notebooks/TestAttention.ipynb

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                "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."
            ]
        },
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            "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.tts.layers import *\n",
                "from TTS.utils.audio import AudioProcessor\n",
                "from TTS.tts.utils.generic_utils import setup_model\n",
                "from TTS.tts.utils.io import load_config\n",
                "from TTS.tts.utils.text import text_to_sequence\n",
                "from TTS.tts.utils.synthesis import synthesis\n",
                "from TTS.tts.utils.visual import plot_alignment\n",
                "from TTS.tts.utils.measures import alignment_diagonal_score\n",
                "\n",
                "import IPython\n",
                "from IPython.display import Audio\n",
                "\n",
                "os.environ['CUDA_VISIBLE_DEVICES']='1'\n",
                "\n",
                "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, 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",
                "    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\n",
                "\n",
                "# Set constants\n",
                "ROOT_PATH = '/home/erogol/Models/LJSpeech/ljspeech-May-20-2020_12+29PM-1835628/'\n",
                "MODEL_PATH = ROOT_PATH + '/best_model.pth'\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,
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            "source": [
                "# LOAD TTS MODEL\n",
                "from TTS.tts.utils.text.symbols import make_symbols, 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",
                "# if the vocabulary was passed, replace the default\n",
                "if 'characters' in CONFIG.keys():\n",
                "    symbols, phonemes = make_symbols(**CONFIG.characters)\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,
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            "source": [
                "model.decoder.max_decoder_steps=3000\n",
                "attn_scores = []\n",
                "with open(SENTENCES_PATH, 'r') as f:\n",
                "    for text in f:\n",
                "        attn_score = tts(model, text, CONFIG, use_cuda, ap)\n",
                "        attn_scores.append(attn_score)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {
                "Collapsed": "false"
            },
            "outputs": [],
            "source": [
                "np.mean(attn_scores)"
            ]
        }
    ],
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