crepe.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "crepe.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNuX9N0qZpnd2QV4FZt3WBT",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dvschultz/ml-art-colabs/blob/master/crepe.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TJeTzH9Mvj_R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "outputId": "b5ac4a4b-5872-414a-d964-12419195313a"
      },
      "source": [
        "!pip install crepe"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting crepe\n",
            "  Downloading https://files.pythonhosted.org/packages/c8/74/1677b9369f233745b3dedf707ce26fb935c5c400379c45400df818f3a805/crepe-0.0.11.tar.gz\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.6/dist-packages (from crepe) (1.18.5)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from crepe) (1.4.1)\n",
            "Requirement already satisfied: matplotlib>=2.1.0 in /usr/local/lib/python3.6/dist-packages (from crepe) (3.2.2)\n",
            "Requirement already satisfied: resampy<0.3.0,>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from crepe) (0.2.2)\n",
            "Requirement already satisfied: h5py<3.0.0,>=2.7.0 in /usr/local/lib/python3.6/dist-packages (from crepe) (2.10.0)\n",
            "Collecting hmmlearn<0.3.0,>=0.2.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ff/7b/33f629a443a0671161c019e55c3f1b511c7e9fdce5ab8c8c3c33470eb939/hmmlearn-0.2.3-cp36-cp36m-manylinux1_x86_64.whl (363kB)\n",
            "\u001b[K     |████████████████████████████████| 368kB 10.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from crepe) (2.4.1)\n",
            "Requirement already satisfied: scikit-learn>=0.16 in /usr/local/lib/python3.6/dist-packages (from crepe) (0.22.2.post1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1.0->crepe) (1.2.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1.0->crepe) (2.4.7)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1.0->crepe) (2.8.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1.0->crepe) (0.10.0)\n",
            "Requirement already satisfied: six>=1.3 in /usr/local/lib/python3.6/dist-packages (from resampy<0.3.0,>=0.2.0->crepe) (1.15.0)\n",
            "Requirement already satisfied: numba>=0.32 in /usr/local/lib/python3.6/dist-packages (from resampy<0.3.0,>=0.2.0->crepe) (0.48.0)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (from imageio>=2.3.0->crepe) (7.0.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.16->crepe) (0.16.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from numba>=0.32->resampy<0.3.0,>=0.2.0->crepe) (49.1.0)\n",
            "Requirement already satisfied: llvmlite<0.32.0,>=0.31.0dev0 in /usr/local/lib/python3.6/dist-packages (from numba>=0.32->resampy<0.3.0,>=0.2.0->crepe) (0.31.0)\n",
            "Building wheels for collected packages: crepe\n",
            "  Building wheel for crepe (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for crepe: filename=crepe-0.0.11-cp36-none-any.whl size=134848476 sha256=7cf88957e671d6031a4c0a86bb85241bc01ec9c006209601b250cea8de1a770e\n",
            "  Stored in directory: /root/.cache/pip/wheels/7f/68/d0/502d27bfe590bfa51c6fe95cf9e4482fed18c22c480a8d7c77\n",
            "Successfully built crepe\n",
            "Installing collected packages: hmmlearn, crepe\n",
            "Successfully installed crepe-0.0.11 hmmlearn-0.2.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQW2ICioNYot",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 986
        },
        "outputId": "1737f297-8376-46b7-8daa-ddaa21daf659"
      },
      "source": [
        "!crepe --help"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "usage: /usr/local/bin/crepe [-h] [--output OUTPUT]\n",
            "                            [--model-capacity {tiny,small,medium,large,full}]\n",
            "                            [--viterbi] [--save-activation] [--save-plot]\n",
            "                            [--plot-voicing] [--no-centering]\n",
            "                            [--step-size STEP_SIZE] [--quiet]\n",
            "                            filename [filename ...]\n",
            "\n",
            "    This is a script for running the pre-trained pitch estimation model, CREPE,\n",
            "    by taking WAV files(s) as input. For each input WAV, a CSV file containing:\n",
            "\n",
            "        time, frequency, confidence\n",
            "        0.00, 424.24, 0.42\n",
            "        0.01, 422.42, 0.84\n",
            "        ...\n",
            "\n",
            "    is created as the output, where the first column is a timestamp in seconds,\n",
            "    the second column is the estimated frequency in Hz, and the third column is\n",
            "    a value between 0 and 1 indicating the model's voicing confidence (i.e.\n",
            "    confidence in the presence of a pitch for every frame).\n",
            "\n",
            "    The script can also optionally save the output activation matrix of the\n",
            "    model to an npy file, where the matrix dimensions are (n_frames, 360) using\n",
