mirror of https://github.com/coqui-ai/TTS.git
pylint and test fixes
erogol
parent
2abe3df153
commit
fede46e96e
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@ -13,7 +13,6 @@ Sample run on LJSpeech dataset.
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import argparse
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import glob
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import importlib
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import os
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@ -22,7 +21,7 @@ import torch
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from torch.utils.data import DataLoader
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from tqdm import tqdm
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from TTS.tts.datasets.TTSDataset import MyDataset
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from TTS.tts.utils.generic_utils import sequence_mask, setup_model
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from TTS.tts.utils.generic_utils import setup_model
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from TTS.tts.utils.io import load_checkpoint
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from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols
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from TTS.utils.audio import AudioProcessor
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@ -101,7 +100,7 @@ if __name__ == '__main__':
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ap=ap,
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meta_data=meta_data,
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tp=C.characters if 'characters' in C.keys() else None,
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add_blank=c['add_blank'] if 'add_blank' in C.keys() else False,
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add_blank=C['add_blank'] if 'add_blank' in C.keys() else False,
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use_phonemes=C.use_phonemes,
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phoneme_cache_path=C.phoneme_cache_path,
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phoneme_language=C.phoneme_language,
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@ -155,7 +155,7 @@ def train(data_loader, model, criterion, optimizer, scheduler,
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# format data
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text_input, text_lengths, mel_targets, mel_lengths, speaker_c,\
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avg_text_length, avg_spec_length, attn_mask, dur_target, item_idx = format_data(data)
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avg_text_length, avg_spec_length, _, dur_target, _ = format_data(data)
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loader_time = time.time() - end_time
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@ -302,7 +302,7 @@ def evaluate(data_loader, model, criterion, ap, global_step, epoch):
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# format data
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text_input, text_lengths, mel_targets, mel_lengths, speaker_c,\
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avg_text_length, avg_spec_length, attn_mask, dur_target, item_idx = format_data(data)
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_, _, _, dur_target, _ = format_data(data)
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# forward pass model
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with torch.cuda.amp.autocast(enabled=c.mixed_precision):
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@ -379,7 +379,7 @@ class MonotonicDynamicConvolutionAttention(nn.Module):
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def __init__(
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self,
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query_dim,
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embedding_dim,
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embedding_dim, # pylint: disable=unused-argument
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attention_dim,
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static_filter_dim,
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static_kernel_size,
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@ -447,7 +447,7 @@ class MonotonicDynamicConvolutionAttention(nn.Module):
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context = torch.bmm(attention_weights.unsqueeze(1), inputs).squeeze(1)
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return context
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def preprocess_inputs(self, inputs):
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def preprocess_inputs(self, inputs): # pylint: disable=no-self-use
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return None
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def init_states(self, inputs):
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@ -1,15 +1,17 @@
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import torch
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from torch import nn
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from .normalization import TemporalBatchNorm1d
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class ZeroTemporalPad(nn.ZeroPad2d):
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class ZeroTemporalPad(nn.Module):
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"""Pad sequences to equal lentgh in the temporal dimension"""
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def __init__(self, kernel_size, dilation):
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super().__init__()
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total_pad = (dilation * (kernel_size - 1))
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begin = total_pad // 2
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end = total_pad - begin
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super(ZeroTemporalPad, self).__init__((0, 0, begin, end))
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self.pad_layer = nn.ZeroPad2d((0, 0, begin, end))
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def forward(self, x):
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return self.pad_layer(x)
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class ConvBN(nn.Module):
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@ -116,6 +116,8 @@ class Encoder(nn.Module):
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hidden_channels,
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kernel_size=5,
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num_layers=3 + num_layers)
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else:
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raise ValueError(" [!] Unkown encoder type.")
