notebooks/DDC_TTS_and_MultiBand_MelGAN_Example.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Mozilla TTS on CPU Real-Time Speech Synthesis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FAqrSIWgLyP0"
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   "source": [
    "We use Tacotron2 and MultiBand-Melgan models and LJSpeech dataset.\n",
    "\n",
    "Tacotron2 is trained using [Double Decoder Consistency](https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency/) (DDC) only for 130K steps (3 days) with a single GPU.\n",
    "\n",
    "MultiBand-Melgan is trained  1.45M steps with real spectrograms.\n",
    "\n",
    "Note that both model performances can be improved with more training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ku-dA4DKoeXk"
   },
   "source": [
    "### Download Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "outputId": "88725e41-a8dc-4885-b3bf-cac939f38abe",
    "tags": []
   },
   "outputs": [],
   "source": [
    "!gdown --id 1dntzjWFg7ufWaTaFy80nRz-Tu02xWZos -O data/tts_model.pth.tar\n",
    "!gdown --id 18CQ6G6tBEOfvCHlPqP8EBI4xWbrr9dBc -O data/config.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "!gdown --id 1Ty5DZdOc0F7OTGj9oJThYbL5iVu_2G0K -O data/vocoder_model.pth.tar\n",
    "!gdown --id 1Rd0R_nRCrbjEdpOwq6XwZAktvugiBvmu -O data/config_vocoder.json\n",
    "!gdown --id 11oY3Tv0kQtxK_JPgxrfesa99maVXHNxU -O data/scale_stats.npy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Zlgi8fPdpRF0"
   },
   "source": [
    "### Define TTS function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "source": [
    "def tts(model, text, CONFIG, use_cuda, ap, use_gl, figures=True):\n",
    "    t_1 = time.time()\n",
    "    waveform, alignment, mel_spec, mel_postnet_spec, stop_tokens, inputs = synthesis(model, text, CONFIG, use_cuda, ap, speaker_id, style_wav=None,\n",
    "                                                                             truncated=False, enable_eos_bos_chars=CONFIG.enable_eos_bos_chars)\n",
    "    # mel_postnet_spec = ap.denormalize(mel_postnet_spec.T)\n",
    "    if not use_gl:\n",
    "        waveform = vocoder_model.inference(torch.FloatTensor(mel_postnet_spec.T).unsqueeze(0))\n",
    "        waveform = waveform.flatten()\n",
    "    if use_cuda:\n",
    "        waveform = waveform.cpu()\n",
    "    waveform = waveform.numpy()\n",
    "    rtf = (time.time() - t_1) / (len(waveform) / ap.sample_rate)\n",
    "    tps = (time.time() - t_1) / len(waveform)\n",
    "    print(waveform.shape)\n",
    "    print(\" > Run-time: {}\".format(time.time() - t_1))\n",
    "    print(\" > Real-time factor: {}\".format(rtf))\n",
    "    print(\" > Time per step: {}\".format(tps))\n",
    "    IPython.display.display(IPython.display.Audio(waveform, rate=CONFIG.audio['sample_rate']))  \n",
    "    return alignment, mel_postnet_spec, stop_tokens, waveform"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZksegYQepkFg"
   },
   "source": [
    "### Load Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oVa0kOamprgj"
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   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import time\n",
    "import IPython\n",
    "\n",
    "from TTS.tts.utils.generic_utils import setup_model\n",
    "from TTS.utils.io import load_config\n",
    "from TTS.tts.utils.text.symbols import symbols, phonemes\n",
    "from TTS.utils.audio import AudioProcessor\n",
    "from TTS.tts.utils.synthesis import synthesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EY-sHVO8IFSH"
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   "outputs": [],
   "source": [
    "# runtime settings\n",
    "use_cuda = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_1aIUp2FpxOQ"
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   "outputs": [],
   "source": [
    "# model paths\n",
    "TTS_MODEL = \"data/tts_model.pth.tar\"\n",
    "TTS_CONFIG = \"data/config.json\"\n",
    "VOCODER_MODEL = \"data/vocoder_model.pth.tar\"\n",
    "VOCODER_CONFIG = \"data/config_vocoder.json\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CpgmdBVQplbv"
   },
   "outputs": [],
   "source": [
    "# load configs\n",
    "TTS_CONFIG = load_config(TTS_CONFIG)\n",
    "VOCODER_CONFIG = load_config(VOCODER_CONFIG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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    },
    "colab_type": "code",
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    "outputId": "60c4daa0-4c5b-4a2e-fe0d-be437d003a49",
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   "outputs": [],
   "source": [
    "# load the audio processor\n",
    "TTS_CONFIG.audio['stats_path'] = 'data/scale_stats.npy'\n",
    "ap = AudioProcessor(**TTS_CONFIG.audio)         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "8fLoI4ipqMeS",
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   "outputs": [],
   "source": [
    "# LOAD TTS MODEL\n",
    "# multi speaker \n",
    "speaker_id = None\n",
    "speakers = []\n",
    "\n",
    "# load the model\n",
    "num_chars = len(phonemes) if TTS_CONFIG.use_phonemes else len(symbols)\n",
    "model = setup_model(num_chars, len(speakers), TTS_CONFIG)\n",
    "\n",
    "# load model state\n",
    "cp =  torch.load(TTS_MODEL, map_location=torch.device('cpu'))\n",
    "\n",
    "# load the model\n",
    "model.load_state_dict(cp['model'])\n",
    "if use_cuda:\n",
    "    model.cuda()\n",
    "model.eval()\n",
    "\n",
    "# set model stepsize\n",
    "if 'r' in cp:\n",
    "    model.decoder.set_r(cp['r'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "zKoq0GgzqzhQ",
    "outputId": "234efc61-f37a-40bc-95a3-b51896018ccb",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from TTS.vocoder.utils.generic_utils import setup_generator\n",
    "\n",
    "# LOAD VOCODER MODEL\n",
    "vocoder_model = setup_generator(VOCODER_CONFIG)\n",
    "vocoder_model.load_state_dict(torch.load(VOCODER_MODEL, map_location=\"cpu\")[\"model\"])\n",
    "vocoder_model.remove_weight_norm()\n",
    "vocoder_model.inference_padding = 0\n",
    "\n",
    "ap_vocoder = AudioProcessor(**VOCODER_CONFIG['audio'])    \n",
    "if use_cuda:\n",
    "    vocoder_model.cuda()\n",
    "vocoder_model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ws_YkPKsLgo-"
   },
   "source": [
    "## Run Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "outputId": "9c06adad-5451-4393-89a1-a2e7dc39ab91",
    "tags": []
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   "outputs": [],
   "source": [
    "sentence =  \"Bill got in the habit of asking himself “Is that thought true?” and if he wasn’t absolutely certain it was, he just let it go.\"\n",
    "align, spec, stop_tokens, wav = tts(model, sentence, TTS_CONFIG, use_cuda, ap, use_gl=False, figures=True)"
   ]
  }
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