mirror of https://github.com/coqui-ai/TTS.git
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1.8 MiB
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In [1]:
%load_ext autoreload %autoreload 2 import os import sys import io import torch import time import numpy as np from collections import OrderedDict %pylab inline rcParams["figure.figsize"] = (16,5) sys.path.append('/home/erogol/projects/') import librosa import librosa.display from TTS.models.tacotron import Tacotron from TTS.layers import * from TTS.utils.data import * from TTS.utils.audio import AudioProcessor from TTS.utils.generic_utils import load_config from TTS.utils.text import text_to_sequence import IPython from IPython.display import Audio from utils import *
Populating the interactive namespace from numpy and matplotlib
In [2]:
def tts(model, text, CONFIG, use_cuda, ap, figures=True): t_1 = time.time() waveform, alignment, spectrogram = create_speech(model, text, CONFIG, use_cuda, ap) print(" > Run-time: {}".format(time.time() - t_1)) if figures: visualize(alignment, spectrogram, CONFIG) IPython.display.display(Audio(waveform, rate=CONFIG.sample_rate)) return alignment, spectrogram
In [3]:
# Set constants ROOT_PATH = '../result/February-13-2018_01:04AM/' MODEL_PATH = ROOT_PATH + '/best_model.pth.tar' CONFIG_PATH = ROOT_PATH + '/config.json' OUT_FOLDER = ROOT_PATH + '/test/' CONFIG = load_config(CONFIG_PATH) use_cuda = False
In [5]:
# load the model model = Tacotron(CONFIG.embedding_size, CONFIG.hidden_size, CONFIG.num_mels, CONFIG.num_freq, CONFIG.r) # load the audio processor ap = AudioProcessor(CONFIG.sample_rate, CONFIG.num_mels, CONFIG.min_level_db, CONFIG.frame_shift_ms, CONFIG.frame_length_ms, CONFIG.preemphasis, CONFIG.ref_level_db, CONFIG.num_freq, CONFIG.power, griffin_lim_iters=80) # load model state if use_cuda: cp = torch.load(MODEL_PATH) else: cp = torch.load(MODEL_PATH, map_location=lambda storage, loc: storage) # # small trick to remove DataParallel wrapper new_state_dict = OrderedDict() for k, v in cp['model'].items(): name = k[7:] # remove `module.` new_state_dict[name] = v cp['model'] = new_state_dict # load the model model.load_state_dict(cp['model']) if use_cuda: model.cuda() model.eval()
| > Embedding dim : 149
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-5-994bc6f7ae61> in <module>() 23 24 # load the model ---> 25 model.load_state_dict(cp['model']) 26 if use_cuda: 27 model.cuda() ~/miniconda3/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/module.py in load_state_dict(self, state_dict, strict) 488 elif strict: 489 raise KeyError('unexpected key "{}" in state_dict' --> 490 .format(name)) 491 if strict: 492 missing = set(own_state.keys()) - set(state_dict.keys()) KeyError: 'unexpected key "module.embedding.weight" in state_dict'
In [6]:
cp['model']
Out[6]:
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( 0 ,.,.) =
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-1.9679e+00 3.3696e-01
⋮
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( 1 ,.,.) =
-7.0265e-02 -4.4727e-02
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⋮
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5.3568e-02 1.9476e-01
( 2 ,.,.) =
1.2149e-02 -6.5031e-02
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⋮
2.2661e-01 -1.4156e-01
1.0049e-01 3.1395e-03
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...
(125,.,.) =
-4.5840e-02 -3.6793e-02
1.6884e-01 -4.0931e-01
2.5989e-01 2.7923e-01
⋮
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1.5648e-01 -6.1283e-02
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(126,.,.) =
-6.2937e-02 4.4589e-02
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2.1476e-01 -3.5696e-01
⋮
2.1135e-01 -5.8720e-01
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2.3508e-01 -8.5545e-03
(127,.,.) =
1.0879e-01 -3.1740e-02
4.4000e-03 -1.4642e+00
3.1502e-01 2.8231e-01
⋮
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⋮
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( 1 ,.,.) =
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⋮
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( 2 ,.,.) =
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⋮
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1.2165e-01 -2.6676e-02 -4.5937e-02
...
(125,.,.) =
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⋮
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2.8011e-01 -4.7094e-02 2.2105e-01
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(126,.,.) =
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1.4140e-01 1.4029e-01 -1.3311e-01
⋮
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2.2137e-01 -2.6558e-02 -3.1574e-02
1.0015e-02 -1.2476e-01 -3.1886e-02
(127,.,.) =
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3.4954e-01 -6.2865e-02 4.2429e-01
⋮
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[torch.FloatTensor of size 128]),
('module.encoder.cbhg.conv1d_banks.3.conv1d.weight',
( 0 ,.,.) =
-3.0199e-02 -4.5101e-02 4.1657e-02 -1.3989e-01
5.9655e-02 1.3077e-01 -1.0970e+00 9.7011e-03
-1.3781e+00 2.7077e-01 -1.9742e-02 2.1801e-02
⋮
-9.1431e-02 1.5480e-01 2.4212e-02 7.3165e-02
1.4958e-01 -4.2327e-01 1.9488e-01 -6.6253e-01
-1.3541e-01 -2.3292e-01 -3.2272e-01 -1.7024e-02
( 1 ,.,.) =
3.2595e-02 -3.2977e-03 4.1706e-02 -1.3661e-03
-1.0077e+00 -2.9712e-01 -4.7499e-02 -5.7860e-01
2.7742e-01 5.4497e-02 2.4373e-01 -1.1944e+00
⋮
-7.5219e-01 5.6820e-02 -4.2962e-01 -2.7082e-01
6.5423e-02 2.2855e-01 -7.5106e-02 2.1938e-01
-1.2268e-01 -2.7853e-02 8.2643e-02 -2.9314e-01
( 2 ,.,.) =
-3.8005e-02 -1.8723e-02 9.9610e-03 -1.8004e-02
2.8541e-01 -4.0136e-01 -6.4734e-01 -9.4345e-01
-4.9899e-03 1.0887e-01 -5.8836e-02 -5.9108e-01
⋮
-5.1846e-01 4.7479e-03 -4.8246e-01 -1.0629e-01
8.4392e-02 9.2139e-02 -5.7263e-02 -2.2137e-01
-2.2947e-01 -8.1368e-02 -1.8130e-01 1.4157e-01
...
