1. make_Full_input.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"toc_visible":true,"machine_shape":"hm","gpuType":"T4"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OgyNXgp-TKaO","executionInfo":{"status":"ok","timestamp":1707321656978,"user_tz":-540,"elapsed":19173,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"58290baf-dbcb-4d0b-c0b7-d1e3ea2244fc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"p4dAg7WlSogZ","executionInfo":{"status":"ok","timestamp":1707377867248,"user_tz":-540,"elapsed":6286,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"4a919f7f-718f-456b-c3f4-d5796990bb63"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ffmpeg-python\n","  Downloading ffmpeg_python-0.2.0-py3-none-any.whl (25 kB)\n","Requirement already satisfied: future in /usr/local/lib/python3.10/dist-packages (from ffmpeg-python) (0.18.3)\n","Installing collected packages: ffmpeg-python\n","Successfully installed ffmpeg-python-0.2.0\n"]}],"source":["!pip install ffmpeg-python"]},{"cell_type":"code","source":["!pip install spleeter"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"T6OaYEkuTDJN","executionInfo":{"status":"ok","timestamp":1707377922382,"user_tz":-540,"elapsed":55144,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"e85478ae-9872-4e7e-e534-f6cfe8cedd82"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting spleeter\n","  Downloading spleeter-2.4.0-py3-none-any.whl (49 kB)\n","\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/49.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: ffmpeg-python<0.3.0,>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from spleeter) (0.2.0)\n","Collecting httpx[http2]<0.20.0,>=0.19.0 (from spleeter)\n"," 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keras, flatbuffers, tensorflow-estimator, tensorboard-data-server, protobuf, keras-preprocessing, hyperframe, hpack, h11, gast, click, typer, norbert, httpcore, h2, httpx, google-auth-oauthlib, tensorboard, tensorflow, spleeter\n","  Attempting uninstall: keras\n","    Found existing installation: keras 2.15.0\n","    Uninstalling keras-2.15.0:\n","      Successfully uninstalled keras-2.15.0\n","  Attempting uninstall: flatbuffers\n","    Found existing installation: flatbuffers 23.5.26\n","    Uninstalling flatbuffers-23.5.26:\n","      Successfully uninstalled flatbuffers-23.5.26\n","  Attempting uninstall: tensorflow-estimator\n","    Found existing installation: tensorflow-estimator 2.15.0\n","    Uninstalling tensorflow-estimator-2.15.0:\n","      Successfully uninstalled tensorflow-estimator-2.15.0\n","  Attempting uninstall: tensorboard-data-server\n","    Found existing installation: tensorboard-data-server 0.7.2\n","    Uninstalling tensorboard-data-server-0.7.2:\n","      Successfully uninstalled tensorboard-data-server-0.7.2\n","  Attempting uninstall: protobuf\n","    Found existing installation: protobuf 3.20.3\n","    Uninstalling protobuf-3.20.3:\n","      Successfully uninstalled protobuf-3.20.3\n","  Attempting uninstall: gast\n","    Found existing installation: gast 0.5.4\n","    Uninstalling gast-0.5.4:\n","      Successfully uninstalled gast-0.5.4\n","  Attempting uninstall: click\n","    Found existing installation: click 8.1.7\n","    Uninstalling click-8.1.7:\n","      Successfully uninstalled click-8.1.7\n","  Attempting uninstall: typer\n","    Found existing installation: typer 0.9.0\n","    Uninstalling typer-0.9.0:\n","      Successfully uninstalled typer-0.9.0\n","  Attempting uninstall: google-auth-oauthlib\n","    Found existing installation: google-auth-oauthlib 1.2.0\n","    Uninstalling google-auth-oauthlib-1.2.0:\n","      Successfully uninstalled google-auth-oauthlib-1.2.0\n","  Attempting uninstall: tensorboard\n","    Found existing installation: tensorboard 2.15.1\n","    Uninstalling tensorboard-2.15.1:\n","      Successfully uninstalled tensorboard-2.15.1\n","  Attempting uninstall: tensorflow\n","    Found existing installation: tensorflow 2.15.0\n","    Uninstalling tensorflow-2.15.0:\n","      Successfully uninstalled tensorflow-2.15.0\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","lida 0.0.10 requires fastapi, which is not installed.\n","lida 0.0.10 requires kaleido, which is not installed.\n","lida 0.0.10 requires python-multipart, which is not installed.\n","lida 0.0.10 requires uvicorn, which is not installed.\n","llmx 0.0.15a0 requires cohere, which is not installed.\n","llmx 0.0.15a0 requires openai, which is not installed.\n","llmx 0.0.15a0 requires tiktoken, which is not installed.\n","flask 2.2.5 requires click>=8.0, but you have click 7.1.2 which is incompatible.\n","dask 2023.8.1 requires click>=8.0, but you have click 7.1.2 which is incompatible.\n","distributed 2023.8.1 requires click>=8.0, but you have click 7.1.2 which is incompatible.\n","fiona 1.9.5 requires click~=8.0, but you have click 7.1.2 which is incompatible.\n","pandas-gbq 0.19.2 requires google-auth-oauthlib>=0.7.0, but you have google-auth-oauthlib 0.4.6 which is incompatible.\n","pip-tools 6.13.0 requires click>=8, but you have click 7.1.2 which is incompatible.\n","tensorflow-datasets 4.9.4 requires protobuf>=3.20, but you have protobuf 3.19.6 which is incompatible.\n","tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 3.19.6 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed click-7.1.2 flatbuffers-1.12 gast-0.4.0 google-auth-oauthlib-0.4.6 h11-0.12.0 h2-4.1.0 hpack-4.0.0 httpcore-0.13.7 httpx-0.19.0 hyperframe-6.0.1 keras-2.9.0 keras-preprocessing-1.1.2 norbert-0.2.1 protobuf-3.19.6 rfc3986-1.5.0 spleeter-2.4.0 tensorboard-2.9.1 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 tensorflow-2.9.3 tensorflow-estimator-2.9.0 typer-0.3.2\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["google"]}}},"metadata":{}}]},{"cell_type":"code","source":["!pip install pydub"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-wLLJ8xwTFTw","executionInfo":{"status":"ok","timestamp":1707377949982,"user_tz":-540,"elapsed":6209,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"09196b9e-c56e-4776-a6f7-813ab710a8f7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting pydub\n","  Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n","Installing collected packages: pydub\n","Successfully installed pydub-0.25.1\n"]}]},{"cell_type":"code","source":["from spleeter.separator import Separator\n","import pandas as pd\n","import numpy as np\n","import os, sys\n","from pydub import AudioSegment, silence\n","import librosa"],"metadata":{"id":"lphw2KsOTGeq"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### 음원 처리 방식 변경"],"metadata":{"id":"MmYAVjEZ6g6Q"}},{"cell_type":"code","source":["import matplotlib.pyplot as plt"],"metadata":{"id":"7pJqbovGylgg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import librosa\n","audio, sr = librosa.load(cur_dir+file_name+'_vocals.mp3', sr=22050)\n","plt.plot(audio)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":466},"id":"qSUa2-G5xCx5","executionInfo":{"status":"ok","timestamp":1707330509201,"user_tz":-540,"elapsed":3163,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"6b8eadd2-e2cc-4eaf-e383-82d81f333469"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7f3ec171d7e0>]"]},"metadata":{},"execution_count":94},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["term = int(sr/8)\n","Amplitude_ABS = np.abs(audio)\n","Amplitude_ABS_mean = []\n","\n","for t in range(0, len(Amplitude_ABS), term):\n","    Amplitude_ABS_mean.append(Amplitude_ABS[t:t + term].mean())\n","\n","Amplitude_Distance = []\n","for i in range(0, len(Amplitude_ABS_mean) - 1) :\n","    distance = 1 + ((Amplitude_ABS_mean[i+1] - Amplitude_ABS_mean[i]) ** 2)\n","    Amplitude_Distance.append(distance ** 0.5)\n","\n","Amplitude_Distance_array = np.array(Amplitude_Distance)\n","threshold = Amplitude_Distance_array.mean()\n","print(\"Average of Distance1 = \", threshold)\n","\n","valid_index = []\n","for i in range(0, len(Amplitude_Distance)) :\n","    if Amplitude_Distance[i] > threshold :\n","        valid_index.append(i)"],"metadata":{"id":"Ah641FVh-tVi"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["plt.plot(Amplitude_ABS_mean)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"OpKjZUnJy6CW","executionInfo":{"status":"ok","timestamp":1707330524704,"user_tz":-540,"elapsed":514,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"1294abb9-c8da-481b-e452-f19fdcbd275f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7f3ec18ad5a0>]"]},"metadata":{},"execution_count":97},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["plt.plot(Amplitude_Distance)\n","plt.axhline(y=threshold, color='r', linewidth=1)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"id":"-78_Q30jy8B3","executionInfo":{"status":"ok","timestamp":1707330525774,"user_tz":-540,"elapsed":626,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"17349b5f-73ec-40f8-de57-ad6ac3b8ef66"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["startP = valid_index[0] * term\n","endP = valid_index[len(valid_index) - 1] * term\n","print(\"Start Point = \", startP)\n","print(\"End Point = \", endP)\n","print(\"delay :\", startP/22050, \"sec\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gETNskqdzgYa","executionInfo":{"status":"ok","timestamp":1707330534624,"user_tz":-540,"elapsed":330,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"f8fecc09-9cb3-4f71-c0b4-44ce7b953419"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Start