 3.9 or higher (but no lower than 3.9.0) [here](https://www.python.org/downloads/)\n - **Note:** Ensure the *\"Add Python to PATH\"* box is checked\n2. Download the Source code [here](https://github.com/Frikallo/MISST/releases/latest)\n3. Open the command prompt from the MISST directory and run the following commands, separately - \n\n```\n$ python3 -m venv ./venv\n$ pip install -r requirements.txt\n$ python3 MISSTapp.py\n```\n- **Note:** Install `requirements-minimal.txt` if you don't intend to accelerate preprocessing with your GPU.\n\nFrom here you should be able to open and run the MISSTapp.py file\n\n- CUDA \n\n - CUDA must be installed and configured for the application to process any track with GPU acceleration. You will need to look up instruction on how to configure it on your operating system. Click [here](https://developer.nvidia.com/cuda-downloads) for nvidia's installation guide.\n\n## Benchmark\n\nThe audio processing performance was evaluated using an **NVIDIA GeForce RTX 2070 SUPER** with **8GB VRAM** and an **AMD Ryzen 3700X 8-Core Processor** on the htdemucs pretrained model. This test aimed to compare the processing time of audio on a CPU versus a GPU.\n\nHere are the results of the test:\n\n| Source | Source Length | CPU | GPU | Model |\n|------------------------------------------------------------------|---------------|-----------|-----------| --------- |\n| [Frank Ocean - Ivy](https://www.youtube.com/watch?v=AE005nZeF-A) | 4m 09.00s | 2m 22.16s | 0m 28.04s | htdemucs |\n\n## Demo\n\nhttps://github.com/Frikallo/MISST/assets/88942100/15fb7ce3-9f83-4228-9ab0-f453593be632\n\n[Open in YouTube](https://www.youtube.com/watch?v=XYJm5WW9Zvs)\n\n## License\n\nThe **MISST** code is [GPL-licensed](LICENSE). \n\n- **Please Note:** For all third-party application developers who wish to use MISST or its code, please honor the GPL license by providing credit to MISST and its developer.\n\n## Issue Reporting\n\nPlease be as detailed as possible when posting a new issue. \n\nIf possible, check the \"MISST.log\" file in your install directory for detailed error information that can be provided to me.\n\n## Contributing\n\n- For anyone interested in the ongoing development of **MISST**, please send us a pull request, and I will review it. \n- This project is 100% open-source and free for anyone to use and modify as they wish. \n- I only maintain the development for **MISST** and the models provided. \n\n## More documentation to come...\n"
}
]