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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "from tts.utils.audio import AudioProcessor\n",
+ "from tts.tts.utils.visual import plot_spectrogram\n",
+ "from tts.utils.io import load_config\n",
+ "import glob "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "outputs": [],
+ "source": [
+ "config_path = \"/home/erogol/Projects/TTS/tts/tts/config_thorsten_de.json\"\n",
+ "data_path = \"/home/erogol/Data/thorsten-german/\"\n",
+ "file_paths = glob.glob(data_path + \"/**/*.wav\", recursive=True)\n",
+ "CONFIG = load_config(config_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "source": [
+ "### Setup Audio Processor\n",
+ "Play with the AP parameters until you find a good fit with the synthesis speech below. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " > Setting up Audio Processor...\n",
+ " | > sample_rate:22050\n",
+ " | > num_mels:80\n",
+ " | > min_level_db:-100\n",
+ " | > frame_shift_ms:None\n",
+ " | > frame_length_ms:None\n",
+ " | > ref_level_db:20\n",
+ " | > fft_size:1024\n",
+ " | > power:1.5\n",
+ " | > preemphasis:0.0\n",
+ " | > griffin_lim_iters:60\n",
+ " | > signal_norm:True\n",
+ " | > symmetric_norm:True\n",
+ " | > mel_fmin:0\n",
+ " | > mel_fmax:8000.0\n",
+ " | > spec_gain:1.0\n",
+ " | > stft_pad_mode:reflect\n",
+ " | > max_norm:4.0\n",
+ " | > clip_norm:True\n",
+ " | > do_trim_silence:True\n",
+ " | > trim_db:60\n",
+ " | > do_sound_norm:True\n",
+ " | > stats_path:None\n",
+ " | > hop_length:256\n",
+ " | > win_length:1024\n"
+ ]
+ }
+ ],
+ "source": [
+ "# audio={\n",
+ "# 'audio_processor': 'audio',\n",
+ "# 'num_mels': 80, # In general, you don'tneed to change it \n",
+ "# 'fft_size': 1024, # In general, you don'tneed to change it \n",
+ "# 'sample_rate': 22050, # It depends to the sample rate of the dataset.\n",
+ "# 'hop_length': 256, # In general, you don'tneed to change it \n",
+ "# 'win_length': 1024, # In general, you don'tneed to change it \n",
+ "# 'preemphasis': 0.98, # In general, 0 gives better voice recovery but makes traning harder. If your model does not train, try 0.97 - 0.99.\n",
+ "# 'min_level_db': -100,\n",
+ "# 'ref_level_db': 20, # It is the base DB, higher until you remove the background noise in the spectrogram and then lower until you hear a better speech below.\n",
+ "# 'power': 1.5, # Change this value and listen the synthesized voice. 1.2 - 1.5 are some resonable values.\n",
+ "# 'griffin_lim_iters': 60, # It does not give any imporvement for values > 60\n",
+ "# 'signal_norm': True, # This is more about your model. It does not give any change for the synthsis performance.\n",
+ "# 'symmetric_norm': False, # Same as above\n",
+ "# 'max_norm': 1, # Same as above\n",
+ "# 'clip_norm': True, # Same as above\n",
+ "# 'mel_fmin': 0.0, # You can play with this and check mel-spectrogram based voice synthesis below.\n",
+ "# 'mel_fmax': 8000.0, # You can play with this and check mel-spectrogram based voice synthesis below.\n",
+ "# 'do_trim_silence': True} # If you dataset has some silience at the beginning or end, this trims it. Check the AP.load_wav() below,if it causes any difference for the loaded audio file.\n",
+ "\n",
+ "AP = AudioProcessor(**CONFIG.audio);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "source": [
+ "### Check audio loading "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "wav = AP.load_wav(file_paths[10])\n",
+ "ipd.Audio(data=wav, rate=AP.sample_rate) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "source": [
+ "### Generate Mel-Spectrogram and Re-synthesis with GL"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "AP.power = 1.0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Max: 2.4340844\n",
+ "Min: 2.0181823\n",
+ "Mean: 2.2137265\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mel = AP.melspectrogram(wav)\n",
+ "print(\"Max:\", mel.max())\n",
+ "print(\"Min:\", mel.min())\n",
+ "print(\"Mean:\", mel.mean())\n",
+ "plot_spectrogram(mel.T, AP);\n",
+ "\n",
+ "wav_gen = AP.inv_melspectrogram(mel)\n",
+ "ipd.Audio(wav_gen, rate=AP.sample_rate)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "source": [
+ "### Generate Linear-Spectrogram and Re-synthesis with GL"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "outputs": [
+ {
+ "ename": "RuntimeError",
+ "evalue": " [!] Mean-Var stats does not match the given feature dimensions.",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mspec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAP\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspectrogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwav\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Max:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Min:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Mean:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mplot_spectrogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAP\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/Projects/TTS/tts/utils/audio.py\u001b[0m in \u001b[0;36mspectrogram\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m 218\u001b[0m \u001b[0mD\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stft\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0mS\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_amp_to_db\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_normalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 221\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmelspectrogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/Projects/TTS/tts/utils/audio.py\u001b[0m in \u001b[0;36m_normalize\u001b[0;34m(self, S)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear_scaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' [!] Mean-Var stats does not match the given feature dimensions.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 120\u001b[0m \u001b[0;31m# range normalization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0mS\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mref_level_db\u001b[0m \u001b[0;31m# discard certain range of DB assuming it is air noise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mRuntimeError\u001b[0m: [!] Mean-Var stats does not match the given feature dimensions."