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# final projection layers
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self.proj_m = nn.Conv1d(hidden_channels, out_channels, 1)
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@ -241,6 +241,7 @@ class GuidedAttentionLoss(torch.nn.Module):
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class Huber(nn.Module):
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# pylint: disable=R0201
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def forward(self, x, y, length=None):
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"""
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Shapes:
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@ -1,11 +1,5 @@
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import torch
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from torch import nn
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from torch.nn import functional as F
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from TTS.tts.layers.glow_tts.transformer import Transformer
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from TTS.tts.layers.glow_tts.glow import ConvLayerNorm
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from TTS.tts.utils.generic_utils import sequence_mask
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from TTS.tts.layers.generic.res_conv_bn import ResidualConvBNBlock, ConvBNBlock
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from TTS.tts.layers.generic.res_conv_bn import ConvBNBlock, ResidualConvBNBlock
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class Decoder(nn.Module):
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@ -35,7 +29,8 @@ class Decoder(nn.Module):
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nn.Conv1d(hidden_channels, out_channels, 1),
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)
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def forward(self, x, x_mask, g=None):
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def forward(self, x, x_mask, g=None): # pylint: disable=unused-argument
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# TODO: implement multi-speaker
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o = self.decoder(x, x_mask)
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o = self.post_conv(o) + x
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return self.post_net(o)
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return self.post_net(o)
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@ -17,6 +17,7 @@ class PositionalEncoding(nn.Module):
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"""
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def __init__(self, dim, dropout=0.0, max_len=5000):
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super().__init__()
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if dim % 2 != 0:
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raise ValueError("Cannot use sin/cos positional encoding with "
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"odd dim (got dim={:d})".format(dim))
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@ -27,7 +28,6 @@ class PositionalEncoding(nn.Module):
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pe[:, 0::2] = torch.sin(position.float() * div_term)
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pe[:, 1::2] = torch.cos(position.float() * div_term)
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pe = pe.unsqueeze(0).transpose(1, 2)
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super(PositionalEncoding, self).__init__()
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self.register_buffer('pe', pe)
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if dropout > 0:
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self.dropout = nn.Dropout(p=dropout)
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@ -125,7 +125,7 @@ class Encoder(nn.Module):
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num_layers=3,
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dropout_p=0.5)
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# text encoder
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self.encoder = Transformer(hidden_channels, **encoder_params)
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self.encoder = Transformer(hidden_channels, **encoder_params) # pylint: disable=unexpected-keyword-arg
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elif encoder_type.lower() == 'residual_conv_bn':
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self.pre = nn.Sequential(
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nn.Conv1d(hidden_channels, hidden_channels, 1), nn.ReLU())
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@ -139,7 +139,8 @@ class Encoder(nn.Module):
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self.post_bn = nn.BatchNorm1d(hidden_channels)
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self.post_conv2 = nn.Conv1d(hidden_channels, out_channels, 1)
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def forward(self, x, x_mask, g=None):
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def forward(self, x, x_mask, g=None): # pylint: disable=unused-argument
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# TODO: implement multi-speaker
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if self.encoder_type == 'transformer':
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o = self.pre(x, x_mask)
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else:
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@ -8,6 +8,7 @@ from TTS.tts.layers.glow_tts.monotonic_align import generate_path
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class SpeedySpeech(nn.Module):
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# pylint: disable=dangerous-default-value
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def __init__(
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self,
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num_chars,
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@ -40,7 +41,8 @@ class SpeedySpeech(nn.Module):
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decoder_residual_conv_bn_params)
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self.duration_predictor = DurationPredictor(hidden_channels)
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def expand_encoder_outputs(self, en, dr, x_mask, y_mask):
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@staticmethod
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def expand_encoder_outputs(en, dr, x_mask, y_mask):
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attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2)
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attn = generate_path(dr, attn_mask.squeeze(1)).to(en.dtype)
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o_en_ex = torch.matmul(
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@ -54,10 +56,8 @@ class SpeedySpeech(nn.Module):
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o_dr = torch.round(o_dr)
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return o_dr
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def forward(self, x, x_lengths, y_lengths, dr, g=None):
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"""
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docstring
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"""
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def forward(self, x, x_lengths, y_lengths, dr, g=None): # pylint: disable=unused-argument
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# TODO: multi-speaker
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# [B, T, C]
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x_emb = self.emb(x)
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# [B, C, T]
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@ -88,7 +88,8 @@ class SpeedySpeech(nn.Module):
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return o_de, o_dr_log.squeeze(1), attn.transpose(1, 2)
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def inference(self, x, x_lengths, g=None):
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def inference(self, x, x_lengths, g=None): # pylint: disable=unused-argument
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# TODO: multi-speaker
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# pad input to prevent dropping the last word
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x = torch.nn.functional.pad(x, pad=(0, 5), mode='constant', value=0)
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@ -107,7 +107,6 @@ def setup_model(num_chars, num_speakers, c, speaker_embedding_dim=None):
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hidden_channels_ffn=768,
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hidden_channels_dp=256,
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out_channels=c.audio['num_mels'],
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kernel_size=3,
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num_heads=2,
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num_layers_enc=6,
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encoder_type=c.encoder_type,
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@ -86,7 +86,7 @@
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"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
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"seq_len_norm": false, // Normalize eash sample loss with its length to alleviate imbalanced datasets. Use it if your dataset is small or has skewed distribution of sequence lengths.