(125,.,.) =
-1.9266e-02 -5.8194e-03 -5.9193e-02 1.2790e-02
7.3518e-02 1.8488e-01 1.2557e-01 1.2247e-01
-1.3630e-01 5.6454e-02 1.3659e-01 -6.8306e-02
⋮
4.1640e-01 -1.0616e-01 -2.5067e-01 1.5842e-01
-7.1185e-02 1.9634e-01 -1.7411e-01 2.7764e-01
3.3945e-03 -3.4820e-03 8.2969e-02 -1.0172e-01
(126,.,.) =
-1.9898e-02 1.1437e-02 -6.1109e-02 -2.0294e-02
-1.9157e-01 1.5399e-01 -2.3774e+00 -1.2851e+00
4.7223e-02 -8.3199e-01 1.5482e-01 -1.4128e+00
⋮
9.8303e-02 8.7049e-02 -4.9641e-01 1.8631e-02
-1.5819e-01 -7.8419e-01 -4.9958e-01 -4.4801e-01
-2.3128e-01 -8.0637e-02 -1.0088e-01 3.4297e-02
(127,.,.) =
-4.6688e-02 2.6377e-02 -1.1830e-01 4.1776e-02
1.7381e-01 7.2495e-02 -4.0078e-02 1.2306e-01
-1.6091e-01 -1.0786e-01 -1.6649e-01 3.2966e-02
⋮
2.8699e-01 2.1908e-01 -4.5487e-01 -2.5770e-02
2.0351e-01 -4.6262e-01 1.0559e-01 -3.1717e-01
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[torch.FloatTensor of size 128]),
('module.encoder.cbhg.conv1d_banks.4.conv1d.weight',
( 0 ,.,.) =
1.8676e-02 4.3095e-02 -1.5726e-02 -4.3661e-02 -9.8139e-03
-2.0828e-02 3.0502e-01 -2.4855e-01 -1.1689e-01 -1.2934e-02
-2.4981e-01 6.2893e-02 -1.9024e-01 -5.0738e-02 3.3915e-01
⋮
1.6444e-01 -6.0511e-01 -5.3369e-01 -2.9930e-01 3.1245e-01
-2.8105e-01 2.4210e-01 -5.1733e-01 -5.1014e-01 -3.4072e-01
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( 1 ,.,.) =
6.9531e-02 -4.4087e-02 9.2566e-02 -4.6828e-02 3.0438e-02
1.0145e-01 1.8946e-01 -2.9237e+00 -2.4120e+00 1.6530e-01
-4.3283e-02 -7.5086e-02 -6.1910e-01 -1.1024e+00 3.5328e-01
⋮
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-4.6553e-02 1.3329e-02 -8.7335e-01 -3.2406e-02 2.4081e-01
1.9314e-01 3.8841e-02 -6.1874e-01 4.3118e-02 -2.3558e-01
( 2 ,.,.) =
1.9084e-02 -6.8872e-03 -4.7294e-02 -1.1103e-03 5.2093e-02
3.9098e-01 -4.9400e-02 -5.5011e-02 -1.4766e-01 3.7494e-01
-3.2525e-01 1.6407e-01 1.2210e-01 2.1276e-01 3.4819e-02
⋮
-1.2377e-01 3.4028e-02 2.1447e-01 -4.6228e-01 8.3448e-02
-4.6388e-01 2.2220e-01 -3.5371e-01 2.5234e-01 3.4024e-01
3.3419e-02 1.7029e-01 3.2783e-02 -5.3499e-02 9.5706e-02
...
(125,.,.) =
-4.0932e-02 -2.5763e-02 7.7512e-03 3.6522e-02 3.7960e-02
-3.4469e-02 -1.0437e+00 -7.4566e-01 -1.6032e-02 1.0350e-01
-8.7426e-01 1.6048e-01 -8.3122e-02 -1.8413e-03 -9.7011e-01
⋮
-1.6335e-01 7.2348e-02 -1.1172e+00 -4.5718e-01 -1.0383e+00
2.5089e-01 1.4073e-01 -5.9574e-01 -9.8118e-01 1.5447e-01
-1.8739e-01 -4.8575e-01 -1.2125e-01 -3.5486e-01 -4.8543e-01
(126,.,.) =
4.5631e-02 1.0822e-02 -2.5906e-02 8.9613e-03 -4.3499e-02
-6.0878e-01 -9.8588e-01 -5.2404e-01 -8.0881e-01 -3.5317e-01
-3.4792e-02 -1.0723e+00 -7.0286e-01 -5.1342e-01 8.7097e-03
⋮
1.7486e-01 5.7678e-02 -5.5733e-01 -1.9898e-01 -3.2039e-01
1.6729e-01 -2.5874e-01 -3.0634e-01 -4.0217e-01 7.8019e-02
-3.8354e-02 5.5831e-02 -1.5912e-01 -1.3364e-01 -1.0953e-01
(127,.,.) =
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-7.1364e-01 -3.9804e-01 2.8420e-02 -1.2269e+00 -4.1309e-01
-1.2522e-01 5.2512e-02 1.4330e-02 9.9259e-02 3.5246e-02
⋮
-1.6750e-02 -4.6182e-01 -8.4984e-01 -7.7151e-01 6.1423e-02
8.0939e-02 -4.9918e-01 -3.9325e-02 -3.2784e-01 -4.4412e-01
7.7830e-02 -6.5344e-02 1.7677e-02 -5.8119e-02 2.2737e-02
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[torch.FloatTensor of size 128]),
('module.encoder.cbhg.conv1d_banks.5.conv1d.weight',
( 0 ,.,.) =
-1.6707e-02 1.5062e-02 -2.4290e-02 -2.0553e-02 7.6927e-02 2.5556e-02
-1.5825e-01 1.8375e-01 -1.3715e+00 -1.0272e-01 -1.0912e+00 5.3088e-03
-2.9068e-01 -4.3543e-01 -4.9879e-02 1.9555e-02 1.5983e-01 7.6618e-02
⋮
-3.8250e-03 -8.1282e-02 7.3171e-02 1.9127e-02 1.2154e-01 -1.3264e+00
-5.9122e-02 -4.1025e-01 -3.0630e-01 -8.2133e-02 -5.3492e-01 1.9383e-01
1.1465e-01 -8.7856e-02 1.9600e-01 -2.7086e-01 -3.4039e-02 -6.0773e-02
( 1 ,.,.) =
-5.3984e-04 9.0873e-04 9.3033e-03 -9.9289e-03 -2.5024e-02 6.0367e-02
-4.0239e-01 1.9475e-02 -5.3144e-01 -2.0597e+00 -9.3734e-02 1.1011e-01
1.1027e-01 -8.8092e-02 -6.0984e-01 2.7714e-01 -2.5632e+00 -1.7113e-02
⋮
-7.9546e-01 -1.1268e+00 5.8257e-02 1.1209e-01 1.2857e-01 -1.9039e-01
1.4975e-01 1.0255e-01 -5.6228e-01 -4.2333e-01 -4.6771e-01 5.2113e-02
9.4120e-02 -2.0300e-01 -1.4289e-01 -3.4866e-01 -3.5331e-01 6.1327e-03
( 2 ,.,.) =
8.2903e-02 -2.8668e-02 -4.9796e-03 8.3328e-02 7.2743e-03 -1.2039e-03
1.2976e-01 3.7283e-02 5.2159e-02 2.3635e-01 7.3198e-02 -1.5133e+00
1.3264e-01 -1.4702e-01 1.6758e-01 1.0320e-01 -8.4526e-02 6.9735e-02
⋮
-1.4193e-01 -1.2630e-02 1.8733e-01 1.9073e-01 -2.8929e-01 -3.8090e-02
-3.0786e-01 -6.9524e-01 -2.1240e-01 -8.6229e-01 -2.4218e-02 9.6994e-02
-1.9311e-01 9.7328e-03 -2.3853e-01 -6.1829e-02 2.2485e-02 2.0454e-01
...
(125,.,.) =
-1.9382e-02 -5.8581e-02 2.0791e-02 -3.7576e-02 -2.6406e-02 -1.5199e-02
2.0389e-02 -1.8575e-01 -1.1484e-01 1.1992e-01 -1.4563e-02 -6.6121e-02
3.8035e-02 -1.8355e-02 -3.8509e-01 1.3890e-01 -3.7742e-01 3.6402e-02
⋮
3.2539e-01 1.4324e-01 1.2824e-01 7.3527e-02 -1.1932e-01 -2.4204e-01
-1.6890e-01 7.2598e-02 -7.4014e-03 -8.6582e-02 -2.3952e-01 9.7968e-02
5.8235e-02 -7.7000e-02 5.9546e-02 4.4890e-02 3.4878e-01 -2.8980e-01
(126,.,.) =
6.8186e-02 2.5241e-02 8.6074e-03 -5.2280e-02 -2.3363e-02 3.2551e-02
1.8539e-01 -5.2975e-02 -4.4095e-01 -1.0673e+00 2.9257e-01 1.3955e-01
4.7721e-03 1.0114e-01 -4.3054e-01 -1.8220e-01 -3.3834e-01 -3.7529e-01
⋮
-1.3286e-01 6.4792e-03 1.3652e-01 -1.6517e+00 -3.3156e-01 2.8734e-01
-9.4012e-01 3.8226e-01 1.5905e-01 -3.5121e-01 6.5831e-02 -1.9648e-01
6.5724e-02 -1.5490e-01 6.5657e-02 -1.0527e-01 -1.5488e-01 -9.4005e-03
(127,.,.) =
-2.3498e-02 6.9453e-03 4.4837e-02 2.5762e-02 4.6459e-02 2.5738e-02
1.3858e-01 -5.1303e-01 -2.2565e-02 9.0544e-02 1.9153e-01 1.7328e-01
-8.9238e-02 -5.8354e-02 5.8674e-02 1.4331e-02 -1.2725e-01 -9.0258e-02
⋮
5.6267e-02 7.4358e-02 -2.7942e-01 -1.4918e+00 -1.2619e+00 -7.9139e-03
-1.6702e-01 2.0627e-01 -7.0767e-02 6.5989e-03 6.8970e-02 1.4741e-01
8.9828e-02 -1.9443e-01 -1.1682e-01 1.7129e-01 5.7646e-02 1.2306e-02
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[torch.FloatTensor of size 128]),
('module.encoder.cbhg.conv1d_banks.6.conv1d.weight',
( 0 ,.,.) =
-4.5353e-03 4.5222e-03 -3.9971e-02 ... -3.5038e-02 4.8943e-02 -5.0395e-03
8.4028e-02 2.2407e-02 -6.3263e-01 ... -2.4554e-01 -4.2537e-03 -8.9171e-01
-4.7564e-02 6.2109e-02 -9.9553e-01 ... 1.4646e-01 -9.9596e-01 1.6758e-01
... ⋱ ...
