griffin_lim_synthesis_tool.ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "import numpy as np\n",
    "from datasets.audio import *\n",
    "import os\n",
    "from hparams import hparams\n",
    "\n",
    "n_sample = 0 #Change n_steps here\n",
    "mel_folder = 'logs-Tacotron/mel-spectrograms' #Or change file path\n",
    "mel_file = 'mel-prediction-step-{}.npy'.format(n_sample) #Or file name (for other generated mels)\n",
    "out_dir = 'wav_out'\n",
    "\n",
    "os.makedirs(out_dir, exist_ok=True)\n",
    "\n",
    "#mel_file = os.path.join(mel_folder, mel_file)\n",
    "mel_file = 'training_data/mels/mel-LJ001-0001.npy'\n",
    "mel_spectro = np.load(mel_file)\n",
    "mel_spectro.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wav = inv_mel_spectrogram(mel_spectro.T, hparams) \n",
    "#save the wav under test_<folder>_<file>\n",
    "save_wav(wav, os.path.join(out_dir, 'test_mel_{}.wav'.format(mel_file.replace('/', '_').replace('\\\\', '_').replace('.npy', ''))),\n",
    "        sr=hparams.sample_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tacotron.utils.plot import *\n",
    "\n",
    "plot_spectrogram(mel_spectro, path=os.path.join(out_dir, 'test_mel_{}.png'.format(mel_file.replace('/', '_').replace('\\\\', '_').replace('.npy', ''))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_file = 'training_data/linear/linear-LJ001-0001.npy'\n",
    "lin_spectro = np.load(lin_file)\n",
    "lin_spectro.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wav = inv_linear_spectrogram(lin_spectro.T, hparams)\n",
    "save_wav(wav, os.path.join(out_dir, 'test_linear_{}.wav'.format(mel_file.replace('/', '_').replace('\\\\', '_').replace('.npy', ''))),\n",
    "        sr=hparams.sample_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_spectrogram(lin_spectro, path=os.path.join(out_dir, 'test_linear_{}.png'.format(mel_file.replace('/', '_').replace('\\\\', '_').replace('.npy', ''))),\n",
    "                auto_aspect=True)"
   ]
  }
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