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log grad norm on tf
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Eren Golge committed
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@@ -1,285 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import sys\n",
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"import io\n",
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"import torch \n",
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"import numpy as np\n",
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"from collections import OrderedDict\n",
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"\n",
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"%pylab inline\n",
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"rcParams[\"figure.figsize\"] = (16,5)\n",
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"\n",
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"import librosa\n",
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"import librosa.display\n",
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"\n",
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"from TTS.models.tacotron import Tacotron \n",
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"from TTS.layers import *\n",
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"from TTS.utils.data import *\n",
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"from TTS.utils.audio import AudioProcessor\n",
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"from TTS.utils.generic_utils import load_config\n",
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"from TTS.utils.text import text_to_sequence\n",
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"\n",
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"import IPython\n",
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"from IPython.display import Audio"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def create_speech(m, s):\n",
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" text_cleaner = [CONFIG.text_cleaner]\n",
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" seq = np.array(text_to_sequence(s, text_cleaner))\n",
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" \n",
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"# mel = np.zeros([seq.shape[0], CONFIG.num_mels, 1], dtype=np.float32)\n",
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" \n",
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" if use_cuda:\n",
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" chars_var = torch.autograd.Variable(torch.from_numpy(seq), volatile=True).unsqueeze(0).cuda()\n",
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"# mel_var = torch.autograd.Variable(torch.from_numpy(mel).type(torch.cuda.FloatTensor), volatile=True).cuda() \n",
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" else:\n",
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" chars_var = torch.autograd.Variable(torch.from_numpy(seq), volatile=True).unsqueeze(0)\n",
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"# mel_var = torch.autograd.Variable(torch.from_numpy(mel).type(torch.FloatTensor), volatile=True)\n",
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" \n",
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" mel_out, linear_out, alignments = model.forward(chars_var)\n",
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" linear_out = linear_out[0].data.cpu().numpy()\n",
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" alignment = alignments[0].cpu().data.numpy()\n",
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" spec = ap._denormalize(linear_out)\n",
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" \n",
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" wav = ap.inv_spectrogram(linear_out.T)\n",
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" wav = wav[:ap.find_endpoint(wav)]\n",
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" out = io.BytesIO()\n",
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" ap.save_wav(wav, out)\n",
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" return wav, alignment, spec\n",
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"\n",
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"\n",
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"def visualize(alignment, spectrogram):\n",
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" label_fontsize = 16\n",
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" figure(figsize=(16,16))\n",
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" \n",
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" subplot(2,1,1)\n",
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" imshow(alignment.T, aspect=\"auto\", origin=\"lower\", interpolation=None)\n",
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" xlabel(\"Decoder timestamp\", fontsize=label_fontsize)\n",
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" ylabel(\"Encoder timestamp\", fontsize=label_fontsize)\n",
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" colorbar()\n",
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"\n",
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" subplot(2,1,2)\n",
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" librosa.display.specshow(spectrogram.T, sr=CONFIG.sample_rate, \n",
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" hop_length=hop_length, x_axis=\"time\", y_axis=\"linear\")\n",
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" xlabel(\"Time\", fontsize=label_fontsize)\n",
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" ylabel(\"Hz\", fontsize=label_fontsize)\n",
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" tight_layout()\n",
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" colorbar()\n",
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" \n",
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" \n",
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"def tts(model, text, figures=True):\n",
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" waveform, alignment, spectrogram = create_speech(model, text)\n",
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" if figures:\n",
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" visualize(alignment, spectrogram)\n",
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" IPython.display.display(Audio(waveform, rate=CONFIG.sample_rate))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"MODEL_PATH = '../result/January-26-2018_02:44AM/checkpoint_1800.pth.tar'\n",
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"CONFIG_PATH = '../result/January-26-2018_02:44AM/config.json'\n",
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"OUT_FOLDER = '../result/January-26-2018_02:44AM/test/'\n",
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"CONFIG = load_config(CONFIG_PATH)\n",
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"use_cuda = False\n",
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"hop_length = 250"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# load the model\n",
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"model = Tacotron(CONFIG.embedding_size, CONFIG.hidden_size,\n",
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" CONFIG.num_mels, CONFIG.num_freq, CONFIG.r)\n",
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"ap = AudioProcessor(CONFIG.sample_rate, CONFIG.num_mels, CONFIG.min_level_db,\n",
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" CONFIG.frame_shift_ms, CONFIG.frame_length_ms, CONFIG.preemphasis,\n",
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" CONFIG.ref_level_db, CONFIG.num_freq, CONFIG.power, griffin_lim_iters=80) \n",
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"\n",
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"if use_cuda:\n",
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" model = torch.nn.DataParallel(model.cuda())\n",
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"else:\n",