2.2037e-01 1.9275e-01 1.5566e-01 ... -4.8230e-01 -9.3068e-01 -1.2051e-01
-2.5592e-02 -6.7962e-01 -2.8601e-01 ... 1.0041e-01 -3.2003e-01 -2.3566e-02
9.6592e-02 -1.9106e-01 8.4323e-02 ... -7.2522e-01 -4.6632e-02 1.1757e-01
( 1 ,.,.) =
3.8156e-02 3.8708e-02 9.7287e-03 ... 4.9475e-02 4.8088e-02 3.4232e-02
-1.2674e-01 1.4788e-01 -2.9398e-01 ... 1.5944e-01 -1.7241e-02 7.4101e-02
3.7165e-01 -1.2560e-01 1.2577e-01 ... -1.2738e-01 5.0040e-01 -6.6209e-01
... ⋱ ...
1.1140e-01 2.7386e-01 -4.2877e-01 ... 2.7582e-01 9.3554e-02 -5.7552e-01
-5.0430e-01 1.2536e-01 -3.1027e-01 ... -4.4512e-01 -4.1321e-01 7.5062e-02
-2.2296e-01 9.1203e-02 -1.4282e-01 ... -1.0473e-01 2.4301e-01 -1.2898e-03
( 2 ,.,.) =
-3.0079e-02 1.5203e-02 -2.8322e-02 ... -4.0184e-03 -1.2454e-02 8.4558e-04
6.6261e-02 -7.0814e-02 -6.4725e-02 ... 7.4598e-02 -5.3767e-01 -4.6577e-02
-1.0299e-01 -6.4324e-02 -9.7807e-02 ... -6.6077e-01 -6.5349e-02 -4.2513e-02
... ⋱ ...
-2.4065e-01 2.6608e-01 1.7404e-01 ... -6.9059e-02 -4.1446e-01 9.7021e-02
-9.1595e-02 -2.3584e-01 1.2416e-01 ... -9.2408e-01 4.9623e-02 -5.6548e-01
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...
(125,.,.) =
1.0336e-02 -2.2211e-03 3.0974e-02 ... 5.5323e-02 7.0989e-03 1.1988e-02
-3.3972e-02 -2.6190e-01 3.3510e-02 ... -2.2103e+00 -1.2093e+00 -8.1943e-02
1.9794e-02 -1.1747e-01 2.1005e-01 ... -3.2863e-02 6.6548e-02 1.8791e-01
... ⋱ ...
1.8541e-01 2.1995e-01 -9.6276e-01 ... -1.6338e-01 -8.7571e-01 -1.0884e+00
2.3457e-01 2.4633e-01 1.5244e-01 ... -2.2887e-01 -2.0436e-01 9.2099e-02
7.3668e-02 -1.5229e-01 -2.5827e-01 ... -1.0859e-01 -4.0845e-01 -2.8507e-02
(126,.,.) =
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5.1298e-02 -1.8507e-01 1.7350e-01 ... -6.8860e-01 1.3121e-01 1.1693e-01
-2.1713e-02 9.1121e-02 -8.1626e-01 ... -8.0668e-02 7.9635e-02 -1.6500e-01
... ⋱ ...
5.4700e-02 -1.4175e-01 7.1425e-02 ... -9.5701e-01 3.1120e-01 -2.1914e-02
-3.2772e-01 4.5213e-02 -4.7685e-01 ... -3.1668e-01 -3.9196e-01 2.9794e-02
2.3587e-01 6.2199e-02 9.8133e-02 ... -2.6222e-01 -2.2871e-02 8.5439e-02
(127,.,.) =
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-1.4162e+00 -7.0018e-02 -7.7413e-01 ... -9.2951e-01 -1.7080e+00 -1.0842e-01
-2.1010e-03 1.1678e-01 -3.1630e-01 ... -3.0953e-01 2.2892e-01 -1.1174e+00
... ⋱ ...
-6.8819e-02 -2.1103e-03 2.1246e-02 ... 1.1823e-01 -8.3776e-01 -1.3537e+00
-1.6399e-01 2.0398e-01 -8.5368e-01 ... -5.6074e-01 -5.9672e-01 1.0280e-01
1.9300e-01 1.8914e-02 -8.1840e-02 ... 1.1959e-01 -3.3886e-01 -3.0645e-01
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( 0 ,.,.) =
5.8847e-03 2.1921e-03 -1.8377e-02 ... -1.3964e-02 2.6978e-02 5.5054e-02
7.2680e-02 -3.6535e-01 -5.2221e-03 ... -2.3408e-01 1.2410e-01 3.1972e-03
1.3844e-02 -1.1377e-01 1.9315e-02 ... 3.8549e-02 -1.6374e+00 4.1895e-01
... ⋱ ...
-4.8199e-01 3.3490e-01 -3.2895e-01 ... 8.4339e-02 6.1372e-02 -1.5737e+00
-1.0041e+00 -1.5568e-01 5.7800e-02 ... -5.0289e-01 9.4830e-02 1.0988e-02
-1.4340e-01 -3.7784e-01 1.9994e-01 ... -1.7842e-01 -5.8743e-02 -7.0364e-02
( 1 ,.,.) =
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3.4651e-02 -1.7901e-01 -5.5429e-01 ... -7.5145e-01 -4.6915e-01 6.6311e-02
2.7764e-01 -9.3591e-02 1.3791e-01 ... -2.7145e-01 2.8122e-02 1.0651e-01
... ⋱ ...
8.7166e-02 1.5260e-01 -7.1763e-02 ... -1.3844e+00 -1.0487e+00 -1.2397e+00
-1.2803e-01 -3.3113e-02 -3.9552e-01 ... -3.1659e-01 1.0684e-01 8.3546e-02
-8.9448e-02 2.4670e-02 2.8182e-02 ... 1.6902e-01 -5.5595e-02 -2.6387e-01
( 2 ,.,.) =
2.4514e-02 2.0854e-02 -3.0092e-02 ... 4.2989e-02 3.4699e-02 3.5149e-02
-8.7910e-02 -3.7414e-02 -1.6182e-02 ... 1.3356e-01 -3.4510e-01 2.9737e-01
-5.0063e-02 -2.8385e-01 2.0934e-01 ... 2.8240e-02 8.9445e-02 -7.5023e-01
... ⋱ ...
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5.2983e-01 3.4188e-02 -1.1855e-01 ... -2.9443e-02 -2.1767e-01 -1.4374e-01
2.0587e-01 -5.5229e-02 1.2016e-01 ... 1.0737e-01 2.8937e-03 9.0145e-02
...