            "    a hop size of 10 ms (there are 360 pitch bins covering 20 cents each).\n",
            "    The script can also output a plot of the activation matrix, including an\n",
            "    optional visual representation of the model's voicing detection.\n",
            "    \n",
            "\n",
            "positional arguments:\n",
            "  filename              path to one ore more WAV file(s) to analyze OR can be\n",
            "                        a directory\n",
            "\n",
            "optional arguments:\n",
            "  -h, --help            show this help message and exit\n",
            "  --output OUTPUT, -o OUTPUT\n",
            "                        directory to save the ouptut file(s), must already\n",
            "                        exist; if not given, the output will be saved to the\n",
            "                        same directory as the input WAV file(s)\n",
            "  --model-capacity {tiny,small,medium,large,full}, -c {tiny,small,medium,large,full}\n",
            "                        String specifying the model capacity; smaller models\n",
            "                        are faster to compute, but may yield less accurate\n",
            "                        pitch estimation\n",
            "  --viterbi, -V         perform Viterbi decoding to smooth the pitch curve\n",
            "  --save-activation, -a\n",
            "                        save the output activation matrix to a .npy file\n",
            "  --save-plot, -p       save a plot of the activation matrix to a .png file\n",
            "  --plot-voicing, -v    Plot the voicing prediction on top of the output\n",
            "                        activation matrix plot\n",
            "  --no-centering, -n    Don't pad the signal, meaning frames will begin at\n",
            "                        their timestamp instead of being centered around their\n",
            "                        timestamp (which is the default). CAUTION: setting\n",
            "                        this option can result in CREPE's output being\n",
            "                        misaligned with respect to the output of other audio\n",
            "                        processing tools and is generally not recommended.\n",
            "  --step-size STEP_SIZE, -s STEP_SIZE\n",
            "                        The step size in milliseconds for running pitch\n",
            "                        estimation. The default is 10 ms.\n",
            "  --quiet, -q           Suppress all non-error printouts (e.g. progress bar).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MY-jYdKFPAnw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 904
        },
        "outputId": "df3dc082-265f-41c0-b666-5aba7ad1d09e"
      },
      "source": [
        "!crepe /content/test.wav --step-size 40 -n -p -c full"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CREPE: Processing /content/test.wav ... (1/1)\n",
            "2020-07-26 03:48:33.756579: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-07-26 03:48:35.250445: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2020-07-26 03:48:35.293671: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-07-26 03:48:35.294299: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\n",
            "coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n",
            "2020-07-26 03:48:35.294340: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-07-26 03:48:35.580056: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-07-26 03:48:35.746074: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-07-26 03:48:35.773205: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-07-26 03:48:36.032889: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-07-26 03:48:36.053376: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-07-26 03:48:36.600878: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-07-26 03:48:36.601105: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-07-26 03:48:36.601744: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-07-26 03:48:36.602288: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0\n",
            "2020-07-26 03:48:36.602609: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX512F\n",
            "2020-07-26 03:48:36.630216: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2000175000 Hz\n",
            "2020-07-26 03:48:36.630465: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1996d80 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-07-26 03:48:36.630498: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-07-26 03:48:36.785112: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-07-26 03:48:36.785764: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1996f40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-07-26 03:48:36.785792: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n",
            "2020-07-26 03:48:36.787157: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-07-26 03:48:36.787679: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla P100-PCIE-16GB computeCapability: 6.0\n",