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" cp = torch.load(MODEL_PATH, map_location=lambda storage, loc: storage)\n",
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" \n",
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" # remove DataPatallel wrapper\n",
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" new_state_dict = OrderedDict()\n",
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" for k, v in cp['model'].items():\n",
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" name = k[7:] # remove `module.`\n",
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" new_state_dict[name] = v\n",
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" cp['model'] = new_state_dict"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"model.load_state_dict(cp['model'])\n",
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"# model.decoder.eval();\n",
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"model.encoder.eval();\n",
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"model.postnet.eval();"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"sentences = [\n",
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" \"I try to speak my friend.\",\n",
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" \"I speak more than binary any more.\",\n",
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" \"I try ti implement a new TTS system.\"\n",
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"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Warning! doesn't seems to be converged\n"
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]
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},
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{
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"data": {
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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7f5aeb66bb38>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"tts(model, sentences[2])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# os.makedirs(OUT_FOLDER, exist_ok=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# wav, alignments, spec = create_speech(model, sentences[1])\n",
|
||||
"# out_path = os.path.join(OUT_FOLDER, 'speec_{}.wav'.format(1))\n",
|
||||
"# with open(out_path, \"wb\") as f:\n",
|
||||
"# f.write(wav)\n",
|
||||
"# print(\" > Speech saved : {}\".format(out_path))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# import IPython\n",
|
||||
"# import glob\n",
|
||||
"\n",
|
||||
"# # wav_files = glob.glob(OUT_FOLDER+'/**/*.wav', recursive=True)\n",
|
||||
"# # assert len(wav_files) > 0\n",
|
||||
"# # IPython.display.Audio(wav_files[1])\n",
|
||||
"# IPython.display.display(IPython.display.Audio(wav, rate=CONFIG.sample_rate))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"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": "3.6.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
+60
-61
@@ -2,15 +2,26 @@
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"The autoreload extension is already loaded. To reload it, use:\n",
|
||||
" %reload_ext autoreload\n",
|
||||
"Populating the interactive namespace from numpy and matplotlib\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/erogol/miniconda3/envs/pytorch/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['plt']\n",
|
||||
"`%matplotlib` prevents importing * from pylab and numpy\n",
|
||||
" \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
@@ -44,8 +55,10 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 34,
|
||||
"metadata": {},
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def tts(model, text, CONFIG, use_cuda, ap, figures=True): \n",
|
||||
@@ -58,25 +71,12 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "FileNotFoundError",
|
||||
"evalue": "[Errno 2] No such file or directory: '../result/January-26-2018_09:10AM//config.json'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-35-e538c7c7bdac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mCONFIG_PATH\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mROOT_PATH\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/config.json'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mOUT_FOLDER\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mROOT_PATH\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/test/'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mCONFIG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCONFIG_PATH\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0muse_cuda\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/projects/TTS/utils/generic_utils.py\u001b[0m in \u001b[0;36mload_config\u001b[0;34m(config_path)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mload_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0mconfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAttrDict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"r\"\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[0m\u001b[1;32m 21\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../result/January-26-2018_09:10AM//config.json'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ROOT_PATH = '../result/January-26-2018_09:10AM/'\n",
|
||||
"MODEL_PATH = ROOT_PATH + '/checkpoint_27600.pth.tar'\n",
|
||||
"ROOT_PATH = '../result/January-31-2018_08:39AM/'\n",
|
||||
"MODEL_PATH = ROOT_PATH + '/checkpoint_6800.pth.tar'\n",
|
||||
"CONFIG_PATH = ROOT_PATH + '/config.json'\n",
|
||||
"OUT_FOLDER = ROOT_PATH + '/test/'\n",
|
||||
"CONFIG = load_config(CONFIG_PATH)\n",
|
||||
@@ -85,24 +85,9 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "TypeError",
|
||||
"evalue": "super(type, obj): obj must be an instance or subtype of type",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[0;32m<ipython-input-36-bde7da5375c4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# load the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m model = Tacotron(CONFIG.embedding_size, CONFIG.hidden_size,\n\u001b[0;32m----> 3\u001b[0;31m CONFIG.num_mels, CONFIG.num_freq, CONFIG.r)\n\u001b[0m\u001b[1;32m 4\u001b[0m ap = AudioProcessor(CONFIG.sample_rate, CONFIG.num_mels, CONFIG.min_level_db,\n\u001b[1;32m 5\u001b[0m \u001b[0mCONFIG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframe_shift_ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCONFIG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframe_length_ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCONFIG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreemphasis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/projects/TTS/models/tacotron.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, embedding_dim, linear_dim, mel_dim, freq_dim, r, padding_idx, use_memory_mask)\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormal_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEncoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membedding_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecoder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDecoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmel_dim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpostnet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCBHG\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmel_dim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mK\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprojections\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmel_dim\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/layers/tacotron.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, memory_dim, r)\u001b[0m\n\u001b[1;32m 157\u001b[0m self.attention_rnn = AttentionWrapper(\n\u001b[1;32m 158\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGRUCell\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m256\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m256\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 159\u001b[0;31m \u001b[0mBahdanauAttention\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 160\u001b[0m )\n\u001b[1;32m 161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m~/projects/TTS/layers/attention.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, dim)\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mBahdanauAttention\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBahdanauAttention\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\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 10\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquery_layer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtanh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTanh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;31mTypeError\u001b[0m: super(type, obj): obj must be an instance or subtype of type"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# load the model\n",