Point =  923260\n","End Point =  5434832\n","delay : 41.871201814058956 sec\n"]}]},{"cell_type":"code","source":["int(valid_index[0] * term / 22.05)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pGHjQ7RY1o6v","executionInfo":{"status":"ok","timestamp":1707331136903,"user_tz":-540,"elapsed":345,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"43c257a2-a5af-4d22-de5e-0d34ebb98ee9"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["41871"]},"metadata":{},"execution_count":106}]},{"cell_type":"code","source":["valid_index[3] * term / 22050"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DYjWQPde1nDD","executionInfo":{"status":"ok","timestamp":1707330706441,"user_tz":-540,"elapsed":521,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"49c6a0c6-f131-4c9b-c751-bd8330561fd7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["42.62113378684807"]},"metadata":{},"execution_count":104}]},{"cell_type":"code","source":["startidx = []\n","endidx = []\n","startidx.append(valid_index[0] * term / 22.05)\n","for i in range(len(valid_index)-1):\n","  dif = (valid_index[i+1] * term / 22.05) - (valid_index[i] * term / 22.05)\n","  if dif > 2000: # 차이가 2초보다 크면\n","    endidx.append(valid_index[i] * term / 22.05)\n","    startidx.append(valid_index[i+1] * term / 22.05)\n","\n","endidx.append(valid_index[len(valid_index)-1] * term / 22.05)"],"metadata":{"id":"kCvyn9TF1zA1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["startidx"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-a79KsNv3m3-","executionInfo":{"status":"ok","timestamp":1707331693503,"user_tz":-540,"elapsed":308,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"a32d35b4-a968-4865-d4f8-a97a99177e70"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[41871.20181405896,\n"," 70868.57142857142,\n"," 76118.09523809524,\n"," 80742.67573696145,\n"," 99240.9977324263,\n"," 130488.16326530612,\n"," 141237.18820861678,\n"," 146486.7120181406,\n"," 151111.2925170068,\n"," 169609.61451247166,\n"," 227104.3990929705,\n"," 234478.73015873015,\n"," 239728.25396825396,\n"," 241978.0498866213,\n"," 246477.641723356]"]},"metadata":{},"execution_count":126}]},{"cell_type":"code","source":["endidx"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"l042mqOl3sLA","executionInfo":{"status":"ok","timestamp":1707331695203,"user_tz":-540,"elapsed":310,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"5932cee1-0834-4860-a63c-16903b5c1ff5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[63994.19501133787,\n"," 72118.45804988661,\n"," 77242.9931972789,\n"," 97116.19047619047,\n"," 127363.44671201814,\n"," 130488.16326530612,\n"," 141237.18820861678,\n"," 146486.7120181406,\n"," 166484.89795918367,\n"," 223729.7052154195,\n"," 230604.08163265305,\n"," 236228.57142857142,\n"," 239728.25396825396,\n"," 242228.02721088435,\n"," 246477.641723356]"]},"metadata":{},"execution_count":127}]},{"cell_type":"code","source":["full_song = mr[0:startidx[0]]\n","\n","for i in range(len(endidx)):\n","  tmp_song = voice[startidx[i]:endidx[i]]\n","  full_song = full_song + tmp_song\n","  if i != len(startidx)-1:\n","    tmp_song = mr[endidx[i]:startidx[i+1]]\n","    full_song = full_song + tmp_song\n","  else:\n","    tmp_song = mr[endidx[i]:len(voice)]\n","    full_song = full_song + tmp_song"],"metadata":{"id":"H70PHVXP3z77"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["full_song"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":74,"output_embedded_package_id":"1ZzJUVVEpmfTT_NH9uJ6vnMQ4UOsksQ8A"},"id":"QoVjEmsc4a7r","executionInfo":{"status":"ok","timestamp":1707331735826,"user_tz":-540,"elapsed":13082,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"37fa3a04-a4f4-48e9-d180-2d0e602466ac"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":"Output hidden; open in https://colab.research.google.com to view."},"metadata":{}}]},{"cell_type":"markdown","source":["### 음원처리 함수"],"metadata":{"id":"MePyUpfiAH1C"}},{"cell_type":"code","source":["# voice가 없는 부분에 대한 detection 함수\n","def indexing(file_link):\n","  audio, sr = librosa.load(file_link, sr=22050)\n","\n","  term = int(sr/8)\n","  Amplitude_ABS = np.abs(audio)\n","  Amplitude_ABS_mean = []\n","\n","  for t in range(0, len(Amplitude_ABS), term):\n","      Amplitude_ABS_mean.append(Amplitude_ABS[t:t + term].mean())\n","\n","  Amplitude_Distance = []\n","  for i in range(0, len(Amplitude_ABS_mean) - 1) :\n","      distance = 1 + ((Amplitude_ABS_mean[i+1] - Amplitude_ABS_mean[i]) ** 2)\n","      Amplitude_Distance.append(distance ** 0.5)\n","\n","  Amplitude_Distance_array = np.array(Amplitude_Distance)\n","  threshold = Amplitude_Distance_array.mean()\n","\n","  valid_index = []\n","  for i in range(0, len(Amplitude_Distance)) :\n","      if Amplitude_Distance[i] > threshold :\n","          valid_index.append(i)\n","\n","  startidx = []\n","  endidx = []\n","  startidx.append(valid_index[0] * term / 22.05)\n","  for i in range(len(valid_index)-1):\n","    dif = (valid_index[i+1] * term / 22.05) - (valid_index[i] * term / 22.05)\n","    if dif > 2000: # 차이가 2초보다 크면\n","      endidx.append(valid_index[i] * term / 22.05)\n","      startidx.append(valid_index[i+1] * term / 22.05)\n","\n","  endidx.append(valid_index[len(valid_index)-1] * term / 22.05)\n","\n","  return startidx, endidx"],"metadata":{"id":"3mzL4xJByp9h"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["separator = Separator('spleeter:2stems')"],"metadata":{"id":"VgdMnG75T3Fw"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## RnB_Soul"],"metadata":{"id":"-nO9mGyTT7tx"}},{"cell_type":"code","source":["src = '/content/drive/MyDrive/Hmm2Song/data/song/RnB_Soul'\n","src_files = os.listdir(src)\n","files = [x for x in src_files]\n","files"],"metadata":{"id":"h0i1oedRTswJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["cur_dir = src + '/'"],"metadata":{"id":"AROXRWgwUWMr"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for f in files:\n","  file_name = f[:-4]\n","  separator.separate_to_file(f'{cur_dir}{file_name}.mp3', f'{cur_dir}', codec='mp3', filename_format='{filename}_{instrument}.{codec}')\n","  print(f\"{f} --- done\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TahRR5MPUOE4","executionInfo":{"status":"ok","timestamp":1707326796456,"user_tz":-540,"elapsed":400728,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"38574a26-af4c-475a-d0b3-78375869c648"},"execution_count":null,"outputs":[{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["INFO:spleeter:Downloading model archive https://github.com/deezer/spleeter/releases/download/v1.4.0/2stems.tar.gz\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["INFO:spleeter:Downloading model archive https://github.com/deezer/spleeter/releases/download/v1.4.0/2stems.tar.gz\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["INFO:spleeter:Validating archive checksum\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["INFO:spleeter:Validating archive checksum\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["INFO:spleeter:Extracting downloaded 2stems archive\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["INFO:spleeter:Extracting downloaded 2stems archive\n"]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["INFO:spleeter:2stems model file(s) extracted\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["INFO:spleeter:2stems model file(s) extracted\n","WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/spleeter/separator.py:146: calling DatasetV2.from_generator (from tensorflow.python.data.ops.dataset_ops) with output_types is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use output_signature instead\n","WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/spleeter/separator.py:146: calling DatasetV2.from_generator (from tensorflow.python.data.ops.dataset_ops) with output_shapes is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use output_signature instead\n","WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/keras/layers/normalization/batch_normalization.py:514: _colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Colocations handled automatically by placer.