(125,.,.) =
2.3339e-02 -2.2911e-02 -2.5257e-02 ... 3.4837e-02 -3.6842e-03 -2.4066e-02
1.7219e-01 2.6568e-01 3.3388e-02 ... -8.4869e-02 1.0888e-01 -1.6184e-01
1.4101e-01 1.0440e-01 -1.5647e-01 ... 1.7188e-01 -1.0538e+00 -1.3552e+00
... ⋱ ...
-3.3658e-01 1.9421e-01 7.1169e-04 ... -4.4340e-01 -1.4363e+00 1.1073e-01
-2.8200e-01 -2.7627e-01 -3.5717e-01 ... -2.6617e-01 1.8424e-01 -5.1155e-02
-2.2967e-01 -9.3827e-02 1.8522e-01 ... -3.8467e-01 -8.9247e-02 -5.5430e-02
(126,.,.) =
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-2.9091e-01 1.1981e-01 8.3795e-02 ... -1.5997e+00 -1.1918e+00 -9.8389e-02
1.3939e-01 1.5886e-01 1.7142e-01 ... 2.3185e-01 -5.8920e-01 2.3799e-01
... ⋱ ...
3.5581e-02 2.9580e-02 1.4395e-01 ... -6.6605e-01 -1.4438e+00 -7.2258e-01
1.3443e-02 -1.0530e-01 -6.6303e-01 ... -5.2410e-01 -1.6476e-01 9.5399e-02
2.5848e-02 -8.6919e-02 -1.2343e-01 ... 7.9862e-02 -1.0104e-02 -3.2548e-01
(127,.,.) =
1.3345e-02 1.6564e-02 -3.3550e-02 ... 2.5938e-02 2.8583e-02 -4.1905e-02
1.3643e-01 -1.3233e-01 1.6668e-01 ... 1.4648e-01 -1.8051e-01 -9.3971e-02
7.5661e-02 1.2190e-01 -1.9221e-01 ... 6.9154e-03 -1.6507e-02 -5.3025e-02
... ⋱ ...
3.2565e-01 1.2659e-01 1.3699e-01 ... -1.9743e-01 2.6545e-01 -3.4281e-02
-1.7613e-02 -8.1578e-02 4.1549e-01 ... -1.9517e-01 4.2245e-01 2.1490e-02
9.5571e-02 1.0512e-01 -6.3192e-02 ... 1.4061e-01 2.6262e-01 1.6268e-01
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( 0 ,.,.) =
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7.7272e-02 1.5924e-01 1.1967e-01 ... -1.0723e+00 -1.3182e+00 8.4199e-02
1.2160e-01 -2.6351e-01 -9.3865e-02 ... 1.9552e-01 3.4170e-01 -3.8920e-01
... ⋱ ...
8.6547e-02 1.8861e-02 1.8739e-01 ... -1.3392e+00 1.6523e-01 -7.8392e-01
6.9175e-02 5.4181e-02 2.3608e-01 ... -6.6663e-01 -8.3389e-02 -2.4729e-01
-1.6154e-01 -5.3170e-02 7.6704e-02 ... -8.9796e-02 -4.5192e-02 -1.7497e-01
( 1 ,.,.) =
2.0992e-02 -6.3350e-03 5.8229e-03 ... -7.8143e-03 1.5739e-02 9.7577e-04
-6.3255e-02 1.6842e-01 2.9652e-01 ... -1.1351e+00 2.6696e-02 -2.3831e-01
-7.5772e-03 4.0876e-01 2.8722e-02 ... -3.6409e-01 1.6402e-02 6.1327e-02
... ⋱ ...
4.7118e-03 4.9662e-02 -1.0712e-01 ... 2.1898e-01 2.2844e-01 4.2636e-01
-1.5443e-01 -4.4623e-02 -4.2821e-02 ... 3.0447e-01 -1.1925e+00 -1.1519e-01
-6.3182e-02 8.5808e-02 -1.1307e-01 ... 5.1514e-02 -1.5927e-01 -2.3402e-01
( 2 ,.,.) =
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-5.5718e-02 -3.8603e-02 3.3173e-01 ... -5.4399e-02 -2.6046e-02 -1.8899e-01
1.7230e-01 -3.0073e-01 1.2899e-01 ... 1.5112e-01 -2.3045e-01 -2.2903e-01
... ⋱ ...
-6.5743e-02 -4.2672e-03 1.5176e-01 ... 6.4832e-03 1.4361e-01 3.0495e-02
1.7581e-01 -1.3613e-01 -1.2332e-01 ... -1.2568e-01 -4.6832e-03 -2.4509e-01
-4.4992e-02 -1.2710e-01 1.3147e-02 ... -3.4621e-02 2.4574e-02 -4.5390e-02
...
(125,.,.) =
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-7.9919e-02 -4.9393e-02 -5.8662e-01 ... -3.1168e-01 -1.3692e+00 -9.1817e-01
-3.7842e-01 5.7602e-02 -7.9871e-02 ... 9.0100e-03 -9.7410e-02 5.3491e-02
... ⋱ ...
-2.0529e-01 6.3498e-02 2.7442e-02 ... -7.3926e-01 1.5999e-01 -1.1082e-01
-2.1393e-01 1.3996e-01 -7.4514e-02 ... -1.9744e-01 -4.8600e-01 -4.3896e-01
1.2696e-02 -1.9454e-01 5.5097e-02 ... -9.6668e-03 -7.0573e-01 -4.2450e-01
(126,.,.) =
9.5369e-03 -1.4266e-02 -9.3971e-03 ... 1.1653e-02 -4.1660e-03 1.2811e-02
1.8217e-01 2.1676e-02 -3.0760e-01 ... 1.6222e-01 1.0926e-01 -4.7690e-02
5.3789e-02 -2.5205e-01 2.3654e-01 ... -5.4497e-03 7.4248e-02 -2.2669e-01
... ⋱ ...
-1.6270e-01 -8.9960e-02 -6.6290e-02 ... 5.3901e-02 2.5797e-04 7.1787e-01
-1.0061e-01 1.4840e-01 -1.0621e-01 ... -5.7225e-01 3.4818e-02 4.1044e-01
-3.5203e-02 -7.5935e-03 8.3569e-02 ... 1.1512e-01 1.9343e-02 -8.4515e-02
(127,.,.) =
1.7825e-02 5.7461e-03 -5.2420e-02 ... -8.4316e-03 3.7530e-02 7.4160e-03
-7.5303e-01 2.5693e-01 -5.4925e-01 ... -1.6473e-01 9.0183e-03 -1.2218e-01
-3.1759e-01 5.5704e-02 -2.5702e-01 ... 1.2007e-01 -5.1170e-01 -5.0482e-01
... ⋱ ...
7.2999e-02 2.4884e-01 -6.3399e-01 ... -1.0819e-01 -8.2761e-01 -3.9823e-02
-6.4678e-02 2.1777e-01 1.4814e-01 ... -2.4322e-01 2.4405e-01 -5.4170e-01
1.5825e-01 -1.0545e-01 8.2131e-02 ... 3.3050e-01 -3.3931e-01 -6.4139e-02
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('module.encoder.cbhg.conv1d_banks.9.conv1d.weight',
( 0 ,.,.) =
-1.2490e-03 -2.1708e-02 -1.3717e-02 ... -3.0470e-02 2.3697e-02 -2.0284e-02
5.8066e-04 -1.5938e-01 1.0033e-01 ... -1.2398e-01 7.3970e-02 -2.7781e-01
5.9249e-03 4.6126e-02 1.9371e-02 ... 3.9461e-01 -4.0300e-01 -8.3866e-02
... ⋱ ...
