TTS/notebooks/TestAttention.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is to test attention performance on hard sentences taken from DeepVoice paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import os, sys\n",
    "import torch \n",
    "import time\n",
    "import numpy as np\n",
    "from matplotlib import pylab as plt\n",
    "\n",
    "%pylab inline\n",
    "plt.rcParams[\"figure.figsize\"] = (16,5)\n",
    "\n",
    "import librosa\n",
    "import librosa.display\n",
    "\n",
    "from TTS.layers import *\n",
    "from TTS.utils.audio import AudioProcessor\n",
    "from TTS.utils.generic_utils import load_config, setup_model\n",
    "from TTS.utils.text import text_to_sequence\n",
    "from TTS.utils.synthesis import synthesis\n",
    "from TTS.utils.visual import plot_alignment\n",
    "from TTS.utils.measures import alignment_diagonal_score\n",
    "\n",
    "import IPython\n",
    "from IPython.display import Audio\n",
    "\n",
    "os.environ['CUDA_VISIBLE_DEVICES']='2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tts(model, text, CONFIG, use_cuda, ap):\n",
    "    t_1 = time.time()\n",
    "    # run the model\n",
    "    waveform, alignment, mel_spec, mel_postnet_spec, stop_tokens = synthesis(model, text, CONFIG, use_cuda, ap, speaker_id, False, CONFIG.enable_eos_bos_chars)\n",
    "    if CONFIG.model == \"Tacotron\" and not use_gl:\n",
    "        mel_postnet_spec = ap.out_linear_to_mel(mel_postnet_spec.T).T\n",
    "    # plotting\n",
    "    attn_score = alignment_diagonal_score(torch.FloatTensor(alignment).unsqueeze(0))\n",
    "    print(f\" > {text}\")\n",
    "    IPython.display.display(IPython.display.Audio(waveform, rate=ap.sample_rate))\n",
    "    fig = plot_alignment(alignment, fig_size=(8, 5))\n",
    "    IPython.display.display(fig)\n",
    "    #saving results\n",
    "    os.makedirs(OUT_FOLDER, exist_ok=True)\n",
    "    file_name = text[:200].replace(\" \", \"_\").replace(\".\",\"\") + \".wav\"\n",
    "    out_path = os.path.join(OUT_FOLDER, file_name)\n",
    "    ap.save_wav(waveform, out_path)\n",
    "    return attn_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set constants\n",
    "ROOT_PATH = '/data/rw/pit/keep/ljspeech-December-11-2019_04+32PM-ca49ae8/'\n",
    "MODEL_PATH = ROOT_PATH + '/checkpoint_410000.pth.tar'\n",
    "CONFIG_PATH = ROOT_PATH + '/config.json'\n",
    "OUT_FOLDER = './hard_sentences/'\n",
    "CONFIG = load_config(CONFIG_PATH)\n",
    "SENTENCES_PATH = 'sentences.txt'\n",
    "use_cuda = True\n",
    "\n",
    "# Set some config fields manually for testing\n",
    "# CONFIG.windowing = False\n",
    "# CONFIG.prenet_dropout = False\n",
    "# CONFIG.separate_stopnet = True\n",
    "CONFIG.use_forward_attn = False\n",
    "# CONFIG.forward_attn_mask = True\n",
    "# CONFIG.stopnet = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LOAD TTS MODEL\n",
    "from TTS.utils.text.symbols import symbols, phonemes\n",
    "\n",
    "# multi speaker \n",
    "if CONFIG.use_speaker_embedding:\n",
    "    speakers = json.load(open(f\"{ROOT_PATH}/speakers.json\", 'r'))\n",
    "    speakers_idx_to_id = {v: k for k, v in speakers.items()}\n",
    "else:\n",
    "    speakers = []\n",
    "    speaker_id = None\n",
    "\n",
    "# load the model\n",
    "num_chars = len(phonemes) if CONFIG.use_phonemes else len(symbols)\n",
    "model = setup_model(num_chars, len(speakers), CONFIG)\n",
    "\n",
    "# load the audio processor\n",
    "ap = AudioProcessor(**CONFIG.audio)         \n",
    "\n",
    "\n",
    "# load model state\n",
    "if use_cuda:\n",
    "    cp = torch.load(MODEL_PATH)\n",
    "else:\n",
    "    cp = torch.load(MODEL_PATH, map_location=lambda storage, loc: storage)\n",
    "\n",
    "# load the model\n",
    "model.load_state_dict(cp['model'])\n",
    "if use_cuda:\n",
    "    model.cuda()\n",
    "model.eval()\n",
    "print(cp['step'])\n",
    "print(cp['r'])\n",
    "\n",
    "# set model stepsize\n",
    "if 'r' in cp:\n",
    "    model.decoder.set_r(cp['r'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.decoder.max_decoder_steps=3000\n",
    "model.decoder.prenet.train()\n",
    "attn_scores = []\n",
    "with open(SENTENCES_PATH, 'r') as f:\n",
    "    for text in f:\n",
    "        try:\n",
    "            attn_score = tts(model, text, CONFIG, use_cuda, ap)\n",
    "        except ValueError:\n",
    "            attn_score = 0\n",
    "        attn_scores.append(attn_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean(attn_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean(attn_scores)"
   ]
  }
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