\n"]},{"output_type":"stream","name":"stdout","text":["000.mp3 --- done\n","001.mp3 --- done\n","002.mp3 --- done\n","003.mp3 --- done\n","004.mp3 --- done\n","005.mp3 --- done\n","006.mp3 --- done\n","007.mp3 --- done\n","008.mp3 --- done\n","009.mp3 --- done\n","010.mp3 --- done\n","011.mp3 --- done\n","012.mp3 --- done\n","013.mp3 --- done\n","014.mp3 --- done\n","015.mp3 --- done\n","016.mp3 --- done\n","017.mp3 --- done\n","018.mp3 --- done\n","019.mp3 --- done\n","020.mp3 --- done\n","021.mp3 --- done\n","022.mp3 --- done\n","023.mp3 --- done\n","024.mp3 --- done\n","026.mp3 --- done\n","027.mp3 --- done\n","028.mp3 --- done\n","029.mp3 --- done\n","030.mp3 --- done\n","031.mp3 --- done\n","032.mp3 --- done\n","033.mp3 --- done\n","034.mp3 --- done\n","035.mp3 --- done\n","036.mp3 --- done\n","038.mp3 --- done\n","039.mp3 --- done\n","040.mp3 --- done\n","041.mp3 --- done\n","042.mp3 --- done\n","043.mp3 --- done\n","044.mp3 --- done\n","045.mp3 --- done\n","046.mp3 --- done\n","047.mp3 --- done\n","048.mp3 --- done\n","049.mp3 --- done\n","050.mp3 --- done\n","051.mp3 --- done\n","052.mp3 --- done\n","053.mp3 --- done\n","054.mp3 --- done\n","055.mp3 --- done\n","056.mp3 --- done\n","057.mp3 --- done\n","058.mp3 --- done\n","059.mp3 --- done\n","060.mp3 --- done\n","061.mp3 --- done\n","062.mp3 --- done\n","063.mp3 --- done\n","064.mp3 --- done\n","065.mp3 --- done\n","066.mp3 --- done\n","067.mp3 --- done\n","068.mp3 --- done\n","069.mp3 --- done\n","070.mp3 --- done\n","071.mp3 --- done\n","072.mp3 --- done\n","073.mp3 --- done\n","074.mp3 --- done\n","075.mp3 --- done\n","076.mp3 --- done\n","077.mp3 --- done\n","078.mp3 --- done\n","079.mp3 --- done\n","080.mp3 --- done\n","081.mp3 --- done\n","082.mp3 --- done\n","083.mp3 --- done\n","084.mp3 --- done\n","085.mp3 --- done\n","086.mp3 --- done\n","087.mp3 --- done\n","088.mp3 --- done\n","089.mp3 --- done\n","090.mp3 --- done\n","091.mp3 --- done\n","092.mp3 --- done\n","093.mp3 --- done\n","094.mp3 --- done\n","095.mp3 --- done\n","096.mp3 --- done\n","097.mp3 --- done\n","098.mp3 --- done\n","099.mp3 --- done\n"]}]},{"cell_type":"code","source":["out_dir = '/content/drive/MyDrive/Hmm2Song/data/song/Full_input/'"],"metadata":{"id":"r_0mH_S7WRjX"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["## 분리된 거 full_song으로 만들어서 Full_input 폴더로!\n","\n","for f in files:\n","  file_name = f[:-4]\n","  startidx, endidx = indexing(cur_dir+file_name+'_vocals.mp3') # 임계값 이상만 인덱싱\n","\n","  # mr과 voice 불러오기\n","  voice = AudioSegment.from_file(cur_dir+file_name+'_vocals.mp3')\n","  mr = AudioSegment.from_file(cur_dir+file_name+'_accompaniment.mp3')\n","\n","  full_song = mr[0:startidx[0]]\n","\n","  for i in range(len(startidx)):\n","    tmp_song = voice[startidx[i]:endidx[i]]\n","    full_song = full_song + tmp_song\n","    if i != len(startidx)-1:\n","      tmp_song = mr[endidx[i]:startidx[i+1]]\n","      full_song = full_song + tmp_song\n","    else:\n","      tmp_song = mr[endidx[i]:len(voice)]\n","      full_song = full_song + tmp_song\n","\n","  full_song.export(out_dir+f, format='mp3')\n","  print(f\"{f} --- done\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AHObK57OUcAo","executionInfo":{"status":"ok","timestamp":1707333087750,"user_tz":-540,"elapsed":919401,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"59d2cebe-855f-4bc4-eab2-7f49423a3b97"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["000.mp3 --- done\n","001.mp3 --- done\n","002.mp3 --- done\n","003.mp3 --- done\n","004.mp3 --- done\n","005.mp3 --- done\n","006.mp3 --- done\n","007.mp3 --- done\n","008.mp3 --- done\n","009.mp3 --- done\n","010.mp3 --- done\n","011.mp3 --- done\n","012.mp3 --- done\n","013.mp3 --- done\n","014.mp3 --- done\n","015.mp3 --- done\n","016.mp3 --- done\n","017.mp3 --- done\n","018.mp3 --- done\n","019.mp3 --- done\n","020.mp3 --- done\n","021.mp3 --- done\n","022.mp3 --- done\n","023.mp3 --- done\n","024.mp3 --- done\n","026.mp3 --- done\n","027.mp3 --- done\n","028.mp3 --- done\n","029.mp3 --- done\n","030.mp3 --- done\n","031.mp3 --- done\n","032.mp3 --- done\n","033.mp3 --- done\n","034.mp3 --- done\n","035.mp3 --- done\n","036.mp3 --- done\n","038.mp3 --- done\n","039.mp3 --- done\n","040.mp3 --- done\n","041.mp3 --- done\n","042.mp3 --- done\n","043.mp3 --- done\n","044.mp3 --- done\n","045.mp3 --- done\n","046.mp3 --- done\n","047.mp3 --- done\n","048.mp3 --- done\n","049.mp3 --- done\n","050.mp3 --- done\n","051.mp3 --- done\n","052.mp3 --- done\n","053.mp3 --- done\n","054.mp3 --- done\n","055.mp3 --- done\n","056.mp3 --- done\n","057.mp3 --- done\n","058.mp3 --- done\n","059.mp3 --- done\n","060.mp3 --- done\n","061.mp3 --- done\n","062.mp3 --- done\n","063.mp3 --- done\n","064.mp3 --- done\n","065.mp3 --- done\n","066.mp3 --- done\n","067.mp3 --- done\n","068.mp3 --- done\n","069.mp3 --- done\n","070.mp3 --- done\n","071.mp3 --- done\n","072.mp3 --- done\n","073.mp3 --- done\n","074.mp3 --- done\n","075.mp3 --- done\n","076.mp3 --- done\n","077.mp3 --- done\n","078.mp3 --- done\n","079.mp3 --- done\n","080.mp3 --- done\n","081.mp3 --- done\n","082.mp3 --- done\n","083.mp3 --- done\n","084.mp3 --- done\n","085.mp3 --- done\n","086.mp3 --- done\n","087.mp3 --- done\n","088.mp3 --- done\n","089.mp3 --- done\n","090.mp3 --- done\n","091.mp3 --- done\n","092.mp3 --- done\n","093.mp3 --- done\n","094.mp3 --- done\n","095.mp3 --- done\n","096.mp3 --- done\n","097.mp3 --- done\n","098.mp3 --- done\n","099.mp3 --- done\n"]}]},{"cell_type":"markdown","source":["## Indie"],"metadata":{"id":"UI_kk06BXIHR"}},{"cell_type":"code","source":["src = '/content/drive/MyDrive/Hmm2Song/data/song/Indie'\n","src_files = os.listdir(src)\n","files = [x for x in src_files]\n","cur_dir = src + '/'\n","\n","for f in files:\n","  file_name = f[:-4]\n","  separator.separate_to_file(f'{cur_dir}{file_name}.mp3', f'{cur_dir}', codec='mp3', filename_format='{filename}_{instrument}.{codec}')\n","  print(f\"{f} --- done\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4bWie_raXK9e","executionInfo":{"status":"ok","timestamp":1707333842823,"user_tz":-540,"elapsed":550343,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"57dca752-bc0b-4358-8b97-4127d38c9670"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["INFO:spleeter:Downloading model archive https://github.com/deezer/spleeter/releases/download/v1.4.0/2stems.tar.gz\n"]},{"output_type":"stream","name":"stderr","text":["INFO:spleeter:Downloading model archive https://github.com/deezer/spleeter/releases/download/v1.4.0/2stems.tar.gz\n"]},{"output_type":"stream","name":"stdout","text":["INFO:spleeter:Validating archive checksum\n"]},{"output_type":"stream","name":"stderr","text":["INFO:spleeter:Validating archive checksum\n"]},{"output_type":"stream","name":"stdout","text":["INFO:spleeter:Extracting downloaded 2stems archive\n"]},{"output_type":"stream","name":"stderr","text":["INFO:spleeter:Extracting downloaded 2stems archive\n"]},{"output_type":"stream","name":"stdout","text":["INFO:spleeter:2stems model file(s) extracted\n"]},{"output_type":"stream","name":"stderr","text":["INFO:spleeter:2stems model file(s) extracted\n","WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/spleeter/separator.py:146: calling DatasetV2.from_generator (from tensorflow.python.data.ops.dataset_ops) with output_types is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use output_signature instead\n","WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/spleeter/separator.py:146: calling DatasetV2.from_generator (from tensorflow.python.data.ops.dataset_ops) with output_shapes is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use output_signature instead\n","WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/keras/layers/normalization/batch_normalization.py:514: _colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Colocations handled automatically by placer.\n"]},{"output_type":"stream","name":"stdout","text":["104.mp3 --- done\n","110.mp3 --- done\n","111.mp3 --- done\n","119.mp3 --- done\n","120.mp3 --- done\n","121.mp3 --- done\n","125.mp3 --- done\n","127.mp3 --- done\n","130.mp3 --- done\n","134.mp3 --- done\n","138.mp3 --- done\n","141.mp3 --- done\n","142.mp3 --- done\n","147.mp3 --- done\n","150.mp3 --- done\n","153.mp3 --- done\n","156.mp3 --- done\n","159.mp3 --- done\n","161.mp3 --- done\n","163.mp3 --- done\n","164.mp3 --- done\n","167.mp3 --- done\n","170.mp3 --- done\n","176.mp3 --- done\n","181.mp3 --- done\n","185.mp3 --- done\n","186.mp3 --- done\n","188.mp3 --- done\n"]}]},{"cell_type":"code","source":["out_dir = '/content/drive/MyDrive/Hmm2Song/data/song/Full_input/'\n","\n","## 분리된 거 full_song으로 만들어서 Full_input 폴더로!\n","\n","for f in files:\n","  file_name = f[:-4]\n","  startidx, endidx = indexing(cur_dir+file_name+'_vocals.mp3') # 임계값 이상만 인덱싱\n","\n","  # mr과 voice 불러오기\n","  voice = AudioSegment.from_file(cur_dir+file_name+'_vocals.mp3')\n","  mr = AudioSegment.from_file(cur_dir+file_name+'_accompaniment.mp3')\n","\n","  full_song = mr[0:startidx[0]]\n","\n","  for i in range(len(startidx)):\n","    tmp_song = voice[startidx[i]:endidx[i]]\n","    full_song = full_song + tmp_song\n","    if i != len(startidx)-1:\n","      tmp_song = mr[endidx[i]:startidx[i+1]]\n","      full_song = full_song + tmp_song\n","    else:\n","      tmp_song = mr[endidx[i]:len(voice)]\n","      full_song = full_song + tmp_song\n","\n","  full_song.export(out_dir+f, format='mp3')\n","  print(f\"{f} --- done\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SGiTPy1iXUPl","executionInfo":{"status":"ok","timestamp":1707334061567,"user_tz":-540,"elapsed":213897,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"c2d8cad0-d085-49dd-b40a-169aac1a0a2f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["104.mp3 --- done\n","110.mp3 --- done\n","111.mp3 --- done\n","119.mp3 --- done\n","120.mp3 --- done\n","121.mp3 --- done\n","125.mp3 --- done\n","127.mp3 --- done\n","130.mp3 --- done\n","134.mp3 --- done\n","138.mp3 --- done\n","141.mp3 --- done\n","142.mp3 --- done\n","147.mp3 --- done\n","150.mp3 --- done\n","153.mp3 --- done\n","156.mp3 --- done\n","159.mp3 --- done\n","161.mp3 --- done\n","163.mp3 --- done\n","164.mp3 --- done\n","167.mp3 --- done\n","170.mp3 --- done\n","176.mp3 --- done\n","181.mp3 --- done\n","185.mp3 --- done\n","186.mp3 --- done\n","188.mp3 --- done\n"]}]},{"cell_type":"markdown","source":["## Folk_Blues"],"metadata":{"id":"9qugm_mzAS_v"}},{"cell_type":"code","source":["src = '/content/drive/MyDrive/Hmm2Song/data/song/Folk_Blues'\n","src_files = os.listdir(src)\n","files = [x for x in src_files]\n","cur_dir = src + '/'\n","\n","for f in files:\n","  file_name = f[:-4]\n","  separator.separate_to_file(f'{cur_dir}{file_name}.mp3', f'{cur_dir}', codec='mp3', filename_format='{filename}_{instrument}.