import os
import tensorflow as tf
import soundfile as sf
from librosa.core import load
from tests import get_tests_path, get_tests_input_path, get_tests_output_path
from TTS.vocoder.tf.layers.pqmf import PQMF
TESTS_PATH = get_tests_path()
WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
def test_pqmf():
w, sr = load(WAV_FILE)
layer = PQMF(N=4, taps=62, cutoff=0.15, beta=9.0)
w, sr = load(WAV_FILE)
w2 = tf.convert_to_tensor(w[None, None, :])
b2 = layer.analysis(w2)
w2_ = layer.synthesis(b2)
w2_ = w2.numpy()
print(w2_.max())
print(w2_.min())
print(w2_.mean())
sf.write(os.path.join(get_tests_output_path(), 'tf_pqmf_output.wav'),
w2_.flatten(), sr)
import numpy as np
from torch.utils.data import DataLoader
from tests import get_tests_output_path, get_tests_path
from TTS.utils.audio import AudioProcessor
from TTS.vocoder.configs import BaseGANVocoderConfig
from TTS.vocoder.datasets.gan_dataset import GANDataset
from TTS.vocoder.datasets.preprocess import load_wav_data
file_path = os.path.dirname(os.path.realpath(__file__))
OUTPATH = os.path.join(get_tests_output_path(), "loader_tests/")
os.makedirs(OUTPATH, exist_ok=True)
C = BaseGANVocoderConfig()
test_data_path = os.path.join(get_tests_path(), "data/ljspeech/")
ok_ljspeech = os.path.exists(test_data_path)
def gan_dataset_case(batch_size, seq_len, hop_len, conv_pad, return_pairs,
return_segments, use_noise_augment, use_cache,
num_workers):
"""Run dataloader with given parameters and check conditions"""
ap = AudioProcessor(**C.audio)
_, train_items = load_wav_data(test_data_path, 10)
dataset = GANDataset(
ap,
train_items,
seq_len=seq_len,
hop_len=hop_len,
pad_short=2000,
