{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7bd11665",
   "metadata": {},
   "source": [
    "## Getting Started with Mayavoz\n",
    "\n",
    "#### Contents:\n",
    "- [How to do inference using pretrained model](#inference)\n",
    "- [How to train your custom model](#basictrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3c589bb",
   "metadata": {},
   "source": [
    "### Install Mayavoz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b68e053",
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install -q mayavoz "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87ee497f",
   "metadata": {},
   "source": [
    "<div id=\"inference\"></div>\n",
    "\n",
    "###  Pretrained Model\n",
    "\n",
    "To start using pretrained model,select any of the available recipes from [here](). \n",
    "For this exercice I am selecting [mayavoz/waveunet]()\n",
    "\n",
    "- Mayavoz supports multiple input and output format. Input for inference can be in any of the below format\n",
    "    - audio file path\n",
    "    - numpy audio data\n",
    "    - torch tensor audio data\n",
    "    \n",
    "It auto-detects the input format and does inference for you.\n",
    "    \n",
    "At the moment mayavoz only accepts single audio input"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd514ff4",
   "metadata": {},
   "source": [
    "**Load model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67698871",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from mayavoz import Mayamodel\n",
    "model = Mayamodel.from_pretrained(\"mayavoz/waveunet\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7fd4cbe",
   "metadata": {},
   "source": [
    "**Inference using file path**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7996c16",
   "metadata": {},
   "outputs": [],
   "source": [
    "file = \"myvoice.wav\"\n",
    "audio = model.enhance(\"myvoice.wav\")\n",
    "audio.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ee20a83",
   "metadata": {},
   "source": [
    "**Inference using torch tensor**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1a1c718",
   "metadata": {},
   "outputs": [],
   "source": [
    "audio_tensor = torch.rand(1,1,32000) ## random audio data\n",
    "audio = model.enhance(audio_tensor)\n",
    "audio.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ac27920",
   "metadata": {},
   "source": [
    "- if you want to save the output, just pass `save_output=True`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e0313f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "audio = model.enhance(\"myvoice.wav\",save_output=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25077720",
   "metadata": {},
   "outputs": [],
   "source": [
    "from Ipython.audio import Audio\n",
    "\n",
    "Audio(\"myvoice_cleaned.wav\",rate=SAMPLING_RATE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3170bb0b",
   "metadata": {},
   "source": [
    "<div id=\"basictrain\"></div>\n",
    "\n",
    "\n",
    "## Training your own custom Model\n",
    "\n",
    "There are two ways of doing this\n",
    "\n",
    "* [Using mayavoz framework ](#code)\n",
    "* [Using mayavoz command line tool ](#cli)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a44fc314",
   "metadata": {},
   "source": [
    "<div id=\"code\"></div>\n",
    "\n",
    "**Using Mayavoz framwork** [Basic]\n",
    "- Prepapare dataloader\n",
    "- import preferred model\n",
    "- Train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbc14b36",
   "metadata": {},
   "source": [
    "Files is dataclass that helps your to organise your train/test file paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c8c2b12",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mayavoz.utils import Files\n",
    "\n",
    "name = \"dataset_name\"\n",
    "root_dir = \"root_directory_of_your_dataset\"\n",
    "files = Files(train_clean=\"train_cleanfiles_foldername\",\n",
    "         train_noisy=\"noisy_train_foldername\",\n",
    "        test_clean=\"clean_test_foldername\",\n",
    "         test_noisy=\"noisy_test_foldername\")\n",
    "duration = 4.0 \n",
    "stride = None\n",
    "sampling_rate = 16000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07ef8721",
   "metadata": {},
   "source": [
    "Now there are two types of `matching_function`\n",
    "- `one_to_one` : In this one clean file will only have one corresponding noisy file. For example VCTK datasets\n",