{codec}')\n","  print(f\"{f} --- done\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bU3lHdK_AVCS","executionInfo":{"status":"ok","timestamp":1707335875932,"user_tz":-540,"elapsed":1802111,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"00b871c4-bafa-4ed5-d3e7-9c6a0a956cd6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["191.mp3 --- done\n","192.mp3 --- done\n","193.mp3 --- done\n","194.mp3 --- done\n","195.mp3 --- done\n","196.mp3 --- done\n","198.mp3 --- done\n","199.mp3 --- done\n","200.mp3 --- done\n","201.mp3 --- done\n","202.mp3 --- done\n","203.mp3 --- done\n","204.mp3 --- done\n","205.mp3 --- done\n","206.mp3 --- done\n","207.mp3 --- done\n","208.mp3 --- done\n","209.mp3 --- done\n","210.mp3 --- done\n","211.mp3 --- done\n","212.mp3 --- done\n","213.mp3 --- done\n","214.mp3 --- done\n","215.mp3 --- done\n","216.mp3 --- done\n","217.mp3 --- done\n","218.mp3 --- done\n","219.mp3 --- done\n","220.mp3 --- done\n","221.mp3 --- done\n","222.mp3 --- done\n","223.mp3 --- done\n","224.mp3 --- done\n","225.mp3 --- done\n","226.mp3 --- done\n","227.mp3 --- done\n","228.mp3 --- done\n","229.mp3 --- done\n","230.mp3 --- done\n","232.mp3 --- done\n","233.mp3 --- done\n","235.mp3 --- done\n","236.mp3 --- done\n","237.mp3 --- done\n","238.mp3 --- done\n","239.mp3 --- done\n","240.mp3 --- done\n","241.mp3 --- done\n","242.mp3 --- done\n","243.mp3 --- done\n","244.mp3 --- done\n","245.mp3 --- done\n","246.mp3 --- done\n","247.mp3 --- done\n","248.mp3 --- done\n","249.mp3 --- done\n","250.mp3 --- done\n","251.mp3 --- done\n","252.mp3 --- done\n","253.mp3 --- done\n","254.mp3 --- done\n","255.mp3 --- done\n","256.mp3 --- done\n","257.mp3 --- done\n","258.mp3 --- done\n","259.mp3 --- done\n","260.mp3 --- done\n","261.mp3 --- done\n","262.mp3 --- done\n","144.mp3 --- done\n","107.mp3 --- done\n","145.mp3 --- done\n","155.mp3 --- done\n","116.mp3 --- done\n","146.mp3 --- done\n","183.mp3 --- done\n","157.mp3 --- done\n","174.mp3 --- done\n","179.mp3 --- done\n","189.mp3 --- done\n","197.mp3 --- done\n","133.mp3 --- done\n","109.mp3 --- done\n","166.mp3 --- done\n","113.mp3 --- done\n","148.mp3 --- done\n","154.mp3 --- done\n","124.mp3 --- done\n","117.mp3 --- done\n","114.mp3 --- done\n","108.mp3 --- done\n","135.mp3 --- done\n","182.mp3 --- done\n","126.mp3 --- done\n","131.mp3 --- done\n","115.mp3 --- done\n","132.mp3 --- done\n","231.mp3 --- done\n","171.mp3 --- done\n","234.mp3 --- done\n"]}]},{"cell_type":"code","source":["out_dir = '/content/drive/MyDrive/Hmm2Song/data/song/Full_input/'\n","\n","## 분리된 거 full_song으로 만들어서 Full_input 폴더로!\n","\n","for f in files:\n","  file_name = f[:-4]\n","  startidx, endidx = indexing(cur_dir+file_name+'_vocals.mp3') # 임계값 이상만 인덱싱\n","\n","  # mr과 voice 불러오기\n","  voice = AudioSegment.from_file(cur_dir+file_name+'_vocals.mp3')\n","  mr = AudioSegment.from_file(cur_dir+file_name+'_accompaniment.mp3')\n","\n","  full_song = mr[0:startidx[0]]\n","\n","  for i in range(len(startidx)):\n","    tmp_song = voice[startidx[i]:endidx[i]]\n","    full_song = full_song + tmp_song\n","    if i != len(startidx)-1:\n","      tmp_song = mr[endidx[i]:startidx[i+1]]\n","      full_song = full_song + tmp_song\n","    else:\n","      tmp_song = mr[endidx[i]:len(voice)]\n","      full_song = full_song + tmp_song\n","\n","  full_song.export(out_dir+f, format='mp3')\n","  print(f\"{f} --- done\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ey1qkp-3AZfx","executionInfo":{"status":"ok","timestamp":1707336564532,"user_tz":-540,"elapsed":688611,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"e9b9a66b-499f-4bd1-80d6-7fcd39fd7f28"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["191.mp3 --- done\n","192.mp3 --- done\n","193.mp3 --- done\n","194.mp3 --- done\n","195.mp3 --- done\n","196.mp3 --- done\n","198.mp3 --- done\n","199.mp3 --- done\n","200.mp3 --- done\n","201.mp3 --- done\n","202.mp3 --- done\n","203.mp3 --- done\n","204.mp3 --- done\n","205.mp3 --- done\n","206.mp3 --- done\n","207.mp3 --- done\n","208.mp3 --- done\n","209.mp3 --- done\n","210.mp3 --- done\n","211.mp3 --- done\n","212.mp3 --- done\n","213.mp3 --- done\n","214.mp3 --- done\n","215.mp3 --- done\n","216.mp3 --- done\n","217.mp3 --- done\n","218.mp3 --- done\n","219.mp3 --- done\n","220.mp3 --- done\n","221.mp3 --- done\n","222.mp3 --- done\n","223.mp3 --- done\n","224.mp3 --- done\n","225.mp3 --- done\n","226.mp3 --- done\n","227.mp3 --- done\n","228.mp3 --- done\n","229.mp3 --- done\n","230.mp3 --- done\n","232.mp3 --- done\n","233.mp3 --- done\n","235.mp3 --- done\n","236.mp3 --- done\n","237.mp3 --- done\n","238.mp3 --- done\n","239.mp3 --- done\n","240.mp3 --- done\n","241.mp3 --- done\n","242.mp3 --- done\n","243.mp3 --- done\n","244.mp3 --- done\n","245.mp3 --- done\n","246.mp3 --- done\n","247.mp3 --- done\n","248.mp3 --- done\n","249.mp3 --- done\n","250.mp3 --- done\n","251.mp3 --- done\n","252.mp3 --- done\n","253.mp3 --- done\n","254.mp3 --- done\n","255.mp3 --- done\n","256.mp3 --- done\n","257.mp3 --- done\n","258.mp3 --- done\n","259.mp3 --- done\n","260.mp3 --- done\n","261.mp3 --- done\n","262.mp3 --- done\n","144.mp3 --- done\n","107.mp3 --- done\n","145.mp3 --- done\n","155.mp3 --- done\n","116.mp3 --- done\n","146.mp3 --- done\n","183.mp3 --- done\n","157.mp3 --- done\n","174.mp3 --- done\n","179.mp3 --- done\n","189.mp3 --- done\n","197.mp3 --- done\n","133.mp3 --- done\n","109.mp3 --- done\n","166.mp3 --- done\n","113.mp3 --- done\n","148.mp3 --- done\n","154.mp3 --- done\n","124.mp3 --- done\n","117.mp3 --- done\n","114.mp3 --- done\n","108.mp3 --- done\n","135.mp3 --- done\n","182.mp3 --- done\n","126.mp3 --- done\n","131.mp3 --- done\n","115.mp3 --- done\n","132.mp3 --- done\n","231.mp3 --- done\n","171.mp3 --- done\n","234.mp3 --- done\n"]}]},{"cell_type":"markdown","source":["## Rock_Metal"],"metadata":{"id":"35bdpbS0EPNR"}},{"cell_type":"code","source":["src = '/content/drive/MyDrive/Hmm2Song/data/song/Rock_Metal'\n","src_files = os.listdir(src)\n","files = [x for x in src_files]\n","cur_dir = src + '/'\n","\n","for f in files:\n","  file_name = f[:-4]\n","  separator.separate_to_file(f'{cur_dir}{file_name}.mp3', f'{cur_dir}', codec='mp3', filename_format='{filename}_{instrument}.{codec}')\n","  print(f\"{f} --- done\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dLrIVWtGDC9S","executionInfo":{"status":"ok","timestamp":1707338346829,"user_tz":-540,"elapsed":1782307,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"c4d9dbbc-c691-4fd9-a552-6fc4bd304329"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["340.mp3 --- done\n","289.mp3 --- done\n","328.mp3 --- done\n","319.mp3 --- done\n","286.mp3 --- done\n","337.mp3 --- done\n","313.mp3 --- done\n","264.mp3 --- done\n","285 (1).mp3 --- done\n","263.mp3 --- done\n","318.mp3 --- done\n","274.mp3 --- done\n","277.mp3 --- done\n","280.mp3 --- done\n","283.mp3 --- done\n","267.mp3 --- done\n","335.mp3 --- done\n","298.mp3 --- done\n","168.mp3 --- done\n","112.mp3 --- done\n","128.mp3 --- done\n","158.mp3 --- done\n","152.mp3 --- done\n","178.mp3 --- done\n","100.mp3 --- done\n","118.mp3 --- done\n","123.mp3 --- done\n","293.mp3 --- done\n","308.mp3 --- done\n","103.mp3 --- done\n","327.mp3 --- done\n","322.mp3 --- done\n","326.mp3 --- done\n","320.mp3 --- done\n","307.mp3 --- done\n","295.mp3 --- done\n","324.mp3 --- done\n","305.mp3 --- done\n","302.mp3 --- done\n","314.mp3 --- done\n","311.mp3 --- done\n","137.mp3 --- done\n","332.mp3 --- done\n","339.mp3 --- done\n","278.mp3 --- done\n","285.mp3 --- done\n","282.mp3 --- done\n","310.mp3 --- done\n","317.mp3 --- done\n","271.mp3 --- done\n","299.mp3 --- done\n","129.mp3 --- done\n","281.mp3 --- done\n","101.mp3 --- done\n","333.mp3 --- done\n","301.mp3 --- done\n","266.mp3 --- done\n","268.mp3 --- done\n","269.mp3 --- done\n","336.mp3 --- done\n","290.mp3 --- done\n","169.mp3 --- done\n","172.mp3 --- done\n","173.mp3 --- done\n","288.mp3 --- done\n","149.mp3 --- done\n","165.mp3 --- done\n","275.mp3 --- done\n","330.mp3 --- done\n","312.mp3 --- done\n","276.mp3 --- done\n","187.mp3 --- done\n","329.mp3 --- done\n","270.mp3 --- done\n","279.mp3 --- done\n","315.mp3 --- done\n","272.mp3 --- done\n","304.mp3 --- done\n","291.mp3 --- done\n","297.mp3 --- done\n","306.mp3 --- done\n","300.mp3 --- done\n","341.mp3 --- done\n","321.mp3 --- done\n","309.mp3 --- done\n","303.mp3 --- done\n","265.mp3 --- done\n","273.mp3 --- done\n","294.mp3 --- done\n","338.mp3 --- done\n","325.mp3 --- done\n","296.mp3 --- done\n"]}]},{"cell_type":"code","source":["out_dir = '/content/drive/MyDrive/Hmm2Song/data/song/Full_input/'\n","\n","## 분리된 거 full_song으로 만들어서 Full_input 폴더로!