2.8160e-01 3.5017e-01 4.8683e-03 ... -4.4023e-01 -1.8014e+00 1.0488e-01
-2.9543e-01 9.8797e-02 -4.6013e-02 ... -1.5038e-01 1.5641e-01 -1.1150e+00
-1.8047e-01 4.8216e-02 -8.1360e-02 ... -7.3457e-02 -1.7689e-01 1.3239e-01
( 1 ,.,.) =
-1.8107e-02 -1.6579e-02 -2.2401e-02 ... -3.5960e-03 2.1527e-02 1.0929e-02
-1.8054e-01 -2.7563e-01 4.2408e-02 ... 3.0541e-02 -4.6706e-02 -1.8934e-01
-1.1431e-01 2.2401e-01 -2.3183e-01 ... 2.9956e-02 -2.6556e-02 4.7747e-02
... ⋱ ...
-1.4706e-01 -3.5813e-02 3.4498e-01 ... -5.3065e-02 3.5055e-03 -4.3279e-02
6.3806e-02 -1.0685e-01 1.2551e-01 ... 1.1024e-01 1.9199e-01 2.5452e-01
-2.1525e-02 -5.1525e-02 1.2442e-02 ... 2.5958e-02 -6.9614e-02 1.0687e-01
( 2 ,.,.) =
2.2546e-02 3.1870e-02 6.1550e-03 ... 2.5899e-02 2.4231e-03 -2.4090e-02
1.6648e-01 -2.2620e-01 1.8013e-03 ... 2.6781e-01 -2.2226e-01 -2.3612e-01
-2.5657e-01 1.0353e-02 6.2530e-02 ... 9.9296e-02 2.0786e-01 3.0163e-01
... ⋱ ...
-5.6254e-01 -1.4620e-01 -1.5079e-01 ... -1.5648e-02 1.4819e-01 -2.9422e-01
7.9525e-02 4.4902e-01 -1.9844e-01 ... 2.4591e-01 -2.6186e-02 9.1251e-02
-2.2625e-02 -3.3239e-01 5.0177e-02 ... -1.7797e-01 1.0420e-01 -4.0915e-02
...
(125,.,.) =
1.6619e-02 -1.2193e-02 1.8705e-02 ... -3.8572e-02 1.2710e-02 -4.4064e-03
-9.2771e-03 -2.2271e-01 1.7887e-01 ... 1.1817e-01 -1.3845e-01 1.2891e-01
-3.6453e-02 -2.4411e-01 3.2742e-01 ... 2.4662e-01 -1.0074e-01 -7.5640e-01
... ⋱ ...
1.5101e-01 4.6638e-02 -4.4681e-01 ... -2.7759e-01 -2.9360e-01 -4.4486e-03
5.1798e-01 -5.7905e-01 -5.9879e-01 ... 1.4691e-01 3.9102e-01 -2.4114e-02
-4.9070e-02 7.3786e-02 1.1952e-01 ... 6.9462e-02 -4.3941e-02 -1.3187e-01
(126,.,.) =
-2.0730e-02 -2.2372e-02 -1.4065e-02 ... -6.3372e-02 3.2751e-02 5.6936e-02
-1.6303e-01 -3.9686e-01 4.6270e-02 ... -8.9372e-02 -1.7251e-01 1.2557e-01
3.6537e-02 1.4654e-01 5.6433e-01 ... 3.5981e-01 4.9119e-02 -1.3890e-01
... ⋱ ...
-1.7771e-01 -1.9284e-01 3.4713e-01 ... 1.7397e-01 -3.4972e-02 4.2198e-01
-3.6182e-01 -2.9011e-01 -8.5047e-01 ... 2.9102e-02 -8.1940e-01 6.1122e-02
4.3997e-02 1.5453e-01 -7.1149e-02 ... -5.7751e-02 1.6488e-01 -2.2338e-01
(127,.,.) =
-2.2192e-02 -2.3334e-02 -1.8660e-02 ... -1.6811e-02 1.4472e-02 -2.1724e-02
-3.5289e-01 7.6216e-02 1.3014e-01 ... 3.1498e-02 1.8133e-01 -8.8622e-02
2.3091e-01 -7.8763e-02 1.1455e-01 ... -4.8957e-02 -2.1180e-02 9.7200e-02
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( 0 ,.,.) =
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7.5329e-02 -1.8212e-01 -5.1388e-01 ... -1.1485e+00 -8.6555e-02 9.2271e-02
-1.5347e-01 -1.8767e-01 3.9370e-02 ... -7.1084e-02 6.2531e-02 -1.3709e-01
... ⋱ ...
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( 1 ,.,.) =
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9.8703e-02 2.6438e-01 -2.0426e-02 ... -1.3528e+00 3.5872e-02 6.7084e-02
-8.0820e-03 -7.2237e-02 -2.2399e-01 ... -5.5380e-02 -2.0954e-01 -3.6943e-01
... ⋱ ...
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1.7834e-02 2.2267e-02 -7.0417e-02 ... 4.2893e-02 -2.8627e-01 3.4247e-02
( 2 ,.,.) =
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1.8813e-01 2.6524e-01 5.7974e-01 ... -8.7242e-03 -1.0690e-01 -7.8983e-01
... ⋱ ...
2.3529e-01 -6.2904e-02 -1.4557e-01 ... -4.5544e-02 1.7732e-01 8.4309e-03
3.3294e-01 -8.4541e-02 8.7829e-02 ... -1.1917e-01 -4.7819e-02 -7.6159e-01
1.3981e-02 -1.6293e-02 -3.2810e-03 ... -2.1877e-01 1.0173e-01 -1.8190e-01
...
(125,.,.) =
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-3.3739e-02 -1.0891e-01 4.8999e-02 ... 1.4597e-02 1.6591e-01 5.4610e-01
2.9653e-01 -1.0343e-01 1.2764e-01 ... -2.8191e-01 1.4097e-01 -1.2534e-01
... ⋱ ...
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(126,.,.) =
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1.5041e-01 -9.3227e-02 6.9412e-03 ... 1.4876e-01 1.2624e-01 -8.2570e-03
1.4423e-01 5.5396e-02 3.7413e-01 ... -3.2377e-02 9.6140e-03 -1.9368e-01
... ⋱ ...
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-5.8840e-02 3.1408e-02 4.1788e-02 ... 1.1578e-01 8.8983e-02 -9.6329e-02
(127,.,.) =
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-1.6599e-01 -2.7726e-02 1.0764e-01 ... -2.8034e-02 1.1052e-01 1.5459e-01
4.3881e-01 -1.1269e-01 -5.1397e-01 ... 1.1887e-01 -5.0752e-03 1.7655e-02
... ⋱ ...
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( 0 ,.,.) =
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-1.3106e-01 1.4341e-01 7.9323e-02 ... 6.3507e-02 -4.5839e-02 2.1501e-01
9.0208e-02 -1.7771e-01 -4.5272e-01 ... -6.9815e-02 -1.9479e-01 -1.8784e-03
... ⋱ ...
-1.3932e-01 -3.7012e-01 -4.7639e-01 ... -1.0966e+00 1.5456e-01 -9.4229e-01
5.7549e-02 -3.5620e-02 -2.3836e-01 ... 2.2370e-01 -3.1791e-01 -4.1511e-01
7.0249e-02 1.9633e-01 1.1341e-01 ... -3.2817e-01 2.5300e-01 -1.3422e-01
( 1 ,.,.) =
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-1.1532e-02 2.4754e-01 -1.5029e-01 ... 3.5624e-01 -4.6946e-02 -1.4037e-01
... ⋱ ...
-3.6031e-01 1.9696e-01 1.2519e-01 ... -1.9825e-01 -4.8447e-02 -1.3655e+00
3.2029e-01 -6.1391e-02 1.5598e-01 ... -1.5652e-01 1.6171e-01 1.2432e-01
6.3132e-02 -1.3603e-01 1.7496e-01 ... 5.9061e-02 9.7532e-02 -1.7508e-01
( 2 ,.,.) =
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1.9804e-01 -3.1485e-01 2.3391e-01 ... -1.3164e-01 4.3471e-01 -1.9872e-02
... ⋱ ...
2.8241e-01 1.6263e-01 -1.1973e-01 ... -1.9607e-01 2.0565e-01 -4.4889e-01
1.6790e-01 -3.0801e-01 3.3417e-01 ... 4.0863e-02 -6.6297e-02 1.1063e-01
7.7762e-02 1.0632e-01 6.3908e-02 ... 1.2705e-01 -4.4205e-02 9.7256e-02
...
