{
"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": [
"\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": 3,
"id": "67698871",
"metadata": {},
"outputs": [],
"source": [
"\n",
"from mayavoz.models import Mayamodel\n",
"model = Mayamodel.from_pretrained(\"shahules786/mayavoz-dccrn-valentini-28spk\")\n"
]
},
{
"cell_type": "markdown",
"id": "c7fd4cbe",
"metadata": {},
"source": [
"**Inference using file path**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "d7996c16",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 1, 36414])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"audio = model.enhance(\"my_voice.wav\")\n",
"audio.shape"
]
},
{
"cell_type": "markdown",
"id": "8ee20a83",
"metadata": {},
"source": [
"**Inference using numpy ndarray**\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "e1a1c718",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(36414,)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import torch\n",
"from librosa import load\n",
"my_voice,sr = load(\"my_voice.wav\",sr=16000)\n",
"my_voice.shape"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0cbef6c0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1, 1, 36414)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"audio = model.enhance(my_voice,sampling_rate=sr)\n",
"audio.shape"
]
},
{
"cell_type": "markdown",
"id": "a22fc10f",
"metadata": {},
"source": [
"**Inference using torch tensor**\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "a884a935",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 1, 36414])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_voice = torch.from_numpy(my_voice)\n",
"audio = model.enhance(my_voice,sampling_rate=sr)\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": 11,
"id": "9e0313f7",
"metadata": {},
"outputs": [],
"source": [
"audio = model.enhance(\"my_voice.wav\",save_output=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "25077720",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" \n",
" "
],
"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Audio\n",
"SAMPLING_RATE = 16000\n",
"Audio(\"cleaned_my_voice.wav\",rate=SAMPLING_RATE)"
]
},
{
"cell_type": "markdown",
"id": "3170bb0b",
"metadata": {},
"source": [
"\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": [
"\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": 8,
"id": "2c8c2b12",
"metadata": {},
"outputs": [],
"source": [
"from mayavoz.utils import Files\n",
"\n",
"name = \"valentini\"\n",
"root_dir = \"/Users/shahules/Myprojects/enhancer/datasets/vctk\"\n",
"files = Files(train_clean=\"clean_testset_wav\",\n",
" train_noisy=\"clean_testset_wav\",\n",
" test_clean=\"noisy_testset_wav\",\n",
" test_noisy=\"noisy_testset_wav\")\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 Valentini 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": 9,
"id": "4b0fdc62",
"metadata": {},
"outputs": [],
"source": [
"mapping_function = \"one_to_one\"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "ff0cfe60",
"metadata": {},
"outputs": [],
"source": [
"from mayavoz.data 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",
" min_valid_minutes = 5.0,\n",
" )\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "acfdc655",
"metadata": {},
"outputs": [],
"source": [
"from mayavoz.models import Demucs\n",
"model = Demucs(dataset=dataset, loss=\"mae\")\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "4fabe46d",
"metadata": {},
"outputs": [],
"source": [
"import pytorch_lightning as pl"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "20d98ed0",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"GPU available: False, used: False\n",
"TPU available: False, using: 0 TPU cores\n",
"IPU available: False, using: 0 IPUs\n",
"HPU available: False, using: 0 HPUs\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Selected fp257 for valid\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
" | Name | Type | Params\n",
"----------------------------------------\n",
"0 | _loss | LossWrapper | 0 \n",
"1 | encoder | ModuleList | 4.7 M \n",
"2 | decoder | ModuleList | 4.7 M \n",
"3 | de_lstm | DemucsLSTM | 24.8 M\n",
"----------------------------------------\n",
"34.2 M Trainable params\n",
"0 Non-trainable params\n",
"34.2 M Total params\n",
"136.866 Total estimated model params size (MB)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total train duration 27.4 minutes\n",
"Total validation duration 29.733333333333334 minutes\n",
"Total test duration 57.2 minutes\n",
"Epoch 0: 48%|▍| 13/27 [15:18<16:29, 70.66s/it, loss=0.0265, v_num=2, train_loss\n",
"Validation: 0it [00:00, ?it/s]\u001b[A\n",
"Validation: 0%| | 0/14 [00:00\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=VCTK dataset.root_dir = \"your_root_directory\" \\\n",
" trainer=fastrun_dev\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.data import MayaDataset\n",
"from mayavoz.models import Demucs\n",
"\n",
"dataset = MayaDataset(\n",
" name='vctk'\n",
" root_dir=\"your_root_directory\",\n",
" )\n",
"model = Demucs(dataset=dataset, sampling_rate=16000)\n",
"trainer = Trainer()\n",
"trainer.fit(model)"
]
},
{
"cell_type": "markdown",
"id": "15737128",
"metadata": {},
"source": [
"For example, if you want to add/change `stride` of dataset\n",
"\n",
"```bash\n",
"mayavoz-train \\\n",
" model=Demucs \\\n",
" Demucs.sampling_rate=16000 \\\n",
" dataset=VCTK dataset.root_dir = \"your_root_directory\" dataset.stride=1\\\n",
"\n",
"```"
]
},
{
"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",
"```"
]
}
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