\n","\n","for f in files:\n","  file_name = f[:-4]\n","  startidx, endidx = indexing(cur_dir+file_name+'_vocals.mp3') # 임계값 이상만 인덱싱\n","\n","  # mr과 voice 불러오기\n","  voice = AudioSegment.from_file(cur_dir+file_name+'_vocals.mp3')\n","  mr = AudioSegment.from_file(cur_dir+file_name+'_accompaniment.mp3')\n","\n","  full_song = mr[0:startidx[0]]\n","\n","  for i in range(len(startidx)):\n","    tmp_song = voice[startidx[i]:endidx[i]]\n","    full_song = full_song + tmp_song\n","    if i != len(startidx)-1:\n","      tmp_song = mr[endidx[i]:startidx[i+1]]\n","      full_song = full_song + tmp_song\n","    else:\n","      tmp_song = mr[endidx[i]:len(voice)]\n","      full_song = full_song + tmp_song\n","\n","  full_song.export(out_dir+f, format='mp3')\n","  print(f\"{f} --- done\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"J0OHSoq5EXwE","executionInfo":{"status":"ok","timestamp":1707339055845,"user_tz":-540,"elapsed":709027,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"c4b6fdfb-3006-401d-c35b-4d3435a614a0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["340.mp3 --- done\n","289.mp3 --- done\n","328.mp3 --- done\n","319.mp3 --- done\n","286.mp3 --- done\n","337.mp3 --- done\n","313.mp3 --- done\n","264.mp3 --- done\n","285 (1).mp3 --- done\n","263.mp3 --- done\n","318.mp3 --- done\n","274.mp3 --- done\n","277.mp3 --- done\n","280.mp3 --- done\n","283.mp3 --- done\n","267.mp3 --- done\n","335.mp3 --- done\n","298.mp3 --- done\n","168.mp3 --- done\n","112.mp3 --- done\n","128.mp3 --- done\n","158.mp3 --- done\n","152.mp3 --- done\n","178.mp3 --- done\n","100.mp3 --- done\n","118.mp3 --- done\n","123.mp3 --- done\n","293.mp3 --- done\n","308.mp3 --- done\n","103.mp3 --- done\n","327.mp3 --- done\n","322.mp3 --- done\n","326.mp3 --- done\n","320.mp3 --- done\n","307.mp3 --- done\n","295.mp3 --- done\n","324.mp3 --- done\n","305.mp3 --- done\n","302.mp3 --- done\n","314.mp3 --- done\n","311.mp3 --- done\n","137.mp3 --- done\n","332.mp3 --- done\n","339.mp3 --- done\n","278.mp3 --- done\n","285.mp3 --- done\n","282.mp3 --- done\n","310.mp3 --- done\n","317.mp3 --- done\n","271.mp3 --- done\n","299.mp3 --- done\n","129.mp3 --- done\n","281.mp3 --- done\n","101.mp3 --- done\n","333.mp3 --- done\n","301.mp3 --- done\n","266.mp3 --- done\n","268.mp3 --- done\n","269.mp3 --- done\n","336.mp3 --- done\n","290.mp3 --- done\n","169.mp3 --- done\n","172.mp3 --- done\n","173.mp3 --- done\n","288.mp3 --- done\n","149.mp3 --- done\n","165.mp3 --- done\n","275.mp3 --- done\n","330.mp3 --- done\n","312.mp3 --- done\n","276.mp3 --- done\n","187.mp3 --- done\n","329.mp3 --- done\n","270.mp3 --- done\n","279.mp3 --- done\n","315.mp3 --- done\n","272.mp3 --- done\n","304.mp3 --- done\n","291.mp3 --- done\n","297.mp3 --- done\n","306.mp3 --- done\n","300.mp3 --- done\n","341.mp3 --- done\n","321.mp3 --- done\n","309.mp3 --- done\n","303.mp3 --- done\n","265.mp3 --- done\n","273.mp3 --- done\n","294.mp3 --- done\n","338.mp3 --- done\n","325.mp3 --- done\n","296.mp3 --- done\n"]}]},{"cell_type":"markdown","source":["## Trot"],"metadata":{"id":"2qldsQgRHOUr"}},{"cell_type":"code","source":["src = '/content/drive/MyDrive/Hmm2Song/data/song/Trot'\n","src_files = os.listdir(src)\n","files = [x for x in src_files]\n","cur_dir = src + '/'\n","\n","for f in files:\n","  file_name = f[:-4]\n","  separator.separate_to_file(f'{cur_dir}{file_name}.mp3', f'{cur_dir}', codec='mp3', filename_format='{filename}_{instrument}.{codec}')\n","  print(f\"{f} --- done\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VRnKwmTsEgqB","executionInfo":{"status":"ok","timestamp":1707340485931,"user_tz":-540,"elapsed":1430097,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"b45a4251-21d6-4261-e79e-c10c0b46610b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["413.mp3 --- done\n","406.mp3 --- done\n","373.mp3 --- done\n","374.mp3 --- done\n","342.mp3 --- done\n","412.mp3 --- done\n","425.mp3 --- done\n","439.mp3 --- done\n","381.mp3 --- done\n","344.mp3 --- done\n","391.mp3 --- done\n","376.mp3 --- done\n","392.mp3 --- done\n","397.mp3 --- done\n","393.mp3 --- done\n","365.mp3 --- done\n","423.mp3 --- done\n","387.mp3 --- done\n","421.mp3 --- done\n","380.mp3 --- done\n","428.mp3 --- done\n","403.mp3 --- done\n","415.mp3 --- done\n","388.mp3 --- done\n","404.mp3 --- done\n","385.mp3 --- done\n","431.mp3 --- done\n","433.mp3 --- done\n","408.mp3 --- done\n","405.mp3 --- done\n","436.mp3 --- done\n","348.mp3 --- done\n","367.mp3 --- done\n","346.mp3 --- done\n","362.mp3 --- done\n","375.mp3 --- done\n","398.mp3 --- done\n","384.mp3 --- done\n","407.mp3 --- done\n","377.mp3 --- done\n","352.mp3 --- done\n","364.mp3 --- done\n","372.mp3 --- done\n","390.mp3 --- done\n","435.mp3 --- done\n","354.mp3 --- done\n","416.mp3 --- done\n","358.mp3 --- done\n","357.mp3 --- done\n","347.mp3 --- done\n","378.mp3 --- done\n","402.mp3 --- done\n","399.mp3 --- done\n","434.mp3 --- done\n","438.mp3 --- done\n","368.mp3 --- done\n","371.mp3 --- done\n","383.mp3 --- done\n","426.mp3 --- done\n","414.mp3 --- done\n","400.mp3 --- done\n","427.mp3 --- done\n","382.mp3 --- done\n","395.mp3 --- done\n","361.mp3 --- done\n","360.mp3 --- done\n","401.mp3 --- done\n","422.mp3 --- done\n","430.mp3 --- done\n","441.mp3 --- done\n","343.mp3 --- done\n","359.mp3 --- done\n","420.mp3 --- done\n","417.mp3 --- done\n","366.mp3 --- done\n","424.mp3 --- done\n","432.mp3 --- done\n","394.mp3 --- done\n","350.mp3 --- done\n"]}]},{"cell_type":"code","source":["out_dir = '/content/drive/MyDrive/Hmm2Song/data/song/Full_input/'\n","\n","## 분리된 거 full_song으로 만들어서 Full_input 폴더로!\n","\n","for f in files:\n","  file_name = f[:-4]\n","  startidx, endidx = indexing(cur_dir+file_name+'_vocals.mp3') # 임계값 이상만 인덱싱\n","\n","  # mr과 voice 불러오기\n","  voice = AudioSegment.from_file(cur_dir+file_name+'_vocals.mp3')\n","  mr = AudioSegment.from_file(cur_dir+file_name+'_accompaniment.mp3')\n","\n","  full_song = mr[0:startidx[0]]\n","\n","  for i in range(len(startidx)):\n","    tmp_song = voice[startidx[i]:endidx[i]]\n","    full_song = full_song + tmp_song\n","    if i != len(startidx)-1:\n","      tmp_song = mr[endidx[i]:startidx[i+1]]\n","      full_song = full_song + tmp_song\n","    else:\n","      tmp_song = mr[endidx[i]:len(voice)]\n","      full_song = full_song + tmp_song\n","\n","  full_song.export(out_dir+f, format='mp3')\n","  print(f\"{f} --- done\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jiaiGzUOHW_b","executionInfo":{"status":"ok","timestamp":1707341047854,"user_tz":-540,"elapsed":561935,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"606fba1e-00ca-4657-834d-0cb3d1fc392c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["413.mp3 --- done\n","406.mp3 --- done\n","373.mp3 --- done\n","374.mp3 --- done\n","342.mp3 --- done\n","412.mp3 --- done\n","425.mp3 --- done\n","439.mp3 --- done\n","381.mp3 --- done\n","344.mp3 --- done\n","391.mp3 --- done\n","376.mp3 --- done\n","392.mp3 --- done\n","397.mp3 --- done\n","393.mp3 --- done\n","365.mp3 --- done\n","423.mp3 --- done\n","387.mp3 --- done\n","421.mp3 --- done\n","380.mp3 --- done\n","428.mp3 --- done\n","403.mp3 --- done\n","415.mp3 --- done\n","388.mp3 --- done\n","404.mp3 --- done\n","385.mp3 --- done\n","431.mp3 --- done\n","433.mp3 --- done\n","408.mp3 --- done\n","405.mp3 --- done\n","436.mp3 --- done\n","348.mp3 --- done\n","367.mp3 --- done\n","346.mp3 --- done\n","362.mp3 --- done\n","375.mp3 --- done\n","398.mp3 --- done\n","384.mp3 --- done\n","407.mp3 --- done\n","377.mp3 --- done\n","352.mp3 --- done\n","364.mp3 --- done\n","372.mp3 --- done\n","390.mp3 --- done\n","435.mp3 --- done\n","354.mp3 --- done\n","416.mp3 --- done\n","358.mp3 --- done\n","357.mp3 --- done\n","347.mp3 --- done\n","378.mp3 --- done\n","402.mp3 --- done\n","399.mp3 --- done\n","434.mp3 --- done\n","438.mp3 --- done\n","368.mp3 --- done\n","371.mp3 --- done\n","383.mp3 --- done\n","426.mp3 --- done\n","414.mp3 --- done\n","400.mp3 --- done\n","427.mp3 --- done\n","382.mp3 --- done\n","395.mp3 --- done\n","361.mp3 --- done\n","360.mp3 --- done\n","401.mp3 --- done\n","422.mp3 --- done\n","430.mp3 --- done\n","441.mp3 --- done\n","343.mp3 --- done\n","359.mp3 --- done\n","420.mp3 --- done\n","417.mp3 --- done\n","366.mp3 --- done\n","424.mp3 --- done\n","432.mp3 --- done\n","394.mp3 --- done\n","350.mp3 --- done\n"]}]},{"cell_type":"markdown","source":["## Hiphop"],"metadata":{"id":"WbE_x-ig6HRy"}},{"cell_type":"code","source":["src = '/content/drive/MyDrive/Hmm2Song/data/song/Hiphop'\n","src_files = os.listdir(src)\n","files = [x for x in src_files]\n","cur_dir = src + '/'\n","\n","for f in files:\n","  file_name = f[:-4]\n","  separator.separate_to_file(f'{cur_dir}{file_name}.mp3', f'{cur_dir}', codec='mp3', filename_format='{filename}_{instrument}.