(125,.,.) =
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-3.8700e-01 -1.5979e-02 -2.6519e-01 ... 3.7613e-02 -3.7213e-02 1.0377e-01
-6.5393e-02 -1.7880e-01 -4.2023e-02 ... 1.0308e-02 -6.8235e-02 1.3338e-02
... ⋱ ...
-6.8673e-01 1.2962e-01 -1.2936e-01 ... -1.4112e+00 3.2245e-01 3.7807e-02
1.8645e-02 -7.1574e-02 -3.7431e-01 ... 5.2542e-02 -5.8785e-02 3.6606e-02
-2.0383e-01 8.8988e-02 4.6355e-02 ... -2.3510e-01 -2.2312e-01 -3.7548e-01
(126,.,.) =
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-3.4146e-01 2.2077e-02 -4.2619e-01 ... 1.9176e-01 -1.5382e-02 1.8814e-01
2.1378e-01 -2.0700e-01 1.5258e-01 ... -2.4342e-01 -1.1730e-01 1.1733e-01
... ⋱ ...
9.8764e-02 3.2841e-01 3.0355e-01 ... 5.4214e-02 -9.0069e-02 -3.2505e-01
-3.9483e-01 2.0615e-01 2.1718e-01 ... -4.8824e-01 -8.4747e-01 1.1944e-01
-3.3625e-02 -4.8884e-03 4.1738e-02 ... -1.3514e-01 -2.0050e-01 -1.4370e-01
(127,.,.) =
2.5590e-02 1.8387e-02 2.0806e-02 ... 8.1549e-03 -1.6850e-03 7.8334e-03
-2.8174e-01 -3.6004e-01 -2.3312e-01 ... 4.5390e-02 4.9092e-02 -4.5602e-02
6.9535e-01 1.4977e-01 -2.8423e-02 ... -1.3624e-01 1.0768e-01 -2.6318e-01
... ⋱ ...
5.7602e-02 1.3463e-01 5.8125e-03 ... -2.4428e-02 -5.6623e-01 -2.7310e-01
5.5486e-02 2.0697e-01 -2.1729e-01 ... 2.6449e-01 -7.7781e-02 -2.6877e-01
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... ⋱ ...
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... ⋱ ...
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( 1 ,.,.) =
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... ⋱ ...
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... ⋱ ...
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...
(125,.,.) =
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(126,.,.) =
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... ⋱ ...
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(127,.,.) =
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... ⋱ ...
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( 0 ,.,.) =
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... ⋱ ...
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( 1 ,.,.) =
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... ⋱ ...
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1.6876e-01 -1.5944e-01 -3.1784e-02 ... -4.3685e-01 -1.4064e-01 8.8937e-02
( 2 ,.,.) =
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... ⋱ ...
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...
(125,.,.) =
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... ⋱ ...
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(126,.,.) =
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2.9147e-01 1.6190e-01 1.7895e-01 ... -1.2549e+00 1.2088e-01 -3.7305e-01
... ⋱ ...
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1.1004e-01 -1.3190e-01 -3.9576e-02 ... 9.9157e-03 -1.3757e-01 -4.7838e-02
(127,.,.) =
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-4.6693e-03 -2.2383e-01 2.8204e-02 ... 2.2932e-02 -1.6864e-01 -4.6507e-01
... ⋱ ...
-1.7754e-02 1.6717e-01 -2.0567e-01 ... 1.1366e-01 -1.0704e-01 5.5078e-02
3.0380e-01 -1.5191e-01 2.2612e-01 ... 6.7546e-01 6.6147e-02 -1.0390e-01
1.0645e-01 7.5489e-02 1.0369e-01 ... 5.6466e-02 1.0624e-01 3.4812e-02
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⋮
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( 1 ,.,.) =
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⋮
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( 2 ,.,.) =
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3.5820e-01 -2.0916e-01 1.3814e+00
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⋮
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4.1027e-02 1.1533e-01 -2.0033e-01
1.0978e-01 -1.6570e-01 -8.2350e-02
...
(125 ,.,.) =
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1.8074e-01 5.8933e-01 3.6671e-01
-2.7379e-01 -1.4911e-01 -1.0068e-02
⋮
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1.8630e-01 2.2686e-01 -5.0762e-02
4.6377e-01 5.5982e-01 2.5739e-01
(126 ,.,.) =
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1.0508e-01 -2.2993e-02 6.0722e-02
⋮
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1.0348e-02 1.1076e-01 -2.4765e-01
-1.2953e-01 2.0722e-01 -2.8834e-01
(127 ,.,.) =
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⋮
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⋮
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2.0842e-01 -1.6191e-01 -1.3249e-01
( 1 ,.,.) =
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1.5427e-01 -2.4553e-01 -2.8350e-01
⋮
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2.4085e-01 5.7162e-03 3.0859e-01
3.4030e-01 1.2176e-01 1.1497e-01
( 2 ,.,.) =
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⋮
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1.3968e-01 -2.1056e-01 -3.8614e-01
...
(125,.,.) =
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1.9614e-01 1.8801e-01 5.5208e-01
-5.3128e-02 4.0566e-01 -3.9731e-01
⋮
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1.3921e-01 -3.7821e-01 -7.7398e-02
3.1290e-01 1.0250e-01 1.6110e-01
(126,.,.) =
1.8149e-01 1.4161e-01 -2.1990e-01
9.2477e-02 1.8051e-01 3.1933e-01
4.1670e-02 6.6928e-02 2.5147e-01
⋮
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1.1001e-01 3.4186e-01 2.3474e-01
1.2854e-01 1.6606e-01 -1.7315e-02
(127,.,.) =
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4.9821e-02 -5.0298e-02 5.0703e-02
⋮
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1.6557e-01 4.1488e-01 1.0505e-01
8.3979e-02 -3.8755e-01 -1.7537e-02
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⋮
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2.4180e-01 8.2423e-01 3.4042e-01
(1 ,.,.) =
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⋮
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(2 ,.,.) =
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⋮
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...
(77,.,.) =
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⋮
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(78,.,.) =
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⋮
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1.1730e-01 3.1792e-01 3.2876e-01
(79,.,.) =
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⋮
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⋮
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⋮
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(2 ,.,.) =
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⋮
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...
(77,.,.) =
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-7.5183e-02 4.6646e-02 -3.9222e-02 2.0479e-02
⋮
2.2368e-02 3.6631e-02 -1.6998e-01 4.6614e-02
2.6064e-02 5.3832e-02 9.7212e-03 1.9178e-01
6.9384e-02 3.0382e-03 6.0725e-02 4.4700e-01
(78,.,.) =
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5.4072e-03 3.2377e-02 5.7772e-03 4.8725e-02
⋮
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(79,.,.) =
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⋮
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3.8124e-02 -5.4581e-02 -2.2384e-02 1.0250e-01
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3.8151e-01
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2.8821e-09
1.3710e-06
6.7044e-07
3.3338e-09
4.7245e+00
1.7651e-08
4.5999e-06
3.1728e-06
2.7243e-09
4.4030e-10
4.1070e-08
1.1915e-07
5.6752e-08
[torch.FloatTensor of size 80]),
('module.postnet.conv1d_banks.4.conv1d.weight',
(0 ,.,.) =
1.5686e+00 -6.4739e-01 1.4181e-01 4.0395e-01 -7.0657e-01
-4.1751e-01 -2.0063e-02 1.7128e-01 -4.3060e-01 9.8187e-02
-4.4967e-02 1.4617e-01 1.0732e-01 1.3728e-01 1.6912e-01
⋮
1.5863e-01 1.4459e-02 -3.8073e-03 -1.2433e-01 -3.4891e-02
1.5925e-01 6.6438e-02 -9.0607e-03 -6.1685e-02 3.7638e-03
1.9384e-01 1.6836e-01 -5.1740e-02 -1.5689e-01 1.0534e-03
(1 ,.,.) =
-1.7182e-01 -1.8121e-01 -1.7164e-01 -1.9797e-01 -3.2164e-01
-2.1415e-02 3.4693e-02 -2.7767e-02 1.8646e-01 9.8169e-02
-6.0786e-02 -4.2094e-02 -4.6286e-02 6.9078e-02 9.2483e-02
⋮
5.5376e-02 6.4676e-02 -3.8600e-02 5.1214e-02 -8.2247e-02
7.9675e-02 6.6527e-02 -3.0137e-02 4.3228e-02 -6.6019e-02
2.2593e-01 1.6549e-01 1.3740e-01 1.9141e-01 7.4694e-02
(2 ,.,.) =
5.8797e-01 1.1582e-01 2.7823e-01 1.8149e-01 4.8132e-02
1.1961e-01 2.0705e-01 -5.0408e-02 3.7282e-02 -1.9043e-01
-1.5806e-01 4.0247e-02 8.6887e-02 1.2589e-01 -1.8701e-01
⋮
-8.8763e-02 1.3013e-02 -1.0821e-02 1.5986e-02 3.8196e-02
1.5440e-04 -5.4442e-02 8.4634e-03 -1.2476e-01 5.0944e-02
3.9365e-01 1.0105e-01 1.2978e-01 2.1548e-02 6.2940e-02
...
