tacotron2/inference.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tacotron 2 inference code \n",
    "Edit the variables **checkpoint_path** and **text** to match yours and run the entire code to generate plots of mel outputs, alignments and audio synthesis from the generated mel-spectrogram using Griffin-Lim."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import libraries and setup matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dcg-adlr-rafaelvalle-source.cosmos597/repos/nvidia/tacotron2/plotting_utils.py:2: UserWarning: matplotlib.pyplot as already been imported, this call will have no effect.\n",
      "  matplotlib.use(\"Agg\")\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.use(\"Agg\")\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "import IPython.display as ipd\n",
    "\n",
    "import sys\n",
    "sys.path.append('waveglow/')\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "from hparams import create_hparams\n",
    "from model import Tacotron2\n",
    "from layers import TacotronSTFT\n",
    "from audio_processing import griffin_lim\n",
    "from train import load_model\n",
    "from text import text_to_sequence\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_data(data, figsize=(16, 4)):\n",
    "    fig, axes = plt.subplots(1, len(data), figsize=figsize)\n",
    "    for i in range(len(data)):\n",
    "        axes[i].imshow(data[i], aspect='auto', origin='bottom', \n",
    "                       interpolation='none')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Setup hparams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Parsing command line hparams: distributed_run=False,mask_padding=False\n"
     ]
    }
   ],
   "source": [
    "hparams = create_hparams(\"distributed_run=False,mask_padding=False\")\n",
    "hparams.sampling_rate = 22050\n",
    "hparams.filter_length = 1024\n",
    "hparams.hop_length = 256\n",
    "hparams.win_length = 1024"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load model from checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_path = \"tacotron2_statedict\"\n",
    "\n",
    "model = load_model(hparams)\n",
    "try:\n",
    "    model = model.module\n",
    "except:\n",
    "    pass\n",
    "model.load_state_dict({k.replace('module.',''):v for k,v in torch.load(checkpoint_path)['state_dict'].items()})\n",
    "_ = model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load WaveGlow for mel2audio synthesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/torch/serialization.py:425: SourceChangeWarning: source code of class 'glow_old.WaveGlow' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
      "  warnings.warn(msg, SourceChangeWarning)\n",