{codec}')\n","  print(f\"{f} --- done\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gvc3a5-kHbFm","executionInfo":{"status":"ok","timestamp":1707350406307,"user_tz":-540,"elapsed":1710837,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"7c7a3e71-854d-40c3-ccbb-0f607ad61453"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["INFO:spleeter:Downloading model archive https://github.com/deezer/spleeter/releases/download/v1.4.0/2stems.tar.gz\n"]},{"output_type":"stream","name":"stderr","text":["INFO:spleeter:Downloading model archive https://github.com/deezer/spleeter/releases/download/v1.4.0/2stems.tar.gz\n"]},{"output_type":"stream","name":"stdout","text":["INFO:spleeter:Validating archive checksum\n"]},{"output_type":"stream","name":"stderr","text":["INFO:spleeter:Validating archive checksum\n"]},{"output_type":"stream","name":"stdout","text":["INFO:spleeter:Extracting downloaded 2stems archive\n"]},{"output_type":"stream","name":"stderr","text":["INFO:spleeter:Extracting downloaded 2stems archive\n"]},{"output_type":"stream","name":"stdout","text":["INFO:spleeter:2stems model file(s) extracted\n"]},{"output_type":"stream","name":"stderr","text":["INFO:spleeter:2stems model file(s) extracted\n","WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/spleeter/separator.py:146: calling DatasetV2.from_generator (from tensorflow.python.data.ops.dataset_ops) with output_types is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use output_signature instead\n","WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/spleeter/separator.py:146: calling DatasetV2.from_generator (from tensorflow.python.data.ops.dataset_ops) with output_shapes is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use output_signature instead\n","WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/keras/layers/normalization/batch_normalization.py:514: _colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Colocations handled automatically by placer.\n"]},{"output_type":"stream","name":"stdout","text":["442.mp3 --- done\n","443.mp3 --- done\n","444.mp3 --- done\n","445.mp3 --- done\n","446.mp3 --- done\n","447.mp3 --- done\n","448.mp3 --- done\n","449.mp3 --- done\n","450.mp3 --- done\n","451.mp3 --- done\n","452.mp3 --- done\n","453.mp3 --- done\n","454.mp3 --- done\n","455.mp3 --- done\n","456.mp3 --- done\n","457.mp3 --- done\n","458.mp3 --- done\n","459.mp3 --- done\n","461.mp3 --- done\n","462.mp3 --- done\n","463.mp3 --- done\n","464.mp3 --- done\n","467.mp3 --- done\n","468.mp3 --- done\n","469.mp3 --- done\n","470.mp3 --- done\n","471.mp3 --- done\n","472.mp3 --- done\n","473.mp3 --- done\n","474.mp3 --- done\n","476.mp3 --- done\n","478.mp3 --- done\n","479.mp3 --- done\n","481.mp3 --- done\n","482.mp3 --- done\n","483.mp3 --- done\n","484.mp3 --- done\n","487.mp3 --- done\n","488.mp3 --- done\n","489.mp3 --- done\n","491.mp3 --- done\n","492.mp3 --- done\n","494.mp3 --- done\n","496.mp3 --- done\n","498.mp3 --- done\n","500.mp3 --- done\n","501.mp3 --- done\n","502.mp3 --- done\n","503.mp3 --- done\n","505.mp3 --- done\n","506.mp3 --- done\n","507.mp3 --- done\n","508.mp3 --- done\n","510.mp3 --- done\n","511.mp3 --- done\n","513.mp3 --- done\n","515.mp3 --- done\n","519.mp3 --- done\n","517.mp3 --- done\n","520.mp3 --- done\n","521.mp3 --- done\n","523.mp3 --- done\n","524.mp3 --- done\n","525.mp3 --- done\n","526.mp3 --- done\n","527.mp3 --- done\n","528.mp3 --- done\n","516.mp3 --- done\n","536.mp3 --- done\n","533.mp3 --- done\n","535.mp3 --- done\n","537.mp3 --- done\n","522.mp3 --- done\n","499.mp3 --- done\n","495.mp3 --- done\n","140.mp3 --- done\n","460.mp3 --- done\n","151.mp3 --- done\n","490.mp3 --- done\n","162.mp3 --- done\n","177.mp3 --- done\n","486.mp3 --- done\n","531.mp3 --- done\n","493.mp3 --- done\n","466.mp3 --- done\n","475.mp3 --- done\n","480.mp3 --- done\n","485.mp3 --- done\n","504.mp3 --- done\n","509.mp3 --- done\n","512.mp3 --- done\n","514.mp3 --- done\n","529.mp3 --- done\n","530.mp3 --- done\n","532.mp3 --- done\n","534.mp3 --- done\n"]}]},{"cell_type":"code","source":["out_dir = '/content/drive/MyDrive/Hmm2Song/data/song/Full_input/'\n","\n","## 분리된 거 full_song으로 만들어서 Full_input 폴더로!\n","\n","for f in files:\n","  file_name = f[:-4]\n","  startidx, endidx = indexing(cur_dir+file_name+'_vocals.mp3') # 임계값 이상만 인덱싱\n","\n","  # mr과 voice 불러오기\n","  voice = AudioSegment.from_file(cur_dir+file_name+'_vocals.mp3')\n","  mr = AudioSegment.from_file(cur_dir+file_name+'_accompaniment.mp3')\n","\n","  full_song = mr[0:startidx[0]]\n","\n","  for i in range(len(startidx)):\n","    tmp_song = voice[startidx[i]:endidx[i]]\n","    full_song = full_song + tmp_song\n","    if i != len(startidx)-1:\n","      tmp_song = mr[endidx[i]:startidx[i+1]]\n","      full_song = full_song + tmp_song\n","    else:\n","      tmp_song = mr[endidx[i]:len(voice)]\n","      full_song = full_song + tmp_song\n","\n","  full_song.export(out_dir+f, format='mp3')\n","  print(f\"{f} --- done\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1X5SMCOf6W9G","executionInfo":{"status":"ok","timestamp":1707351045771,"user_tz":-540,"elapsed":639477,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"f59df5ad-3800-44cb-ae2b-4da6e66d6654"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["442.mp3 --- done\n","443.mp3 --- done\n","444.mp3 --- done\n","445.mp3 --- done\n","446.mp3 --- done\n","447.mp3 --- done\n","448.mp3 --- done\n","449.mp3 --- done\n","450.mp3 --- done\n","451.mp3 --- done\n","452.mp3 --- done\n","453.mp3 --- done\n","454.mp3 --- done\n","455.mp3 --- done\n","456.mp3 --- done\n","457.mp3 --- done\n","458.mp3 --- done\n","459.mp3 --- done\n","461.mp3 --- done\n","462.mp3 --- done\n","463.mp3 --- done\n","464.mp3 --- done\n","467.mp3 --- done\n","468.mp3 --- done\n","469.mp3 --- done\n","470.mp3 --- done\n","471.mp3 --- done\n","472.mp3 --- done\n","473.mp3 --- done\n","474.mp3 --- done\n","476.mp3 --- done\n","478.mp3 --- done\n","479.mp3 --- done\n","481.mp3 --- done\n","482.mp3 --- done\n","483.mp3 --- done\n","484.mp3 --- done\n","487.mp3 --- done\n","488.mp3 --- done\n","489.mp3 --- done\n","491.mp3 --- done\n","492.mp3 --- done\n","494.mp3 --- done\n","496.mp3 --- done\n","498.mp3 --- done\n","500.mp3 --- done\n","501.mp3 --- done\n","502.mp3 --- done\n","503.mp3 --- done\n","505.mp3 --- done\n","506.mp3 --- done\n","507.mp3 --- done\n","508.mp3 --- done\n","510.mp3 --- done\n","511.mp3 --- done\n","513.mp3 --- done\n","515.mp3 --- done\n","519.mp3 --- done\n","517.mp3 --- done\n","520.mp3 --- done\n","521.mp3 --- done\n","523.mp3 --- done\n","524.mp3 --- done\n","525.mp3 --- done\n","526.mp3 --- done\n","527.mp3 --- done\n","528.mp3 --- done\n","516.mp3 --- done\n","536.mp3 --- done\n","533.mp3 --- done\n","535.mp3 --- done\n","537.mp3 --- done\n","522.mp3 --- done\n","499.mp3 --- done\n","495.mp3 --- done\n","140.mp3 --- done\n","460.mp3 --- done\n","151.mp3 --- done\n","490.mp3 --- done\n","162.mp3 --- done\n","177.mp3 --- done\n","486.mp3 --- done\n","531.mp3 --- done\n","493.mp3 --- done\n","466.mp3 --- done\n","475.mp3 --- done\n","480.mp3 --- done\n","485.mp3 --- done\n","504.mp3 --- done\n","509.mp3 --- done\n","512.mp3 --- done\n","514.mp3 --- done\n","529.mp3 --- done\n","530.mp3 --- done\n","532.mp3 --- done\n","534.mp3 --- done\n"]}]},{"cell_type":"markdown","source":["## Dance"],"metadata":{"id":"chsrxYilpqv0"}},{"cell_type":"code","source":["src = '/content/drive/MyDrive/Hmm2Song/data/song/Dance'\n","src_files = os.listdir(src)\n","files = [x for x in src_files]\n","cur_dir = src + '/'\n","\n","for f in files:\n","  file_name = f[:-4]\n","  separator.separate_to_file(f'{cur_dir}{file_name}.mp3', f'{cur_dir}', codec='mp3', filename_format='{filename}_{instrument}.{codec}')\n","  print(f\"{f} --- done\")"],"metadata":{"id":"of62fa1I6aA0","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1707380374170,"user_tz":-540,"elapsed":2416629,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"618fe9a8-1721-42a2-939e-0d98e385ba35"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["INFO:spleeter:Downloading model archive https://github.com/deezer/spleeter/releases/download/v1.4.0/2stems.tar.gz\n"]},{"output_type":"stream","name":"stderr","text":["INFO:spleeter:Downloading model archive https://github.com/deezer/spleeter/releases/download/v1.4.0/2stems.tar.gz\n"]},{"output_type":"stream","name":"stdout","text":["INFO:spleeter:Validating archive checksum\n"]},{"output_type":"stream","name":"stderr","text":["INFO:spleeter:Validating archive checksum\n"]},{"output_type":"stream","name":"stdout","text":["INFO:spleeter:Extracting downloaded 2stems archive\n"]},{"output_type":"stream","name":"stderr","text":["INFO:spleeter:Extracting downloaded 2stems archive\n"]},{"output_type":"stream","name":"stdout","text":["INFO:spleeter:2stems model file(s) extracted\n"]},{"output_type":"stream","name":"stderr","text":["INFO:spleeter:2stems model file(s) extracted\n","WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/spleeter/separator.py:146: calling DatasetV2.from_generator (from tensorflow.python.data.ops.dataset_ops) with output_types is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use output_signature instead\n","WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/spleeter/separator.py:146: calling DatasetV2.from_generator (from tensorflow.python.data.ops.dataset_ops) with output_shapes is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Use output_signature instead\n","WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/keras/layers/normalization/batch_normalization.py:514: _colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Colocations handled automatically by placer.