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"encoder_type": "gatedconv",
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"encoder_type": "transformer",
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// TENSORBOARD and LOGGING
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"print_step": 25, // Number of steps to log training on console.
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@ -0,0 +1,149 @@
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{
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"model": "speedy_speech",
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"run_name": "test_sample_dataset_run",
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"run_description": "sample dataset test run",
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// AUDIO PARAMETERS
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"audio":{
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// stft parameters
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"fft_size": 1024, // number of stft frequency levels. Size of the linear spectogram frame.
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"win_length": 1024, // stft window length in ms.
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"hop_length": 256, // stft window hop-lengh in ms.
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"frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
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"frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
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// Audio processing parameters
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"sample_rate": 22050, // DATASET-RELATED: wav sample-rate.
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"preemphasis": 0.0, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
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"ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
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// Silence trimming
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"do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (true), TWEB (false), Nancy (true)
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"trim_db": 60, // threshold for timming silence. Set this according to your dataset.
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// Griffin-Lim
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"power": 1.5, // value to sharpen wav signals after GL algorithm.
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"griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
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// MelSpectrogram parameters
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"num_mels": 80, // size of the mel spec frame.
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"mel_fmin": 50.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
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"mel_fmax": 7600.0, // maximum freq level for mel-spec. Tune for dataset!!
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"spec_gain": 1,
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// Normalization parameters
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"signal_norm": true, // normalize spec values. Mean-Var normalization if 'stats_path' is defined otherwise range normalization defined by the other params.
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"min_level_db": -100, // lower bound for normalization
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"symmetric_norm": true, // move normalization to range [-1, 1]
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"max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
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"clip_norm": true, // clip normalized values into the range.
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"stats_path": "/home/erogol/Data/LJSpeech-1.1/scale_stats.npy" // DO NOT USE WITH MULTI_SPEAKER MODEL. scaler stats file computed by 'compute_statistics.py'. If it is defined, mean-std based notmalization is used and other normalization params are ignored
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},
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// VOCABULARY PARAMETERS
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// if custom character set is not defined,
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// default set in symbols.py is used
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// "characters":{
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// "pad": "_",
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// "eos": "&",
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// "bos": "*",
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// "characters": "ABCDEFGHIJKLMNOPQRSTUVWXYZÇÃÀÁÂÊÉÍÓÔÕÚÛabcdefghijklmnopqrstuvwxyzçãàáâêéíóôõúû!(),-.:;? ",
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// "punctuations":"!'(),-.:;? ",
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// "phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ'̃' "
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// },
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"add_blank": false, // if true add a new token after each token of the sentence. This increases the size of the input sequence, but has considerably improved the prosody of the GlowTTS model.
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// DISTRIBUTED TRAINING
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"distributed":{
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"backend": "nccl",
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"url": "tcp:\/\/localhost:54321"
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},
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"reinit_layers": [], // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
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// MODEL PARAMETERS
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"positional_encoding": true,
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"encoder_type": "residual_conv_bn",
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"encoder_params":{
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"kernel_size": 4,
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"dilations": [1, 2, 4, 1, 2, 4, 1, 2, 4, 1, 2, 4, 1],
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"num_conv_blocks": 2,
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"num_res_blocks": 13
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},
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"decoder_residual_conv_bn_params":{
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"kernel_size": 4,
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"dilations": [1, 2, 4, 8, 1, 2, 4, 8, 1, 2, 4, 8, 1, 2, 4, 8, 1],
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"num_conv_blocks": 2,
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"num_res_blocks": 17
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},
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// TRAINING
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"batch_size":64, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
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"eval_batch_size":32,
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"r": 1, // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
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"loss_masking": true, // enable / disable loss masking against the sequence padding.
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// LOSS PARAMETERS
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"ssim_alpha": 1,
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"l1_alpha": 1,
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"huber_alpha": 1,
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// VALIDATION
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"run_eval": true,
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"test_delay_epochs": -1, //Until attention is aligned, testing only wastes computation time.
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"test_sentences_file": null, // set a file to load sentences to be used for testing. If it is null then we use default english sentences.
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// OPTIMIZER
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"noam_schedule": true, // use noam warmup and lr schedule.
