Medix-AI/coqui-tts
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coqui-tts
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fix pylint once again

This commit is contained in:
sanjaesc 2020-10-25 10:04:24 +01:00
parent 80f5e39e56
commit d158ec0806
4 changed files with 10 additions and 11 deletions
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1
TTS/bin/train_wavernn_vocoder.py
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@ -28,7 +28,6 @@ from TTS.utils.generic_utils import (
) )
from TTS.vocoder.datasets.wavernn_dataset import WaveRNNDataset from TTS.vocoder.datasets.wavernn_dataset import WaveRNNDataset
from TTS.vocoder.datasets.preprocess import ( from TTS.vocoder.datasets.preprocess import (
find_feat_files,
load_wav_data, load_wav_data,
load_wav_feat_data load_wav_feat_data
) )

1
TTS/vocoder/datasets/wavernn_dataset.py
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@ -1,7 +1,6 @@
import torch import torch
import numpy as np import numpy as np
from torch.utils.data import Dataset from torch.utils.data import Dataset
from multiprocessing import Manager
class WaveRNNDataset(Dataset): class WaveRNNDataset(Dataset):

2
tests/test_vocoder_wavernn.py
View File

@ -17,7 +17,7 @@ def test_wavernn():
feat_dims=80, feat_dims=80,
compute_dims=128, compute_dims=128,
res_out_dims=128, res_out_dims=128,
res_blocks=10, num_res_blocks=10,
hop_length=256, hop_length=256,
sample_rate=22050, sample_rate=22050,
) )

17
tests/test_vocoder_wavernn_datasets.py
View File

@ -23,7 +23,7 @@ test_quant_feat_path = os.path.join(test_data_path, "quant")
ok_ljspeech = os.path.exists(test_data_path) ok_ljspeech = os.path.exists(test_data_path)
def wavernn_dataset_case(batch_size, seq_len, hop_len, pad, mode, num_workers): def wavernn_dataset_case(batch_size, seq_len, hop_len, pad, mode, mulaw, num_workers):
""" run dataloader with given parameters and check conditions """ """ run dataloader with given parameters and check conditions """
ap = AudioProcessor(**C.audio) ap = AudioProcessor(**C.audio)
@ -42,6 +42,7 @@ def wavernn_dataset_case(batch_size, seq_len, hop_len, pad, mode, num_workers):
hop_len=hop_len, hop_len=hop_len,
pad=pad, pad=pad,
mode=mode, mode=mode,
mulaw=mulaw
) )
# sampler = DistributedSampler(dataset) if num_gpus > 1 else None # sampler = DistributedSampler(dataset) if num_gpus > 1 else None
loader = DataLoader(dataset, loader = DataLoader(dataset,
@ -78,13 +79,13 @@ def wavernn_dataset_case(batch_size, seq_len, hop_len, pad, mode, num_workers):
def test_parametrized_wavernn_dataset(): def test_parametrized_wavernn_dataset():
''' test dataloader with different parameters ''' ''' test dataloader with different parameters '''
params = [ params = [
[16, C.audio['hop_length'] * 10, C.audio['hop_length'], 2, 10, 0], [16, C.audio['hop_length'] * 10, C.audio['hop_length'], 2, 10, True, 0],
[16, C.audio['hop_length'] * 10, C.audio['hop_length'], 2, "mold", 4], [16, C.audio['hop_length'] * 10, C.audio['hop_length'], 2, "mold", False, 4],
[1, C.audio['hop_length'] * 10, C.audio['hop_length'], 2, 9, 0], [1, C.audio['hop_length'] * 10, C.audio['hop_length'], 2, 9, False, 0],
[1, C.audio['hop_length'], C.audio['hop_length'], 2, 10, 0], [1, C.audio['hop_length'], C.audio['hop_length'], 2, 10, True, 0],
[1, C.audio['hop_length'], C.audio['hop_length'], 2, "mold", 0], [1, C.audio['hop_length'], C.audio['hop_length'], 2, "mold", False, 0],
[1, C.audio['hop_length'] * 5, C.audio['hop_length'], 4, 10, 2], [1, C.audio['hop_length'] * 5, C.audio['hop_length'], 4, 10, False, 2],
[1, C.audio['hop_length'] * 5, C.audio['hop_length'], 2, "mold", 0], [1, C.audio['hop_length'] * 5, C.audio['hop_length'], 2, "mold", False, 0],
] ]
for param in params: for param in params:
print(param) print(param)