update notebook

notebooks/TestAttention.ipynb

@@ -2,15 +2,22 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "Collapsed": "false"
+   },
    "source": [
-    "This notebook is to test attention performance on hard sentences taken from DeepVoice paper."
+    "This notebook is to test attention performance of a TTS model on a list of sentences taken from DeepVoice paper.\n",
+    "### Features of this notebook\n",
+    "- You can see visually how your model performs on each sentence and try to dicern common problems.\n",
+    "- At the end, final attention score would be printed showing the ultimate performace of your model. You can use this value to perform model selection.\n",
+    "- You can change the list of sentences byt providing a different sentence file."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
+    "Collapsed": "false",
     "scrolled": true
    },
    "outputs": [],
@@ -31,7 +38,8 @@
     "\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.generic_utils import setup_model\n",
+    "from TTS.utils.io import load_config\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",
@@ -45,7 +53,7 @@
     "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, None, False, CONFIG.enable_eos_bos_chars, True)\n",
+    "    waveform, alignment, mel_spec, mel_postnet_spec, stop_tokens, inputs = synthesis(model, text, CONFIG, use_cuda, ap, speaker_id, None, False, CONFIG.enable_eos_bos_chars, True)\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",
@@ -62,7 +70,7 @@
     "    return attn_score\n",
     "\n",
     "# Set constants\n",
-    "ROOT_PATH = '/home/erogol/Models/LJSpeech/ljspeech-March-17-2020_01+16AM-871588c/'\n",
+    "ROOT_PATH = '/home/erogol/Models/LJSpeech/ljspeech-May-20-2020_12+29PM-1835628/'\n",
     "MODEL_PATH = ROOT_PATH + '/best_model.pth.tar'\n",
     "CONFIG_PATH = ROOT_PATH + '/config.json'\n",
     "OUT_FOLDER = './hard_sentences/'\n",
@@ -82,7 +90,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "Collapsed": "false"
+   },
    "outputs": [],
    "source": [
     "# LOAD TTS MODEL\n",
@@ -130,7 +140,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "Collapsed": "false"
+   },
    "outputs": [],
    "source": [
     "model.decoder.max_decoder_steps=3000\n",
@@ -144,7 +156,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "Collapsed": "false"
+   },
    "outputs": [],
    "source": [
     "np.mean(attn_scores)"