      "/opt/conda/lib/python3.6/site-packages/torch/serialization.py:425: SourceChangeWarning: source code of class 'glow_old.WN' has changed. you can retrieve the original source code by accessing the object's source attribute or set `torch.nn.Module.dump_patches = True` and use the patch tool to revert the changes.\n",
      "  warnings.warn(msg, SourceChangeWarning)\n"
     ]
    }
   ],
   "source": [
    "waveglow_path = 'waveglow_old.pt'\n",
    "waveglow = torch.load(waveglow_path)['model']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare text input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"Waveglow is really awesome!\"\n",
    "sequence = np.array(text_to_sequence(text, ['english_cleaners']))[None, :]\n",
    "sequence = torch.autograd.Variable(\n",
    "    torch.from_numpy(sequence)).cuda().long()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decode text input and plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1152x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence)\n",
    "plot_data((mel_outputs.data.cpu().numpy()[0],\n",
    "           mel_outputs_postnet.data.cpu().numpy()[0],\n",
    "           alignments.data.cpu().numpy()[0].T))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Synthesize audio from spectrogram using WaveGlow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio controls=\"controls\" >\n",
       "                    <source src=\"data:audio/wav;base64,UklGRiRaAQBXQVZFZm10IBAAAAABAAEAIlYAAESsAAACABAAZGF0YQBaAQATABEAFQAaAAwACwANAAYAAAADAPj/8v/u/97/5v/k/9n/0f/U/9f/0//L/9T/zv/L/8n/y//W/9j/1//j/+j/5f/k/+z/7v/5//7/CQAEAPr//P8AAAoAAgAAAAoACgD8//j/+P/6//7/9v8CAPf/7v/u/+//8f/t/+b/7P/i/9n/zP/L/9T/1P/K/8//z//H/8D/vv/H/8v/z//S/9j/0v/W/+D/6v/3//b/EQANAAwAFwAjACsALAAzADQAMgAnAB4AGgAbAB0AEgATAAcA+//1//n//v/8/wAABwAAAAMA/f8DAAoAEQAbACEALAAoACUAJwAjACoALAAtACUAIAAOAAsAEgAMAA4ADQAPAAYA+P8EAAAA+f/7/wUAAAAFAPz/BwAKAAQAEgAiACsAJwArAC0AMQAxADMAMAAmAB8ABwALAAQA9v/z//T/9//r/+P/7v/s/+7/7f/8//j/9P/u//P/9v/5//3/8//y/+7/5P/n/+f/4//d/+P/6f/a/83/1//a/93/5P/t/+r/4v/f/+L/1P/V/9X/4f/o/+D/2v/j/+3/8v8DAAcAFAAdABYAGAAnADYAMwBGAE8AUABLAEwAVgBSAE4ASgBMADsAIgAiACEADAAAABQADQD6/+3/5P/o/+X/3//j/+D/0v/O/9D/x//G/77/xP/F/7P/nv+f/6L/kv+K/5L/jv+O/5P/mf+j/5n/ov+v/7X/vf+7/8H/zv/a/+X/8f8BABQAFwApADQAPwBEAFcAXwBcAGMAbACHAIwAmAClALgAzQDMANEA1ADcAOYA7QDzAAEB+AD+AAYB/wAEAQkB/wD3AN8A2gDZAMcAtwCoAJEAfwB2AGUAWgA4ACYADQDn/83/kv94/1T/Iv/9/tD+p/5q/jj+C/7f/az9dv1R/Rr97fzB/KX8hfxl/FT8Rfw4/Cj8Ifwk/Cr8LvxB/Fj8bPyh/MX8/fxF/Y/93P0u/ob+2/41/5v/CgBxAN0AVAHAATQCowIQA5AD/QNyBPIEYAXQBUIGtgYdB4QH6QdLCJgI5QgiCV0JgwmUCZQJfwlVCSAJtAhOCNYHOgedBvYFLAVaBIUDqALBAdEA1P/s/vr9/vwd/DT7XvqM+cH4APhG96r2FvaW9Sb1wPRe9Cz0BvTo8+Tz8fMX9FP0q/Qn9aD1IvbI9nv3N/jt+Kv5dfpB+wP8xvx//TT+1/5v/wwAgQDpAEQBmgHtASYCPQJSAl0CUAJPAksCQQInAioCOgJQAokCxQI1A7MDVAQYBfAF3wbOB9sI6wkLCxAM9QzQDXcO8A5AD1oPQg/nDkQOgA2QDHQLIAqzCBwHhgXkA1kC0gBY/+79kfxr+176dfmz+B/4r/dd9yT3B/f99gz3Pvdu96T32/cj+HD4pPjh+CL5WfmX+df5B/o++nb6pfrr+iX7Vft7+6T72vsH/Cv8Tfxj/Gb8Yvxz/H78b/xT/Ej8Mvwc/Pv71/u4+5f7k/uN+4z7iPuO+6772fsH/ET8nfwM/Xf9+v2o/m7/NAAaATECWAOYBM0FJQeMCPMJWQvEDA8ORw9vEHoRbRIgE6cTABQBFMoTYhPFEukR0hB8DwUOVwyGCoIIhAaGBI4CpgDR/gv9Yfvi+az4rffQ9h72t/WD9Wb1gPW19QP2a/bS9kn3p/fq9zb4cPi1+PH4Dvk5+UX5Tvlv+YP5pfnH+eD5Ivpe+pD63vom+4D7vvvx+yX8Nfw3/CX8/vu9+3D7AvuP+in6o/kl+bv4T/jf95n3efdm94X3rfca+Lv4Xvk4+mH7yPw0/sj/kAFhAzkFOwciCRAL5wyeDkYQvhECEykUGRW/FTwWXRZ2FiEWlhXPFNATnhI+EakPAg47DEMKVQhjBmQEYQJxAJr+4/w6+7b5dfhb91v2f/XQ9Ez06fOl85LzoPO98/XzQvSg9AL1ifUY9sT2cvcb+Of4xvmX+lP7Kvzo/H39Df6E/s7+Bv8l/yj/D//L/m3+Fv6q/Sz9svxB/PH7nftf+zr7Fvv6+t/6yfqv+m36MfrV+Vv50/hU+Mz3X/f99sj2/vZs91f4lvkX+9z84v41AeEDhwYlCdMLgw4eEZkToxWTFzAZZRpZG+cb/xusG/0a2hlGGGEWLxS5ERsPVAx+CdcGBwRUAcf+dPxv+qf4GffO9cr09fNI8/7ytvJ78nbyaPJ48onyoPLB8vbyFfNx89vzSPS99Ej1Fvb59gv4Kflv+sX7Iv11/qf/vwCQARQCgwKaAlcCxwH6APr/5P7w/en87vvf+vn5X/nd+IH4Ofjo96H3UPft9nf2yvUN9T/0pPP18o/yNPJs8kbzb/Q49kr4xPoG/tQB4QVgCmIOvxIZFxgb5R7kIV4kaCaeJysoDyjWJkUl2SIqIPgcMRkdFdcQhAw/CBoEEgB0/Nr4lvXu8pXwvu467QjsZ+sZ6zTroesR7J3sWu0u7gPvx+9R8ODwafHr8ZLyMvMK9AH1NPbB92X5QvtL/Uv/QQEWA7kELwYqB8IH2Qd9B5oGMAVnA2YBNP/6/ML6zfgn9831yvQc9O7zBvRl9N30d/XW9Uf2cfZf9iX2kvUQ9Y/0CfTl85H0Z/V/9835oPyBAOYEJgqfDxgVIBpSH0ckqCgXLIQu/i/JMJAw9i5pLLcouyRUIIYbThYdEbkL6wZjAi3+TfrU9oTzcvCs7QnrwOiT5qDk8eK64b3gJ+Dv3yvg++Bl4kbkb+a+6GvrVu6T8fD0DfgD+8T9JgCQArUEegYACCEJMQoGC6EL2QvJC2EL3gr4CawI7gabBCAChv/X/B/6Zfe69IryrfA+70Tul+2B7Yvt7e1q7sfuOu9/753vwu8S8JfwSvGI8ezyF/Wr+HH9RQKsB7sNtRR0HA4kKCpuMFg1BDpvPaA+2T00PIg5RDagMbUrMCWDHioY6RENDPcFHgCU+nz1rfBG7MbnoeOp3yvcSdnw1pjU+tIe0kjS1dMF1ovY2dur30nku+nz7vTztvhE/aYBrAW0CAELtgw8DjcPAhAaEOwPmg8zDwEPbg4bDYULswl6ByMFxQGz/Q359/SK8MPspujf5NrhfeCA30Xg6OCC4nXkbOZY6TPrSe4U7xPx+fDK8xr0sfdQ96v7WgCKCBASmBm5Ie4o0TSUPJJH50rdT7xQp1KET+JL9ER+PcA2oS7/JZIckhNiCagD5/sC+LjxDuza5C3f6dma1R3RWss+xr/Be7+9vR2+x76FwhTINs8Q1/7eX+ff8KD6oAOIC7QQ2xSfF7cZIRs8G0EaPRnKF4wWYRXhE/QSgBIYEpQRoQ91DBsI7AJm/TD3QvAz6dvhHdva1fPRzs8sz//PxtKU1gLbR9+m40znrusJ7jnwA/Hc8Vfy5/Jj8yr2Rv8eCE0WLyAOK1M2d0S/URNdumJKZItkxmIEX+xWcU7RQvE6JTIYKXoe2RRMCx0GOQB9+frxeOh033HXys+ox2TAIrdCsQOsHauzqymueLI5uSDDKs6Z2CHi2+td9VsAuQjmDt0StxXdF9kaChxEHV4eFB91ICciyyJpI8kikSBEHrAZiRNtC1IBr/fQ7pbmvt4R1/7Qscwjy23Ltsw3z4vSttUj2tvcR+Hp49bl6Oal6Fnppe0F8EHxk/Se90L/YwcpG+AleThvQ+xIxVHSXkJk0WjGaAdebV56WUlSPkdAQLs41TnRNbksSx+3FYkK9QFc9qPm9Ni+yRG9ZbEQqsalAqRBoXSj2KbPrsC2/Lz1w5rNrNfU4InmQutO83r8CweTDSwTnhhGH1wm+CqdLsMwIjJRMH0tBSggI78dQRiPEYIKBQSF+83zquw158nhPN1T1jzRo82+y8/KwMuszY7QM9TA1lnazdvF4Ufljux07sbxNPIs9MD2IvyrAPQIlh5iKgRAYktYT9paaGbWaRluHGmEYHxd+FhOTw9HEUGlPG46JTX+KO0YbQ2F/2v1pueF2c/J7rshsU6pEqZEpLa
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    audio = waveglow.infer(mel_outputs_postnet, sigma=0.666)\n",
    "ipd.Audio(audio[0].data.cpu().numpy(), rate=hparams.sampling_rate)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}