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Fix import in CheckSpectrogram.ipynb

This commit is contained in:
Eren Gölge 2021-08-17 11:47:44 +00:00
parent 3fd78241f8
commit 625ab614f0
1 changed files with 82 additions and 80 deletions
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162
notebooks/dataset_analysis/CheckSpectrograms.ipynb
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@ -3,28 +3,24 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [ "source": [
"%matplotlib inline\n", "%matplotlib inline\n",
"\n", "\n",
"from TTS.utils.audio import AudioProcessor\n", "from TTS.utils.audio import AudioProcessor\n",
"from TTS.tts.utils.visual import plot_spectrogram\n", "from TTS.tts.utils.visual import plot_spectrogram\n",
"from TTS.utils.io import load_config\n", "from TTS.config import load_config\n",
"\n", "\n",
"import IPython.display as ipd\n", "import IPython.display as ipd\n",
"import glob" "import glob"
] ],
"outputs": [],
"metadata": {
"Collapsed": "false"
}
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [ "source": [
"config_path = \"/home/erogol/gdrive/Projects/TTS/recipes/ljspeech/align_tts/config_transformer2.json\"\n", "config_path = \"/home/erogol/gdrive/Projects/TTS/recipes/ljspeech/align_tts/config_transformer2.json\"\n",
"data_path = \"/home/erogol/gdrive/Datasets/LJSpeech-1.1/\"\n", "data_path = \"/home/erogol/gdrive/Datasets/LJSpeech-1.1/\"\n",
@ -39,28 +35,28 @@
"\n", "\n",
"print(\"File list, by index:\")\n", "print(\"File list, by index:\")\n",
"dict(enumerate(file_paths))" "dict(enumerate(file_paths))"
] ],
"outputs": [],
"metadata": {
"Collapsed": "false"
}
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [ "source": [
"### Setup Audio Processor\n", "### Setup Audio Processor\n",
"Play with the AP parameters until you find a good fit with the synthesis speech below.\n", "Play with the AP parameters until you find a good fit with the synthesis speech below.\n",
"\n", "\n",
"The default values are loaded from your config.json file, so you only need to\n", "The default values are loaded from your config.json file, so you only need to\n",
"uncomment and modify values below that you'd like to tune." "uncomment and modify values below that you'd like to tune."
] ],
"metadata": {
"Collapsed": "false"
}
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [ "source": [
"tune_params={\n", "tune_params={\n",
"# 'audio_processor': 'audio',\n", "# 'audio_processor': 'audio',\n",
@ -95,54 +91,54 @@
"tuned_config.update(tune_params)\n", "tuned_config.update(tune_params)\n",
"\n", "\n",
"AP = AudioProcessor(**tuned_config);" "AP = AudioProcessor(**tuned_config);"
] ],
"outputs": [],
"metadata": {
"Collapsed": "false"
}
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [ "source": [
"### Check audio loading " "### Check audio loading "
] ],
"metadata": {
"Collapsed": "false"
}
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [ "source": [
"wav = AP.load_wav(SAMPLE_FILE_PATH)\n", "wav = AP.load_wav(SAMPLE_FILE_PATH)\n",
"ipd.Audio(data=wav, rate=AP.sample_rate) " "ipd.Audio(data=wav, rate=AP.sample_rate) "
] ],
"outputs": [],
"metadata": {
"Collapsed": "false"
}
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [ "source": [
"### Generate Mel-Spectrogram and Re-synthesis with GL" "### Generate Mel-Spectrogram and Re-synthesis with GL"
] ],
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"AP.power = 1.5"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": { "metadata": {
"Collapsed": "false" "Collapsed": "false"
}, }
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"AP.power = 1.5"
],
"outputs": [], "outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [ "source": [
"mel = AP.melspectrogram(wav)\n", "mel = AP.melspectrogram(wav)\n",
"print(\"Max:\", mel.max())\n", "print(\"Max:\", mel.max())\n",
@ -152,24 +148,24 @@
"\n", "\n",
"wav_gen = AP.inv_melspectrogram(mel)\n", "wav_gen = AP.inv_melspectrogram(mel)\n",
"ipd.Audio(wav_gen, rate=AP.sample_rate)" "ipd.Audio(wav_gen, rate=AP.sample_rate)"
] ],
"outputs": [],
"metadata": {
"Collapsed": "false"
}
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [ "source": [
"### Generate Linear-Spectrogram and Re-synthesis with GL" "### Generate Linear-Spectrogram and Re-synthesis with GL"
] ],
"metadata": {
"Collapsed": "false"
}
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [ "source": [
"spec = AP.spectrogram(wav)\n", "spec = AP.spectrogram(wav)\n",
"print(\"Max:\", spec.max())\n", "print(\"Max:\", spec.max())\n",
@ -179,26 +175,26 @@
"\n", "\n",
"wav_gen = AP.inv_spectrogram(spec)\n", "wav_gen = AP.inv_spectrogram(spec)\n",
"ipd.Audio(wav_gen, rate=AP.sample_rate)" "ipd.Audio(wav_gen, rate=AP.sample_rate)"
] ],
"outputs": [],
"metadata": {
"Collapsed": "false"
}
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {
"Collapsed": "false"
},
"source": [ "source": [
"### Compare values for a certain parameter\n", "### Compare values for a certain parameter\n",
"\n", "\n",
"Optimize your parameters by comparing different values per parameter at a time." "Optimize your parameters by comparing different values per parameter at a time."
] ],
"metadata": {
"Collapsed": "false"
}
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [ "source": [
"from librosa import display\n", "from librosa import display\n",
"from matplotlib import pylab as plt\n", "from matplotlib import pylab as plt\n",
@ -238,36 +234,39 @@
" val = values[idx]\n", " val = values[idx]\n",
" print(\" > {} = {}\".format(attribute, val))\n", " print(\" > {} = {}\".format(attribute, val))\n",
" IPython.display.display(IPython.display.Audio(wav_gen, rate=AP.sample_rate))" " IPython.display.display(IPython.display.Audio(wav_gen, rate=AP.sample_rate))"
] ],
"outputs": [],
"metadata": {
"Collapsed": "false"
}
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [ "source": [
"compare_values(\"preemphasis\", [0, 0.5, 0.97, 0.98, 0.99])" "compare_values(\"preemphasis\", [0, 0.5, 0.97, 0.98, 0.99])"
] ],
"outputs": [],
"metadata": {
"Collapsed": "false"
}
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {
"Collapsed": "false"
},
"outputs": [],
"source": [ "source": [
"compare_values(\"ref_level_db\", [2, 5, 10, 15, 20, 25, 30, 35, 40, 1000])" "compare_values(\"ref_level_db\", [2, 5, 10, 15, 20, 25, 30, 35, 40, 1000])"
] ],
"outputs": [],
"metadata": {
"Collapsed": "false"
}
} }
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "name": "python3",
"language": "python", "display_name": "Python 3.8.5 64-bit ('torch': conda)"
"name": "python3"
}, },
"language_info": { "language_info": {
"codemirror_mode": { "codemirror_mode": {
@ -280,8 +279,11 @@
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.8.5" "version": "3.8.5"
},
"interpreter": {
"hash": "27648abe09795c3a768a281b31f7524fcf66a207e733f8ecda3a4e1fd1059fb0"
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 4 "nbformat_minor": 4
} }