            "coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 15.90GiB deviceMemoryBandwidth: 681.88GiB/s\n",
            "2020-07-26 03:48:36.787724: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-07-26 03:48:36.787763: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-07-26 03:48:36.787783: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-07-26 03:48:36.787803: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-07-26 03:48:36.787821: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-07-26 03:48:36.787837: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-07-26 03:48:36.787855: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-07-26 03:48:36.787921: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-07-26 03:48:36.788568: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-07-26 03:48:36.789109: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0\n",
            "2020-07-26 03:48:36.792379: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-07-26 03:48:43.215578: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-07-26 03:48:43.215643: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0 \n",
            "2020-07-26 03:48:43.215660: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N \n",
            "2020-07-26 03:48:43.221599: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-07-26 03:48:43.222278: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-07-26 03:48:43.222837: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2020-07-26 03:48:43.222900: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14974 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
            "2020-07-26 03:48:46.853593: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-07-26 03:48:48.339418: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "11/11 [==============================] - 0s 18ms/step\n",
            "CREPE: Saved the estimated frequencies and confidence values at /content/test.f0.csv\n",
            "CREPE: Saved the salience plot at /content/test.activation.png\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5XjgLWQJvl57",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "df352a65-b09a-43a0-ac23-174b3cfd7454"
      },
      "source": [
        "\n",
        "import crepe\n",
        "from scipy.io import wavfile\n",
        "\n",
        "sr, audio = wavfile.read('/content/test.wav')\n",
        "time, frequency, confidence, activation = crepe.predict(audio, sr, viterbi=False,step_size=40)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "11/11 [==============================] - 0s 18ms/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2jE2DUp4xDb0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "aebb9207-7771-41dc-880e-01a9c85720d8"
      },
      "source": [
        "def mapped(x):\n",
        "  return (x-minf)/(maxf-minf)\n",
        "\n",
        "minf = min(frequency)\n",
        "maxf = max(frequency)\n",
        "print(minf,maxf)\n",
        "\n",
        "for t in np.arange(len(time)):\n",
        "  print(mapped(frequency[t]))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "86.9345753117266 349.28126083128717\n",
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            "0.0\n",
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            "0.6600040560508963\n",
            "0.6766018306273026\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7HI3sZJNyfno",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "71a66824-736f-4384-dc38-a7b945627ea5"
      },
      "source": [
        "import numpy as np\n",
        "for t in np.arange(len(time)):\n",
        "  print(time[t], frequency[t], confidence[t])\n",
        "\n",
        "import csv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "12.24 220.27709992307865 0.36771446\n",
            "12.28 220.70622889230617 0.5064549\n",
            "12.32 222.84461218974056 0.35655075\n",
            "12.36 233.6731799183208 0.32444823\n",
            "12.4 257.03071418625564 0.5984411\n",
            "12.44 261.7523212969241 0.40337914\n",
            "12.48 261.5745007305359 0.7753132\n",
            "12.52 261.83600891205697 0.81372905\n",
            "12.56 262.4321217108891 0.5790878\n",
            "12.6 262.03062674234627 0.8596009\n",
            "12.64 261.96661064002984 0.7996155\n",
            "12.68 261.78130863587774 0.8173709\n",
            "12.72 261.58075015470183 0.6180425\n",
            "12.76 260.5939855442482 0.5565763\n",
            "12.8 262.7344399302109 0.134096\n",
            "12.84 260.0844518461455 0.80002004\n",
            "12.88 264.4388229932665 0.7667285\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9N4Tanu1yqdN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "outputId": "05f66cd4-2d63-43b2-edc6-3c14ae2add76"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(audio,'b')\n",
        "plt.show()\n",
        "plt.plot(frequency,'g')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "chiYgRQq5-cK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}