(77,.,.) =
-1.5847e-01 2.6119e-01 -5.9377e-02 -1.8277e+00 -1.5449e+00
2.7968e-01 2.7494e-01 -2.2583e-01 1.4109e-01 -7.7504e-01
-6.5495e-02 -6.3089e-02 1.9451e-01 2.7907e-01 -4.3442e-02
⋮
4.2854e-02 5.0023e-02 -6.5699e-02 -7.9344e-02 -1.7509e-01
6.1825e-02 1.6443e-01 -1.5240e-02 1.2528e-01 1.4501e-01
-1.0310e-01 3.6294e-01 -8.2844e-04 1.3113e-01 1.6783e-01
(78,.,.) =
-9.2892e-02 -2.0476e-01 -1.9328e-01 -1.6904e-01 -3.7297e-01
6.4856e-02 -2.9276e-02 6.3521e-02 -1.6848e-02 -3.5620e-02
1.7215e-02 6.9829e-02 -1.4160e-01 3.9499e-02 4.0538e-02
⋮
3.3965e-02 6.5948e-02 2.1840e-02 5.3068e-02 -3.8973e-02
6.9934e-02 -3.0774e-02 3.1385e-03 3.4650e-02 -5.8947e-02
7.1828e-02 4.0206e-02 1.0805e-01 2.2138e-01 1.2469e-01
(79,.,.) =
-1.5203e-01 -3.8435e-02 1.3500e-02 1.6194e-02 -1.2227e-01
6.4620e-02 2.8093e-02 1.2704e-02 4.0136e-02 -2.4529e-01
-3.5442e-02 2.9202e-02 2.3826e-02 4.1322e-03 -1.2359e-01
⋮
-5.1499e-02 3.8150e-02 4.7023e-03 4.8181e-02 -3.8601e-02
-4.0502e-02 2.0468e-03 -5.0560e-02 5.2808e-02 8.2607e-03
3.4581e-02 -1.8643e-03 7.2634e-02 1.5115e-01 5.9412e-02
[torch.FloatTensor of size 80x80x5]),
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[torch.FloatTensor of size 80]),
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[torch.FloatTensor of size 80]),
('module.postnet.conv1d_banks.4.bn.running_var',
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4.5604e-07
3.1844e-07
2.0638e+00
[torch.FloatTensor of size 80]),
('module.postnet.conv1d_banks.5.conv1d.weight',
(0 ,.,.) =
-5.4860e-01 2.0713e-01 1.6055e+00 -5.2223e-01 1.8030e+00 2.6348e-01
-2.0093e-01 4.5769e-02 6.3496e-01 2.9092e-01 1.0631e-01 1.8241e-01
-1.3228e-01 7.9001e-02 4.3514e-02 1.4466e-01 -8.5870e-02 -1.6509e-01
⋮
-1.5503e-02 4.7154e-02 5.2289e-02 1.6896e-02 2.2193e-02 1.3157e-01
-1.2542e-01 -3.9076e-02 -4.4819e-02 2.0163e-01 2.1832e-01 3.3319e-01
-2.2883e-01 -2.0393e-01 -7.0349e-02 -2.6324e-02 -5.9681e-02 3.6948e-01
(1 ,.,.) =
-1.7623e-01 -1.3750e-01 -3.1823e-02 -1.9775e-01 5.8317e-02 1.9174e-01
-3.5452e-02 -2.3127e-02 -2.1820e-02 2.3226e-02 1.4172e-01 2.6835e-01
-1.3102e-01 -2.6183e-02 -6.7891e-02 -1.7501e-02 -8.8783e-02 -5.0563e-02
⋮
-4.0606e-02 2.6082e-02 6.4175e-02 4.1513e-02 9.5760e-02 5.5027e-02
-1.0897e-02 9.1545e-02 4.0820e-02 5.1575e-02 4.0873e-02 -2.8130e-02
3.7406e-02 -4.9818e-03 1.2074e-02 -1.0079e-02 1.5794e-02 3.5829e-03
(2 ,.,.) =
8.6653e-01 7.1018e-01 1.1930e-01 6.7257e-01 3.2135e-01 2.2231e-01
-5.5292e-01 -1.8671e-01 1.7102e-01 1.4232e-01 2.1184e-01 -8.6735e-02
6.7562e-02 1.0823e-02 1.7989e-01 1.9343e-01 2.2654e-01 -5.1714e-02
⋮
-9.9573e-02 -6.6490e-02 -2.6533e-02 6.5583e-02 2.2089e-02 3.8572e-02
5.8930e-02 -1.1670e-02 1.4769e-02 7.4453e-02 5.9849e-03 7.0727e-02
3.0224e-01 5.2385e-03 2.7315e-01 2.0238e-01 1.1285e-01 3.0515e-01
...
(77,.,.) =
-1.4380e-01 -1.2970e-01 -7.8037e-02 -1.1121e-01 -9.8119e-02 4.0675e-01
-2.5799e-01 -3.3438e-01 -2.3150e-01 -2.2831e-01 -1.6482e-01 1.7353e-01
-4.2796e-02 -4.1783e-02 1.7217e-02 3.3401e-02 4.2506e-02 -6.9167e-02
⋮
2.7723e-02 3.0782e-02 9.9269e-03 3.5218e-02 -4.7910e-02 -3.2218e-02
-5.2994e-03 2.0554e-02 2.8580e-02 3.0602e-02 -1.3117e-02 -5.0382e-02
-6.8279e-02 -1.0015e-02 -1.8131e-02 -7.6639e-02 -7.6285e-02 -2.6653e-01
(78,.,.) =
1.0527e-01 9.4512e-02 1.0673e-01 1.6742e-01 5.7785e-02 -2.8438e-02
7.4955e-02 3.0069e-02 6.6862e-02 3.0888e-02 3.1180e-02 1.3977e-01
9.2024e-03 2.2835e-02 -4.1930e-02 1.0184e-01 7.6296e-02 6.0169e-03
⋮
-9.7722e-02 -1.0904e-03 -6.9749e-02 -6.9405e-03 -1.2642e-01 -9.5243e-02
5.8125e-03 -6.7751e-02 1.1047e-01 8.2299e-03 -4.2008e-02 5.1742e-02
7.3912e-02 8.3876e-02 6.2410e-02 7.7047e-02 6.0862e-02 7.4805e-02
(79,.,.) =
-1.0010e+00 -9.8334e-02 -1.6020e+00 7.5413e-01 -4.1561e-01 1.0840e+00
2.4568e-01 3.1564e-01 -3.4452e-01 -4.6273e-02 1.0520e+00 -9.7148e-01
-7.6527e-02 -2.2401e-01 7.9422e-02 -2.6896e-01 3.6150e-01 6.4700e-02
⋮
1.4473e-01 3.2172e-01 2.0931e-01 1.8520e-01 -2.2031e-01 -1.4759e-01
-6.1705e-02 -6.6544e-03 -1.3486e-01 -9.8805e-02 -1.5604e-01 -6.9360e-02
-3.2565e-01 -4.4507e-01 -4.6296e-01 2.4712e-01 3.9803e-01 7.2480e-01
[torch.FloatTensor of size 80x80x6]),
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(0 ,.,.) =
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6.0406e-02 -2.1307e-01 1.3011e-01 ... -7.6565e-01 1.6152e-01 5.9789e-01
7.5477e-01 -1.1030e-01 3.3991e-01 ... -9.8794e-02 1.3341e-01 -1.6148e+00
... ⋱ ...