+ ]
+ }
+ ],
+ "source": [
+ "spec = AP.spectrogram(wav)\n",
+ "print(\"Max:\", spec.max())\n",
+ "print(\"Min:\", spec.min())\n",
+ "print(\"Mean:\", spec.mean())\n",
+ "plot_spectrogram(spec.T, AP);\n",
+ "\n",
+ "wav_gen = AP.inv_spectrogram(spec)\n",
+ "ipd.Audio(wav_gen, rate=AP.sample_rate)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "source": [
+ "### Compare values for a certain parameter\n",
+ "\n",
+ "Optimize your parameters by comparing different values per parameter at a time."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "outputs": [],
+ "source": [
+ "audio={\n",
+ " 'audio_processor': 'audio',\n",
+ " 'num_mels': 80, # In general, you don'tneed to change it \n",
+ " 'num_freq': 1025, # In general, you don'tneed to change it \n",
+ " 'sample_rate': 22050, # It depends to the sample rate of the dataset.\n",
+ " 'frame_length_ms': 50, # In general, you don'tneed to change it \n",
+ " 'frame_shift_ms': 12.5, # In general, you don'tneed to change it \n",
+ " 'preemphasis': 0.98, # In general, 0 gives better voice recovery but makes traning harder. If your model does not train, try 0.97 - 0.99.\n",
+ " 'min_level_db': -100,\n",
+ " 'ref_level_db': 20, # It is the base DB, higher until you remove the background noise in the spectrogram and then lower until you hear a better speech below.\n",
+ " 'power': 1.5, # Change this value and listen the synthesized voice. 1.2 - 1.5 are some resonable values.\n",
+ " 'griffin_lim_iters': 60, # It does not give any imporvement for values > 60\n",
+ " 'signal_norm': True, # This is more about your model. It does not give any change for the synthsis performance.\n",
+ " 'symmetric_norm': False, # Same as above\n",
+ " 'max_norm': 1, # Same as above\n",
+ " 'clip_norm': True, # Same as above\n",
+ " 'mel_fmin': 0.0, # You can play with this and check mel-spectrogram based voice synthesis below.\n",
+ " 'mel_fmax': 8000.0, # You can play with this and check mel-spectrogram based voice synthesis below.\n",
+ " 'do_trim_silence': True} # If you dataset has some silience at the beginning or end, this trims it. Check the AP.load_wav() below,if it causes any difference for the loaded audio file.\n",
+ "\n",
+ "AP = AudioProcessor(**audio);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "outputs": [],
+ "source": [
+ "from librosa import display\n",
+ "from matplotlib import pylab as plt\n",
+ "import IPython\n",
+ "plt.rcParams['figure.figsize'] = (20.0, 16.0)\n",
+ "\n",
+ "def compare_values(attribute, values, file):\n",
+ " \"\"\"\n",
+ " attributes (str): the names of the attribute you like to test.\n",
+ " values (list): list of values to compare.\n",
+ " file (str): file name to perform the tests.\n",
+ " \"\"\"\n",
+ " wavs = []\n",
+ " for idx, val in enumerate(values):\n",
+ " set_val_cmd = \"AP.{}={}\".format(attribute, val)\n",
+ " exec(set_val_cmd)\n",
+ " wav = AP.load_wav(file)\n",
+ " spec = AP.spectrogram(wav)\n",
+ " spec_norm = AP._denormalize(spec.T)\n",
+ " plt.subplot(len(values), 2, 2*idx + 1)\n",
+ " plt.imshow(spec_norm.T, aspect=\"auto\", origin=\"lower\")\n",
+ " # plt.colorbar()\n",
+ " plt.tight_layout()\n",
+ " wav_gen = AP.inv_spectrogram(spec)\n",
+ " wavs.append(wav_gen)\n",
+ " plt.subplot(len(values), 2, 2*idx + 2)\n",
+ " display.waveplot(wav, alpha=0.5)\n",
+ " display.waveplot(wav_gen, alpha=0.25)\n",
+ " plt.title(\"{}={}\".format(attribute, val))\n",
+ " plt.tight_layout()\n",
+ " \n",
+ " wav = AP.load_wav(file)\n",
+ " print(\" > Ground-truth\")\n",
+ " IPython.display.display(IPython.display.Audio(wav, rate=AP.sample_rate))\n",
+ " \n",
+ " for idx, wav_gen in enumerate(wavs):\n",
+ " val = values[idx]\n",
+ " print(\" > {} = {}\".format(attribute, val))\n",
+ " IPython.display.display(IPython.display.Audio(wav_gen, rate=AP.sample_rate))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "outputs": [],
+ "source": [
+ "compare_values(\"preemphasis\", [0, 0.5, 0.97, 0.98, 0.99], file_paths[10])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "Collapsed": "false"
+ },
+ "outputs": [],
+ "source": [
+ "compare_values(\"ref_level_db\", [10, 15, 20, 25, 30, 35, 40], file_paths[10])"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}