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"grad_clip": 1.0, // upper limit for gradients for clipping.
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"epochs": 1, // total number of epochs to train.
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"lr": 0.002, // Initial learning rate. If Noam decay is active, maximum learning rate.
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"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
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// TENSORBOARD and LOGGING
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"print_step": 1, // Number of steps to log training on console.
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"tb_plot_step": 100, // Number of steps to plot TB training figures.
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"print_eval": false, // If True, it prints intermediate loss values in evalulation.
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"save_step": 5000, // Number of training steps expected to save traninpg stats and checkpoints.
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"checkpoint": true, // If true, it saves checkpoints per "save_step"
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"tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.:set n
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"mixed_precision": false,
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// DATA LOADING
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"text_cleaner": "english_cleaners",
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"enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
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"num_loader_workers": 0, // number of training data loader processes. Don't set it too big. 4-8 are good values.
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"num_val_loader_workers": 0, // number of evaluation data loader processes.
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"batch_group_size": 0, //Number of batches to shuffle after bucketing.
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"min_seq_len": 2, // DATASET-RELATED: minimum text length to use in training
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"max_seq_len": 300, // DATASET-RELATED: maximum text length
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"compute_f0": false, // compute f0 values in data-loader
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"compute_input_seq_cache": false, // if true, text sequences are computed before starting training. If phonemes are enabled, they are also computed at this stage.
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// PATHS
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"output_path": "tests/train_outputs/",
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// PHONEMES
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"phoneme_cache_path": "tests/train_outputs/phoneme_cache/", // phoneme computation is slow, therefore, it caches results in the given folder.
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"use_phonemes": true, // use phonemes instead of raw characters. It is suggested for better pronoun[ciation.
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"phoneme_language": "en-us", // depending on your target language, pick one from https://github.com/bootphon/phonemizer#languages
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// MULTI-SPEAKER and GST
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"use_speaker_embedding": false, // use speaker embedding to enable multi-speaker learning.
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"use_external_speaker_embedding_file": false, // if true, forces the model to use external embedding per sample instead of nn.embeddings, that is, it supports external embeddings such as those used at: https://arxiv.org/abs /1806.04558
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"external_speaker_embedding_file": "/home/erogol/Data/libritts/speakers.json", // if not null and use_external_speaker_embedding_file is true, it is used to load a specific embedding file and thus uses these embeddings instead of nn.embeddings, that is, it supports external embeddings such as those used at: https://arxiv.org/abs /1806.04558
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// DATASETS
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"datasets": // List of datasets. They all merged and they get different speaker_ids.
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[
|
||||
{
|
||||
"name": "ljspeech",
|
||||