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(1 ,.,.) =
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... ⋱ ...
1.7881e-01 1.0789e-01 2.4740e-01 ... 2.3902e-02 8.6342e-02 1.2528e-02
1.9066e-02 -2.1986e-03 7.4409e-02 ... 1.1230e-02 6.7261e-02 1.5289e-01
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(2 ,.,.) =
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4.2291e-02 1.3204e-01 1.5855e-01 ... 3.1037e-01 1.4537e-01 2.1174e-01
1.3034e-01 -7.7696e-02 4.2838e-02 ... -8.1076e-02 6.7180e-02 -8.8425e-02
... ⋱ ...
1.0065e-03 9.5219e-02 -1.1813e-02 ... 6.7723e-03 3.1524e-02 2.6009e-02
1.0982e-01 -1.3729e-01 4.4807e-02 ... 7.5306e-02 5.6696e-02 -1.8068e-01
1.8904e-01 1.3797e-01 2.5836e-01 ... 2.0835e-01 1.6837e-01 1.1935e-02
...
(77,.,.) =
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... ⋱ ...
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3.0891e-03 1.4962e-01 5.6578e-02 ... 6.6190e-02 9.9097e-02 1.1227e-01
1.1993e-01 1.4432e-01 6.9030e-02 ... 5.5823e-02 3.9141e-02 3.2510e-02
(78,.,.) =
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-7.6488e-03 9.0009e-02 -7.3368e-02 ... -1.9346e-02 -5.6370e-02 -1.0089e-01
... ⋱ ...
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7.9000e-02 7.5109e-02 6.8631e-02 ... 3.8451e-02 -9.6734e-02 -2.5716e-01
2.6664e-01 1.3880e-01 2.1747e-01 ... 1.8959e-01 -2.9664e-02 -4.0854e-01
(79,.,.) =
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1.8937e-01 2.3438e-01 8.7372e-02 ... -7.0633e-02 -2.0086e-01 -3.7153e-02
... ⋱ ...
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1.1112e-01 -7.1629e-03 9.1774e-02 ... 1.5254e-01 9.0325e-03 1.7725e-01
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(0 ,.,.) =
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1.5095e+00 2.4575e+00 1.6089e+00 ... 1.7952e-01 -1.2080e+00 -6.9600e-01
6.5311e-01 1.1343e+00 1.4454e+00 ... 8.3564e-01 7.0717e-01 -1.5440e-01
... ⋱ ...
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-2.3222e-01 -4.4655e-01 -4.5594e-01 ... 1.1238e-02 4.7837e-01 1.5670e-01
1.1653e+00 3.5081e-01 -2.5347e-01 ... -2.3556e-01 2.3423e-01 -1.9039e-01
(1 ,.,.) =
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6.1432e-02 -4.1108e-01 4.7087e-02 ... -8.2081e-01 -6.1591e-01 -9.2098e-02
1.9864e-02 2.4329e-02 -9.4784e-03 ... 3.0884e-01 1.7533e-01 1.2645e-01
... ⋱ ...
8.2526e-02 8.2653e-02 -5.7011e-02 ... 3.6185e-01 3.3817e-01 4.4439e-01
1.6853e-01 7.1762e-02 -3.8038e-02 ... 2.2377e-01 1.5718e-01 1.7180e-01
1.4553e-01 -1.6342e-01 -1.1863e-01 ... 1.0773e-01 -2.1940e-01 -9.9362e-02
(2 ,.,.) =
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1.5800e-01 1.5400e-01 -1.4746e-01 ... -1.0636e-02 9.1024e-02 -7.5079e-03
-1.4288e-01 4.5058e-02 7.4069e-02 ... 7.3295e-02 7.5404e-02 2.2525e-01
... ⋱ ...
8.2440e-02 6.3924e-02 -1.6160e-01 ... 5.9316e-02 2.6346e-02 -2.0503e-02
-1.0922e-03 1.1213e-01 -1.8133e-01 ... 4.4410e-02 1.0853e-01 -4.5268e-02
3.2731e-01 3.3002e-01 -1.4442e-01 ... 7.0902e-02 1.3694e-01 2.7840e-01
...
(77,.,.) =
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-4.6372e-02 5.2028e-02 2.9571e-01 ... 4.1118e-01 1.7782e-01 2.5223e-01
-1.6780e-01 -3.2026e-01 7.4724e-02 ... -2.3654e-01 -3.5995e-01 5.3968e-02
... ⋱ ...
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(78,.,.) =
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2.2729e-01 3.3453e-01 9.6079e-02 ... -3.1711e-01 2.8326e-01 2.2800e-01
... ⋱ ...
7.6057e-03 -1.9277e-01 5.0295e-02 ... 8.5391e-02 1.8244e-01 3.4017e-01
3.2960e-02 -1.0860e-01 5.6157e-02 ... 1.4160e-01 3.2270e-01 3.4672e-01
3.1290e-01 7.7094e-02 1.4394e-01 ... 2.8697e-01 1.1527e-01 1.4380e-01
(79,.,.) =
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-1.0203e-01 -7.6795e-02 -6.0822e-02 ... -8.2102e-02 1.5033e-01 2.0831e-01
... ⋱ ...
2.2731e-01 1.8507e-01 1.3185e-01 ... 8.3878e-02 -2.1591e-02 -6.5322e-02
1.1341e-01 4.7051e-02 5.4270e-02 ... 4.8335e-04 -4.4908e-02 -1.3254e-01
-1.3947e-03 6.5450e-02 6.8548e-02 ... -5.2640e-02 -6.0732e-02 -1.5454e-01
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EXAMPLES FROM TRAINING SET¶
In [5]:
import pandas as pd df = pd.read_csv('/data/shared/KeithIto/LJSpeech-1.0/metadata.csv', delimiter='|')
In [6]:
sentence = df.iloc[120, 1].lower().replace(',','') print(sentence) align = tts(model, sentence, CONFIG, use_cuda, ap)
that he has a 5 an 8 or a 3 before him unless the press work is of the best:
/home/erogol/miniconda3/envs/pytorch/lib/python3.6/site-packages/librosa/util/utils.py:1725: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`. if np.issubdtype(x.dtype, float) or np.issubdtype(x.dtype, complex):
> Run-time: 8.474236488342285
/home/erogol/miniconda3/envs/pytorch/lib/python3.6/site-packages/librosa/display.py:656: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`. if np.issubdtype(data.dtype, np.complex):
Your browser does not support the audio element.
NEW EXAMPLES¶
In [10]:
sentence = "That's all folks." model.decoder.max_decoder_steps = 300 alignment = tts(model, sentence, CONFIG, use_cuda, ap)
/home/erogol/miniconda3/envs/pytorch/lib/python3.6/site-packages/librosa/util/utils.py:1725: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`. if np.issubdtype(x.dtype, float) or np.issubdtype(x.dtype, complex):
> Run-time: 1.5912322998046875
/home/erogol/miniconda3/envs/pytorch/lib/python3.6/site-packages/librosa/display.py:656: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`. if np.issubdtype(data.dtype, np.complex):
Your browser does not support the audio element.