\n"]},{"output_type":"stream","name":"stdout","text":["538.mp3 --- done\n","539.mp3 --- done\n","540.mp3 --- done\n","541.mp3 --- done\n","542.mp3 --- done\n","543.mp3 --- done\n","544.mp3 --- done\n","545.mp3 --- done\n","546.mp3 --- done\n","547.mp3 --- done\n","548.mp3 --- done\n","549.mp3 --- done\n","550.mp3 --- done\n","551.mp3 --- done\n","552.mp3 --- done\n","553.mp3 --- done\n","554.mp3 --- done\n","555.mp3 --- done\n","556.mp3 --- done\n","557.mp3 --- done\n","558.mp3 --- done\n","559.mp3 --- done\n","560.mp3 --- done\n","561.mp3 --- done\n","562.mp3 --- done\n","563.mp3 --- done\n","564.mp3 --- done\n","565.mp3 --- done\n","566.mp3 --- done\n","567.mp3 --- done\n","568.mp3 --- done\n","569.mp3 --- done\n","570.mp3 --- done\n","571.mp3 --- done\n","572.mp3 --- done\n","573.mp3 --- done\n","574.mp3 --- done\n","575.mp3 --- done\n","576.mp3 --- done\n","577.mp3 --- done\n","578.mp3 --- done\n","579.mp3 --- done\n","580.mp3 --- done\n","581.mp3 --- done\n","582.mp3 --- done\n","583.mp3 --- done\n","584.mp3 --- done\n","585.mp3 --- done\n","586.mp3 --- done\n","587.mp3 --- done\n","588.mp3 --- done\n","589.mp3 --- done\n","590.mp3 --- done\n","591.mp3 --- done\n","592.mp3 --- done\n","593.mp3 --- done\n","594.mp3 --- done\n","595.mp3 --- done\n","596.mp3 --- done\n","597.mp3 --- done\n","598.mp3 --- done\n","599.mp3 --- done\n","600.mp3 --- done\n","601.mp3 --- done\n","602.mp3 --- done\n","603.mp3 --- done\n","604.mp3 --- done\n","605.mp3 --- done\n","606.mp3 --- done\n","607.mp3 --- done\n","608.mp3 --- done\n","609.mp3 --- done\n","610.mp3 --- done\n","611.mp3 --- done\n","612.mp3 --- done\n","613.mp3 --- done\n","614.mp3 --- done\n","615.mp3 --- done\n","616.mp3 --- done\n","617.mp3 --- done\n","618.mp3 --- done\n","465.mp3 --- done\n","619.mp3 --- done\n","620.mp3 --- done\n","477.mp3 --- done\n","497.mp3 --- done\n","518.mp3 --- done\n","621.mp3 --- done\n","622.mp3 --- done\n","623.mp3 --- done\n","624.mp3 --- done\n","625.mp3 --- done\n","626.mp3 --- done\n","627.mp3 --- done\n","628.mp3 --- done\n","629.mp3 --- done\n","630.mp3 --- done\n","631.mp3 --- done\n","632.mp3 --- done\n","633.mp3 --- done\n","634.mp3 --- done\n","635.mp3 --- done\n","636.mp3 --- done\n","637.mp3 --- done\n","638.mp3 --- done\n","639.mp3 --- done\n","640.mp3 --- done\n","641.mp3 --- done\n","642.mp3 --- done\n","643.mp3 --- done\n","644.mp3 --- done\n","645.mp3 --- done\n","646.mp3 --- done\n","647.mp3 --- done\n","648.mp3 --- done\n","649.mp3 --- done\n","650.mp3 --- done\n","651.mp3 --- done\n","652.mp3 --- done\n","653.mp3 --- done\n","654.mp3 --- done\n","655.mp3 --- done\n","656.mp3 --- done\n","657.mp3 --- done\n","658.mp3 --- done\n","659.mp3 --- done\n","660.mp3 --- done\n","661.mp3 --- done\n","662.mp3 --- done\n","663.mp3 --- done\n","664.mp3 --- done\n","665.mp3 --- done\n","666.mp3 --- done\n","667.mp3 --- done\n","668.mp3 --- done\n","669.mp3 --- done\n","670.mp3 --- done\n","671.mp3 --- done\n","672.mp3 --- done\n","673.mp3 --- done\n","674.mp3 --- done\n","675.mp3 --- done\n","676.mp3 --- done\n","677.mp3 --- done\n","678.mp3 --- done\n","679.mp3 --- done\n","680.mp3 --- done\n","681.mp3 --- done\n","682.mp3 --- done\n","683.mp3 --- done\n"]}]},{"cell_type":"code","source":["out_dir = '/content/drive/MyDrive/Hmm2Song/data/song/Full_input/'\n","\n","## 분리된 거 full_song으로 만들어서 Full_input 폴더로!\n","\n","for f in files:\n","  file_name = f[:-4]\n","  startidx, endidx = indexing(cur_dir+file_name+'_vocals.mp3') # 임계값 이상만 인덱싱\n","\n","  # mr과 voice 불러오기\n","  voice = AudioSegment.from_file(cur_dir+file_name+'_vocals.mp3')\n","  mr = AudioSegment.from_file(cur_dir+file_name+'_accompaniment.mp3')\n","\n","  full_song = mr[0:startidx[0]]\n","\n","  for i in range(len(startidx)):\n","    tmp_song = voice[startidx[i]:endidx[i]]\n","    full_song = full_song + tmp_song\n","    if i != len(startidx)-1:\n","      tmp_song = mr[endidx[i]:startidx[i+1]]\n","      full_song = full_song + tmp_song\n","    else:\n","      tmp_song = mr[endidx[i]:len(voice)]\n","      full_song = full_song + tmp_song\n","\n","  full_song.export(out_dir+f, format='mp3')\n","  print(f\"{f} --- done\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KuBrc2FHpy_r","executionInfo":{"status":"ok","timestamp":1707381333352,"user_tz":-540,"elapsed":959183,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"05e8418b-9556-427b-af97-c3c1abf1048e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["538.mp3 --- done\n","539.mp3 --- done\n","540.mp3 --- done\n","541.mp3 --- done\n","542.mp3 --- done\n","543.mp3 --- done\n","544.mp3 --- done\n","545.mp3 --- done\n","546.mp3 --- done\n","547.mp3 --- done\n","548.mp3 --- done\n","549.mp3 --- done\n","550.mp3 --- done\n","551.mp3 --- done\n","552.mp3 --- done\n","553.mp3 --- done\n","554.mp3 --- done\n","555.mp3 --- done\n","556.mp3 --- done\n","557.mp3 --- done\n","558.mp3 --- done\n","559.mp3 --- done\n","560.mp3 --- done\n","561.mp3 --- done\n","562.mp3 --- done\n","563.mp3 --- done\n","564.mp3 --- done\n","565.mp3 --- done\n","566.mp3 --- done\n","567.mp3 --- done\n","568.mp3 --- done\n","569.mp3 --- done\n","570.mp3 --- done\n","571.mp3 --- done\n","572.mp3 --- done\n","573.mp3 --- done\n","574.mp3 --- done\n","575.mp3 --- done\n","576.mp3 --- done\n","577.mp3 --- done\n","578.mp3 --- done\n","579.mp3 --- done\n","580.mp3 --- done\n","581.mp3 --- done\n","582.mp3 --- done\n","583.mp3 --- done\n","584.mp3 --- done\n","585.mp3 --- done\n","586.mp3 --- done\n","587.mp3 --- done\n","588.mp3 --- done\n","589.mp3 --- done\n","590.mp3 --- done\n","591.mp3 --- done\n","592.mp3 --- done\n","593.mp3 --- done\n","594.mp3 --- done\n","595.mp3 --- done\n","596.mp3 --- done\n","597.mp3 --- done\n","598.mp3 --- done\n","599.mp3 --- done\n","600.mp3 --- done\n","601.mp3 --- done\n","602.mp3 --- done\n","603.mp3 --- done\n","604.mp3 --- done\n","605.mp3 --- done\n","606.mp3 --- done\n","607.mp3 --- done\n","608.mp3 --- done\n","609.mp3 --- done\n","610.mp3 --- done\n","611.mp3 --- done\n","612.mp3 --- done\n","613.mp3 --- done\n","614.mp3 --- done\n","615.mp3 --- done\n","616.mp3 --- done\n","617.mp3 --- done\n","618.mp3 --- done\n","465.mp3 --- done\n","619.mp3 --- done\n","620.mp3 --- done\n","477.mp3 --- done\n","497.mp3 --- done\n","518.mp3 --- done\n","621.mp3 --- done\n","622.mp3 --- done\n","623.mp3 --- done\n","624.mp3 --- done\n","625.mp3 --- done\n","626.mp3 --- done\n","627.mp3 --- done\n","628.mp3 --- done\n","629.mp3 --- done\n","630.mp3 --- done\n","631.mp3 --- done\n","632.mp3 --- done\n","633.mp3 --- done\n","634.mp3 --- done\n","635.mp3 --- done\n","636.mp3 --- done\n","637.mp3 --- done\n","638.mp3 --- done\n","639.mp3 --- done\n","640.mp3 --- done\n","641.mp3 --- done\n","642.mp3 --- done\n","643.mp3 --- done\n","644.mp3 --- done\n","645.mp3 --- done\n","646.mp3 --- done\n","647.mp3 --- done\n","648.mp3 --- done\n","649.mp3 --- done\n","650.mp3 --- done\n","651.mp3 --- done\n","652.mp3 --- done\n","653.mp3 --- done\n","654.mp3 --- done\n","655.mp3 --- done\n","656.mp3 --- done\n","657.mp3 --- done\n","658.mp3 --- done\n","659.mp3 --- done\n","660.mp3 --- done\n","661.mp3 --- done\n","662.mp3 --- done\n","663.mp3 --- done\n","664.mp3 --- done\n","665.mp3 --- done\n","666.mp3 --- done\n","667.mp3 --- done\n","668.mp3 --- done\n","669.mp3 --- done\n","670.mp3 --- done\n","671.mp3 --- done\n","672.mp3 --- done\n","673.mp3 --- done\n","674.mp3 --- done\n","675.mp3 --- done\n","676.mp3 --- done\n","677.mp3 --- done\n","678.mp3 --- done\n","679.mp3 --- done\n","680.mp3 --- done\n","681.mp3 --- done\n","682.mp3 --- done\n","683.mp3 --- done\n"]}]},{"cell_type":"markdown","source":["## Ballad"],"metadata":{"id":"KLgmk9xipzwI"}},{"cell_type":"code","source":["src = '/content/drive/MyDrive/Hmm2Song/data/song/Ballad'\n","src_files = os.listdir(src)\n","files = [x for x in src_files]\n","cur_dir = src + '/'\n","\n","for f in files:\n","  file_name = f[:-4]\n","  separator.separate_to_file(f'{cur_dir}{file_name}.mp3', f'{cur_dir}', codec='mp3', filename_format='{filename}_{instrument}.{codec}')\n","  print(f\"{f} --- done\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AEAnI0m5p1df","executionInfo":{"status":"ok","timestamp":1707384155927,"user_tz":-540,"elapsed":2822591,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"f5f4b684-bacd-466b-c44d-04f3ee1b5274"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["791.mp3 --- done\n","190.mp3 --- done\n","812.mp3 --- done\n","724.mp3 --- done\n","730.mp3 --- done\n","752.mp3 --- done\n","788.mp3 --- done\n","798.mp3 --- done\n","771.mp3 --- done\n","703.mp3 --- done\n","782.mp3 --- done\n","741.mp3 --- done\n","818.mp3 --- done\n","757.mp3 --- done\n","025.mp3 --- done\n","760.mp3 --- done\n","810.mp3 --- done\n","770.mp3 --- done\n","707.mp3 --- done\n","136.mp3 --- done\n","811.mp3 --- done\n","784.mp3 --- done\n","727.mp3 --- done\n","175.mp3 --- done\n","801.mp3 --- done\n","749.mp3 --- done\n","765.mp3 --- done\n","753.mp3 --- done\n","695.mp3 --- done\n","284.mp3 --- done\n","813.mp3 --- done\n","820.mp3 --- done\n","732.mp3 --- done\n","805.mp3 --- done\n","793.mp3 --- done\n","725.mp3 --- done\n","699.mp3 --- done\n","763.mp3 --- done\n","710.mp3 --- done\n","684.mp3 --- done\n","719.mp3 --- done\n","742.mp3 --- done\n","701.mp3 --- done\n","762.mp3 --- done\n","768.mp3 --- done\n","755.mp3 --- done\n","756.mp3 --- done\n","712.mp3 --- done\n","778.mp3 --- done\n","817.mp3 --- done\n","743.mp3 --- done\n","751.mp3 --- done\n","716.mp3 --- done\n","731.mp3 --- done\n","779.mp3 --- done\n","750.mp3 --- done\n","706.mp3 --- done\n","783.mp3 --- done\n","688.mp3 --- done\n","797.mp3 --- done\n","687.mp3 --- done\n","708.mp3 --- done\n","690.mp3 --- done\n","714.mp3 --- done\n","774.mp3 --- done\n","720.mp3 --- done\n","697.mp3 --- done\n","764.mp3 --- done\n","803.mp3 --- done\n","740.mp3 --- done\n","709.mp3 --- done\n","691.mp3 --- done\n","808.mp3 --- done\n","715.mp3 --- done\n","143.mp3 --- done\n","160.mp3 --- done\n","789.mp3 --- done\n","804.mp3 --- done\n","692.mp3 --- done\n","799.mp3 --- done\n","704.mp3 --- done\n","809.mp3 --- done\n","736.mp3 --- done\n","718.mp3 --- done\n","105.mp3 --- done\n","693.mp3 --- done\n","739.mp3 --- done\n","102.mp3 --- done\n","795.mp3 --- done\n","733.mp3 --- done\n","734.mp3 --- done\n","744.mp3 --- done\n","696.mp3 --- done\n","806.mp3 --- done\n","037.mp3 --- done\n","139.mp3 --- done\n","766.mp3 --- done\n","758.mp3 --- done\n","737.mp3 --- done\n","787.mp3 --- done\n","772.mp3 --- done\n","786.mp3 --- done\n","717.mp3 --- done\n","769.mp3 --- done\n","759.mp3 --- done\n","794.mp3 --- done\n","745.mp3 --- done\n","761.mp3 --- done\n","777.mp3 --- done\n","780.mp3 --- done\n","781.mp3 --- done\n","723.mp3 --- done\n","747.mp3 --- done\n","816.mp3 --- done\n","776.mp3 --- done\n","713.mp3 --- done\n","807.mp3 --- done\n","106.mp3 --- done\n","702.mp3 --- done\n","711.mp3 --- done\n","728.mp3 --- done\n","819.mp3 --- done\n","754.mp3 --- done\n","721.mp3 --- done\n","814.mp3 --- done\n","694.mp3 --- done\n","748.mp3 --- done\n","746.mp3 --- done\n","792.mp3 --- done\n","722.mp3 --- done\n","735.mp3 --- done\n","726.mp3 --- done\n","689.mp3 --- done\n","800.mp3 --- done\n","698.mp3 --- done\n","802.mp3 --- done\n","815.mp3 --- done\n","773.mp3 --- done\n","796.mp3 --- done\n","686.mp3 --- done\n","122.mp3 --- done\n","790.mp3 --- done\n","685.mp3 --- done\n","785.mp3 --- done\n","767.mp3 --- done\n","738.mp3 --- done\n","700.mp3 --- done\n","729.mp3 --- done\n","705.mp3 --- done\n","775.mp3 --- done\n"]}]},{"cell_type":"code","source":["out_dir = '/content/drive/MyDrive/Hmm2Song/data/song/Full_input/'\n","\n","## 분리된 거 full_song으로 만들어서 Full_input 폴더로!\n","\n","for f in files:\n","  file_name = f[:-4]\n","  startidx, endidx = indexing(cur_dir+file_name+'_vocals.mp3') # 임계값 이상만 인덱싱\n","\n","  # mr과 voice 불러오기\n","  voice = AudioSegment.from_file(cur_dir+file_name+'_vocals.mp3')\n","  mr = AudioSegment.from_file(cur_dir+file_name+'_accompaniment.mp3')\n","\n","  full_song = mr[0:startidx[0]]\n","\n","  for i in range(len(startidx)):\n","    tmp_song = voice[startidx[i]:endidx[i]]\n","    full_song = full_song + tmp_song\n","    if i != len(startidx)-1:\n","      tmp_song = mr[endidx[i]:startidx[i+1]]\n","      full_song = full_song + tmp_song\n","    else:\n","      tmp_song = mr[endidx[i]:len(voice)]\n","      full_song = full_song + tmp_song\n","\n","  full_song.export(out_dir+f, format='mp3')\n","  print(f\"{f} --- done\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6Ivec_xxp55Y","executionInfo":{"status":"ok","timestamp":1707385248336,"user_tz":-540,"elapsed":1092416,"user":{"displayName":"강효은","userId":"09017213458312197996"}},"outputId":"d187356f-e40c-44d8-9a0a-98eac683ec99"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["791.mp3 --- done\n","190.mp3 --- done\n","812.mp3 --- done\n","724.mp3 --- done\n","730.mp3 --- done\n","752.mp3 --- done\n","788.mp3 --- done\n","798.mp3 --- done\n","771.mp3 --- done\n","703.mp3 --- done\n","782.mp3 --- done\n","741.mp3 --- done\n","818.mp3 --- done\n","757.mp3 --- done\n","025.mp3 --- done\n","760.mp3 --- done\n","810.mp3 --- done\n","770.mp3 --- done\n","707.mp3 --- done\n","136.mp3 --- done\n","811.mp3 --- done\n","784.mp3 --- done\n","727.mp3 --- done\n","175.mp3 --- done\n","801.mp3 --- done\n","749.mp3 --- done\n","765.mp3 --- done\n","753.mp3 --- done\n","695.mp3 --- done\n","284.mp3 --- done\n","813.mp3 --- done\n","820.mp3 --- done\n","732.mp3 --- done\n","805.mp3 --- done\n","793.mp3 --- done\n","725.mp3 --- done\n","699.mp3 --- done\n","763.mp3 --- done\n","710.mp3 --- done\n","684.mp3 --- done\n","719.mp3 --- done\n","742.mp3 --- done\n","701.mp3 --- done\n","762.mp3 --- done\n","768.mp3 --- done\n","755.mp3 --- done\n","756.mp3 --- done\n","712.mp3 --- done\n","778.mp3 --- done\n","817.mp3 --- done\n","743.mp3 --- done\n","751.mp3 --- done\n","716.mp3 --- done\n","731.mp3 --- done\n","779.mp3 --- done\n","750.mp3 --- done\n","706.mp3 --- done\n","783.mp3 --- done\n","688.mp3 --- done\n","797.mp3 --- done\n","687.mp3 --- done\n","708.mp3 --- done\n","690.mp3 --- done\n","714.mp3 --- done\n","774.mp3 --- done\n","720.mp3 --- done\n","697.mp3 --- done\n","764.mp3 --- done\n","803.mp3 --- done\n","740.mp3 --- done\n","709.mp3 --- done\n","691.mp3 --- done\n","808.mp3 --- done\n","715.mp3 --- done\n","143.mp3 --- done\n","160.mp3 --- done\n","789.mp3 --- done\n","804.mp3 --- done\n","692.mp3 --- done\n","799.mp3 --- done\n","704.mp3 --- done\n","809.mp3 --- done\n","736.mp3 --- done\n","718.mp3 --- done\n","105.mp3 --- done\n","693.mp3 --- done\n","739.mp3 --- done\n","102.mp3 --- done\n","795.mp3 --- done\n","733.mp3 --- done\n","734.mp3 --- done\n","744.mp3 --- done\n","696.mp3 --- done\n","806.mp3 --- done\n","037.mp3 --- done\n","139.mp3 --- done\n","766.mp3 --- done\n","758.mp3 --- done\n","737.mp3 --- done\n","787.mp3 --- done\n","772.mp3 --- done\n","786.mp3 --- done\n","717.mp3 --- done\n","769.mp3 --- done\n","759.mp3 --- done\n","794.mp3 --- done\n","745.mp3 --- done\n","761.mp3 --- done\n","777.mp3 --- done\n","780.mp3 --- done\n","781.mp3 --- done\n","723.mp3 --- done\n","747.mp3 --- done\n","816.mp3 --- done\n","776.mp3 --- done\n","713.mp3 --- done\n","807.mp3 --- done\n","106.mp3 --- done\n","702.mp3 --- done\n","711.mp3 --- done\n","728.mp3 --- done\n","819.mp3 --- done\n","754.mp3 --- done\n","721.mp3 --- done\n","814.mp3 --- done\n","694.mp3 --- done\n","748.mp3 --- done\n","746.mp3 --- done\n","792.mp3 --- done\n","722.mp3 --- done\n","735.mp3 --- done\n","726.mp3 --- done\n","689.mp3 --- done\n","800.mp3 --- done\n","698.mp3 --- done\n","802.mp3 --- done\n","815.mp3 --- done\n","773.mp3 --- done\n","796.mp3 --- done\n","686.mp3 --- done\n","122.mp3 --- done\n","790.mp3 --- done\n","685.mp3 --- done\n","785.mp3 --- done\n","767.mp3 --- done\n","738.mp3 --- done\n","700.mp3 --- done\n","729.mp3 --- done\n","705.mp3 --- done\n","775.mp3 --- done\n"]}]},{"cell_type":"markdown","source":["## 누락된 파일들\n","- tmp 폴더에 모아둔 후 처리하였습니다."],"metadata":{"id":"SAtJnnjnzBOU"}},{"cell_type":"code","source":["src = '/content/drive/MyDrive/Hmm2Song/data/song/tmp/'\n","src_files = os.listdir(src)\n","files = [x for x in src_files]\n","cur_dir = src + '/'\n","\n","for f in files:\n","  file_name = f[:-4]\n","  separator.separate_to_file(f'{cur_dir}{file_name}.mp3', f'{cur_dir}', codec='mp3', filename_format='{filename}_{instrument}.{codec}')\n","  print(f\"{f} --- done\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FT6nQFx7zBOV","executionInfo":{"status":"ok","timestamp":1707482485163,"user_tz":-540,"elapsed":1362069,"user":{"displayName":"차수빈","userId":"16979887891359732529"}},"outputId":"2c681967-2c59-4ff3-c6a3-7ad8b1ef1c64"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["184.mp3 --- done\n","331.mp3 --- done\n","180.mp3 --- done\n","334.mp3 --- done\n","287.mp3 --- done\n","316.mp3 --- done\n","323.mp3 --- done\n","409.mp3 --- done\n","369.mp3 --- done\n","345.mp3 --- done\n","379.mp3 --- done\n","353.mp3 --- done\n","370.mp3 --- done\n","386.mp3 --- done\n","351.mp3 --- done\n","363.mp3 --- done\n","349.mp3 --- done\n","356.mp3 --- done\n","355.mp3 --- done\n","437.mp3 --- done\n","410.mp3 --- done\n","418.mp3 --- done\n","419.mp3 --- done\n","389.mp3 --- done\n","411.mp3 --- done\n","396.mp3 --- done\n","429.mp3 --- done\n","440.mp3 --- done\n"]}]},{"cell_type":"code","source":["out_dir = '/content/drive/MyDrive/Hmm2Song/data/song/Full_input/'\n","\n","## 분리된 거 full_song으로 만들어서 Full_input 폴더로!\n","\n","for f in files:\n","  file_name = f[:-4]\n","  startidx, endidx = indexing(cur_dir+file_name+'_vocals.mp3') # 임계값 이상만 인덱싱\n","\n","  # mr과 voice 불러오기\n","  voice = AudioSegment.from_file(cur_dir+file_name+'_vocals.mp3')\n","  mr = AudioSegment.from_file(cur_dir+file_name+'_accompaniment.mp3')\n","\n","  full_song = mr[0:startidx[0]]\n","\n","  for i in range(len(startidx)):\n","    tmp_song = voice[startidx[i]:endidx[i]]\n","    full_song = full_song + tmp_song\n","    if i != len(startidx)-1:\n","      tmp_song = mr[endidx[i]:startidx[i+1]]\n","      full_song = full_song + tmp_song\n","    else:\n","      tmp_song = mr[endidx[i]:len(voice)]\n","      full_song = full_song + tmp_song\n","\n","  full_song.export(out_dir+f, format='mp3')\n","  print(f\"{f} --- done\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0kB5djgBzBOV","executionInfo":{"status":"ok","timestamp":1707482747645,"user_tz":-540,"elapsed":262486,"user":{"displayName":"차수빈","userId":"16979887891359732529"}},"outputId":"82cba1fe-c575-4837-a3ca-96a9e19ef44d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["184.mp3 --- done\n","331.mp3 --- done\n","180.mp3 --- done\n","334.mp3 --- done\n","287.mp3 --- done\n","316.mp3 --- done\n","323.mp3 --- done\n","409.mp3 --- done\n","369.mp3 --- done\n","345.mp3 --- done\n","379.mp3 --- done\n","353.mp3 --- done\n","370.mp3 --- done\n","386.mp3 --- done\n","351.mp3 --- done\n","363.mp3 --- done\n","349.mp3 --- done\n","356.mp3 --- done\n","355.mp3 --- done\n","437.mp3 --- done\n","410.mp3 --- done\n","418.mp3 --- done\n","419.mp3 --- done\n","389.mp3 --- done\n","411.mp3 --- done\n","396.mp3 --- done\n","429.mp3 --- done\n","440.mp3 --- done\n"]}]},{"cell_type":"markdown","source":["- 파일 처리 후 tmp 폴더는 삭제"],"metadata":{"id":"CcY26TWz6AMG"}}]}