|
||||
"model = Tacotron(CONFIG.embedding_size, CONFIG.hidden_size,\n",
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@@ -126,8 +111,10 @@
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},
|
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{
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"cell_type": "code",
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"execution_count": 37,
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"metadata": {},
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"execution_count": 18,
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"metadata": {
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"collapsed": true
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},
|
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"outputs": [],
|
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"source": [
|
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"model.load_state_dict(cp['model'])\n",
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@@ -139,7 +126,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"execution_count": 19,
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"metadata": {
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"collapsed": true
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},
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@@ -154,8 +141,10 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"execution_count": 20,
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"metadata": {
|
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"collapsed": true
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},
|
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"outputs": [],
|
||||
"source": [
|
||||
"# tts(model, sentences[2], CONFIG, use_cuda, ap)"
|
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@@ -163,7 +152,7 @@
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},
|
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{
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"cell_type": "code",
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"execution_count": 39,
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"execution_count": 21,
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"metadata": {},
|
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"outputs": [
|
||||
{
|
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@@ -182,7 +171,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"execution_count": 22,
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"metadata": {
|
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"scrolled": false
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},
|
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@@ -192,7 +181,7 @@
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <audio controls=\"controls\" >\n",
|
||||
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|
||||
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" Your browser does not support the audio element.\n",
|
||||
" </audio>\n",
|
||||
" "
|
||||
@@ -213,9 +202,9 @@
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||||
},
|
||||
{
|
||||
"data": {
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||||
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truncated
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||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7f583fbee160>"
|
||||
"<matplotlib.figure.Figure at 0x7f81308483c8>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -229,7 +218,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -238,7 +227,7 @@
|
||||
"-100.0"
|
||||
]
|
||||
},
|
||||
"execution_count": 46,
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -249,7 +238,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 49,
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -258,7 +247,7 @@
|
||||
"0.0"
|
||||
]
|
||||
},
|
||||
"execution_count": 49,
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@@ -269,18 +258,16 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 39,
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 1.], dtype=float32)"
|
||||
]
|
||||
},
|
||||
"execution_count": 39,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
"ename": "SyntaxError",
|
||||
"evalue": "invalid syntax (<ipython-input-25-0c2c00d6dc20>, line 2)",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-25-0c2c00d6dc20>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m ling)\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
@@ -290,7 +277,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
@@ -329,6 +316,18 @@
|
||||
"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": "3.6.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -43,14 +43,12 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Constants\n",
|
||||
"ROOT_PATH = '../result/January-26-2018_09:10AM/'\n",
|
||||
"ROOT_PATH = '../result/January-31-2018_08:39AM//'\n",
|
||||
"MODEL_PATH = ROOT_PATH + '/checkpoint_27600.pth.tar'\n",
|
||||
"CONFIG_PATH = ROOT_PATH + '/config.json'\n",
|
||||
"OUT_FOLDER = ROOT_PATH + '/test/'\n",
|
||||
@@ -61,7 +59,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -81,8 +79,10 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 48,
|
||||
"metadata": {},
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ap = AudioProcessor(CONFIG.sample_rate, CONFIG.num_mels, CONFIG.min_level_db,\n",
|
||||
@@ -107,8 +107,10 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"file_name = df.iloc[1,0] \n",
|
||||
@@ -118,14 +120,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 54,
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7fcd254a4c88>"
|
||||
"<matplotlib.figure.Figure at 0x7fed4021ab00>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@@ -140,7 +142,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 47,
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@@ -167,14 +169,14 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 49,
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAABAUAAAI4CAYAAAASzVA9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7fcd1d97b8d0>"
|
||||
"<matplotlib.figure.Figure at 0x7feccd7230f0>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
|
||||
@@ -174,12 +174,14 @@ def main(args):
|
||||
('mel_loss', mel_loss.data[0]),
|
||||
('grad_norm', grad_norm)])
|
||||
|
||||
tb.add_scalar('Train/TotalLoss', loss.data[0], current_step)
|
||||
tb.add_scalar('Train/LinearLoss', linear_loss.data[0],
|
||||
tb.add_scalar('Loss/TotalLoss', loss.data[0], current_step)
|
||||
tb.add_scalar('Loss/LinearLoss', linear_loss.data[0],
|
||||
current_step)
|
||||
tb.add_scalar('Train/MelLoss', mel_loss.data[0], current_step)
|
||||
tb.add_scalar('LearningRate', optimizer.param_groups[0]['lr'],
|
||||
tb.add_scalar('Loss/MelLoss', mel_loss.data[0], current_step)
|
||||
|
||||
tb.add_scalar('Params/LearningRate', optimizer.param_groups[0]['lr'],
|
||||
current_step)
|
||||
tb.add_scalar('Params/GradNorm', grad_norm, current_step)
|
||||
tb.add_scalar('Time/StepTime', step_time, current_step)
|
||||
|
||||
|
||||
|
||||
Reference in new issue
Block a user