    "- `one_to_many` : In this one clean file will only have one corresponding noisy file. For example DNS dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b0fdc62",
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping_function = \"one_to_one\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff0cfe60",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mayavoz.dataset import MayaDataset\n",
    "dataset = MayaDataset(\n",
    "            name=name,\n",
    "            root_dir=root_dir,\n",
    "            files=files,\n",
    "            duration=duration,\n",
    "            stride=stride,\n",
    "            sampling_rate=sampling_rate\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acfdc655",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mayavoz.models import Demucs\n",
    "model = Demucs(dataset=dataset, loss=\"mae\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4fabe46d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pytorch_lightning as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20d98ed0",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = pl.Trainer(model)\n",
    "trainer.fit(max_epochs=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28bc697b",
   "metadata": {},
   "source": [
    "**mayavoz model and dataset are highly customazibale**, see [here]() for advanced usage"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df01aa1e",
   "metadata": {},
   "source": [
    "<div id=\"cli\"></div>\n",
    "\n",
    "\n",
    "## Mayavoz CLI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bbf2747",
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install mayavoz[cli]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4447dd07",
   "metadata": {},
   "source": [
    "### TL;DR\n",
    "Calling the following command would train mayavoz Demucs model on DNS-2020 dataset.\n",
    "\n",
    "```bash\n",
    "mayavoz-train \\\n",
    "    model=Demucs \\\n",
    "    Demucs.sampling_rate=16000 \\\n",
    "    dataset=DNS-2020 \\\n",
    "    DNS-2020.name = \"dns-2020\" \\\n",
    "    DNS-2020.root_dir=\"your_root_dir\" \\\n",
    "    DNS-2020.train_clean=\"\" \\\n",
    "    DNS-2020.train_noisy=\"\" \\\n",
    "    DNS-2020.test_clean=\"\" \\\n",
    "    DNS-2020.test_noisy=\"\" \\\n",
    "    DNS-2020.sampling_rate=16000 \\\n",
    "    DNS-2020.duration=2.0 \\\n",
    "    traine=default \\ \n",
    "    default.max_epochs=1 \\\n",
    "\n",
    "```\n",
    "\n",
    "This is more or less equaivalent to below code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9278742a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mayavoz.utils import Files\n",
    "from mayavoz.data import MayaDataset\n",
    "from mayavoz.models import Demucs\n",
    "\n",
    "files = Files(\n",
    "        train_clean=\"\",\n",
    "        train_noisy=\"\",\n",
    "        test_clean=\"\",\n",
    "        test_noisy=\"\"\n",
    ")\n",
    "dataset = MayaDataset(\n",
    "                    name='dns-2020'\n",
    "                    root_dir=\"your_root_dir\",\n",
    "                    files=files,\n",
    "                    sampling_rate=16000,\n",
    "                    duration=2.0)\n",
    "model = Demucs(dataset=dataset,sampling_rate=16000)\n",
    "trainer = Trainer(max_epochs=1)\n",
    "trainer.fit(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb26692c",
   "metadata": {},
   "source": [
    "Hydra-based configuration\n",
    "mayavoz-train relies on Hydra to configure the training process. Adding --cfg job option to the previous command will let you know about the actual configuration used for training:\n",
    "\n",
    "```bash\n",
    "mayavoz-train --cfg job \\\n",
    "    model=Demucs \\\n",
    "    Demucs.sampling_rate=16000 \\\n",
    "    dataset=DNS-2020\n",
    "\n",
    "```\n",
    "\n",
    "```yaml\n",
    "_target_: mayavoz.models.demucs.Demucs\n",
    "num_channels: 1\n",
    "resample: 4\n",
    "sampling_rate : 16000\n",
    "\n",
    "encoder_decoder:\n",
    "  depth: 4\n",
    "  initial_output_channels: 64\n",
    "  \n",
    "[...]\n",
    "```\n",
    "\n",
    "To change the sampling_rate, you can \n",
    "\n",
    "```bash\n",
    "mayavoz-train \\\n",
    "    model=Demucs model.sampling_rate=16000 \\\n",
    "    dataset=DNS-2020\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93555860",
   "metadata": {},
   "source": []
  }
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