"path": "tests/data/ljspeech/",
|
||||
"meta_file_train": "metadata.csv",
|
||||
"meta_file_val": "metadata.csv",
|
||||
"meta_file_attn_mask": "tests/data/ljspeech/metadata_attn_mask.txt"
|
||||
}
|
||||
]
|
||||
}
|
||||
|
|
@ -44,10 +44,9 @@ class GlowTTSTrainTest(unittest.TestCase):
|
|||
# model to train
|
||||
model = GlowTts(num_chars=32,
|
||||
hidden_channels=128,
|
||||
filter_channels=32,
|
||||
filter_channels_dp=32,
|
||||
hidden_channels_ffn=32,
|
||||
hidden_channels_dp=32,
|
||||
out_channels=80,
|
||||
kernel_size=3,
|
||||
num_heads=2,
|
||||
num_layers_enc=6,
|
||||
dropout_p=0.1,
|
||||
|
|
@ -72,10 +71,9 @@ class GlowTTSTrainTest(unittest.TestCase):
|
|||
# reference model to compare model weights
|
||||
model_ref = GlowTts(num_chars=32,
|
||||
hidden_channels=128,
|
||||
filter_channels=32,
|
||||
filter_channels_dp=32,
|
||||
hidden_channels_ffn=32,
|
||||
hidden_channels_dp=32,
|
||||
out_channels=80,
|
||||
kernel_size=3,
|
||||
num_heads=2,
|
||||
num_layers_enc=6,
|
||||
dropout_p=0.1,
|
||||
|
|
|
|||
|
|
@ -0,0 +1,98 @@
|
|||
import torch
|
||||
|
||||
from TTS.tts.layers.speedy_speech.encoder import Encoder
|
||||
from TTS.tts.layers.speedy_speech.decoder import Decoder
|
||||
from TTS.tts.layers.speedy_speech.duration_predictor import DurationPredictor
|
||||
from TTS.tts.utils.generic_utils import sequence_mask
|
||||
from TTS.tts.models.speedy_speech import SpeedySpeech
|
||||
|
||||
|
||||
use_cuda = torch.cuda.is_available()
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
|
||||
def test_encoder():
|
||||
input_dummy = torch.rand(8, 14, 37).to(device)
|
||||
input_lengths = torch.randint(31, 37, (8, )).long().to(device)
|
||||
input_lengths[-1] = 37
|
||||
input_mask = torch.unsqueeze(sequence_mask(input_lengths, input_dummy.size(2)),
|
||||
1).to(device)
|
||||
|
||||
# residual bn conv encoder
|
||||
layer = Encoder(out_channels=11,
|
||||
hidden_channels=14,
|
||||
encoder_type='residual_conv_bn').to(device)
|
||||
output = layer(input_dummy, input_mask)
|
||||
assert list(output.shape)==[8, 11, 37]
|
||||
|
||||
# transformer encoder
|
||||
layer = Encoder(out_channels=11,
|
||||
hidden_channels=14,
|
||||
encoder_type='transformer',
|
||||
encoder_params={
|
||||
'hidden_channels_ffn': 768,
|
||||
'num_heads': 2,
|
||||
"kernel_size": 3,
|
||||
"dropout_p": 0.1,
|
||||
"num_layers": 6,
|
||||
"rel_attn_window_size": 4,
|
||||
"input_length": None
|
||||
}).to(device)
|
||||
output = layer(input_dummy, input_mask)
|
||||
assert list(output.shape)==[8, 11, 37]
|
||||
|
||||
|
||||
def test_decoder():
|
||||
input_dummy = torch.rand(8, 128, 37).to(device)
|
||||
input_lengths = torch.randint(31, 37, (8, )).long().to(device)
|
||||
input_lengths[-1] = 37
|
||||
|
||||
input_mask = torch.unsqueeze(sequence_mask(input_lengths, input_dummy.size(2)), 1).to(device)
|
||||
|
||||
layer = Decoder(out_channels=11, hidden_channels=128).to(device)
|
||||
output = layer(input_dummy, input_mask)
|
||||
assert list(output.shape) == [8, 11, 37]
|
||||
|
||||
|
||||
def test_duration_predictor():
|
||||
input_dummy = torch.rand(8, 128, 27).to(device)
|
||||
input_lengths = torch.randint(20, 27, (8, )).long().to(device)
|
||||
input_lengths[-1] = 27
|
||||
|
||||
x_mask = torch.unsqueeze(sequence_mask(input_lengths, input_dummy.size(2)), 1).to(device)
|
||||
|
||||
layer = DurationPredictor(hidden_channels=128).to(device)
|
||||
|
||||
output = layer(input_dummy, x_mask)
|
||||
assert list(output.shape)==[8, 1, 27]
|
||||
|
||||
|
||||
def test_speedy_speech():
|
||||
num_chars = 7
|
||||
B = 8
|
||||
T_en = 37
|
||||
T_de = 74
|
||||
|
||||
x_dummy = torch.randint(0, 7, (B, T_en)).long().to(device)
|
||||
x_lengths = torch.randint(31, T_en, (B, )).long().to(device)
|
||||
x_lengths[-1] = T_en
|
||||
|
||||
# set durations. max total duration should be equal to T_de
|
||||
durations = torch.randint(1, 4, (B, T_en))
|
||||
durations = durations * (T_de / durations.sum(1)).unsqueeze(1)
|
||||
durations = durations.to(torch.long).to(device)
|
||||
max_dur = durations.sum(1).max()
|
||||
durations[:, 0] += T_de - max_dur if T_de > max_dur else 0
|
||||
|
||||
y_lengths = durations.sum(1)
|
||||
|
||||
model = SpeedySpeech(num_chars, out_channels=80, hidden_channels=128)
|
||||
if use_cuda:
|
||||
model.cuda()
|
||||
|
||||
# forward pass
|
||||
o_de, o_dr, attn = model(x_dummy, x_lengths, y_lengths, durations)
|
||||
|
||||
assert list(o_de.shape) == [B, 80, T_de], f"{list(o_de.shape)}"
|
||||
assert list(attn.shape) == [B, T_de, T_en]
|
||||
assert list(o_dr.shape) == [B, T_en]
|
||||
Loading…
Reference in New Issue