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Add FastSpeechConfig

pull/800/head
Eren Gölge 2021-09-11 10:20:37 +00:00
parent 1e7db32e90
commit cbbc9e0172
3 changed files with 165 additions and 8 deletions
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151
TTS/tts/configs/fast_speech_config.py Normal file
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@ -0,0 +1,151 @@
from dataclasses import dataclass, field
from typing import List
from TTS.tts.configs.shared_configs import BaseTTSConfig
from TTS.tts.models.forward_tts import ForwardTTSArgs
@dataclass
class FastSpeechConfig(BaseTTSConfig):
"""Configure `ForwardTTS` as FastSpeech model.
Example:
>>> from TTS.tts.configs import FastSpeechConfig
>>> config = FastSpeechConfig()
Args:
model (str):
Model name used for selecting the right model at initialization. Defaults to `fast_pitch`.
base_model (str):
Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate
the base model rather than searching for the `model` implementation. Defaults to `forward_tts`.
model_args (Coqpit):
Model class arguments. Check `FastSpeechArgs` for more details. Defaults to `FastSpeechArgs()`.
data_dep_init_steps (int):
Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
Activation Normalization that pre-computes normalization stats at the beginning and use the same values
for the rest. Defaults to 10.
use_speaker_embedding (bool):
enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
in the multi-speaker mode. Defaults to False.
use_d_vector_file (bool):
enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
d_vector_file (str):
Path to the file including pre-computed speaker embeddings. Defaults to None.
d_vector_dim (int):
Dimension of the external speaker embeddings. Defaults to 0.
optimizer (str):
Name of the model optimizer. Defaults to `Adam`.
optimizer_params (dict):
Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
lr_scheduler (str):
Name of the learning rate scheduler. Defaults to `Noam`.
lr_scheduler_params (dict):
Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
lr (float):
Initial learning rate. Defaults to `1e-3`.
grad_clip (float):
Gradient norm clipping value. Defaults to `5.0`.
spec_loss_type (str):
Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
duration_loss_type (str):
Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `mse`.
use_ssim_loss (bool):
Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
wd (float):
Weight decay coefficient. Defaults to `1e-7`.
ssim_loss_alpha (float):
Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
dur_loss_alpha (float):
Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
spec_loss_alpha (float):
Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
pitch_loss_alpha (float):
Weight for the pitch predictor's loss. If set 0, disables the pitch predictor. Defaults to 1.0.
binary_loss_alpha (float):
Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
binary_align_loss_start_step (int):
Start binary alignment loss after this many steps. Defaults to 20000.
min_seq_len (int):
Minimum input sequence length to be used at training.
max_seq_len (int):
Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
"""
model: str = "fast_speech"
base_model: str = "forward_tts"
# model specific params
model_args: ForwardTTSArgs = ForwardTTSArgs(use_pitch=False)
# multi-speaker settings
use_speaker_embedding: bool = False
use_d_vector_file: bool = False
d_vector_file: str = False
d_vector_dim: int = 0
# optimizer parameters
optimizer: str = "Adam"
optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
lr_scheduler: str = "NoamLR"
lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
lr: float = 1e-4
grad_clip: float = 5.0
# loss params
spec_loss_type: str = "mse"
duration_loss_type: str = "mse"
use_ssim_loss: bool = True
ssim_loss_alpha: float = 1.0
dur_loss_alpha: float = 1.0
spec_loss_alpha: float = 1.0
pitch_loss_alpha: float = 0.0
aligner_loss_alpha: float = 1.0
binary_align_loss_alpha: float = 1.0
binary_align_loss_start_step: int = 20000
# overrides
min_seq_len: int = 13
max_seq_len: int = 200
r: int = 1 # DO NOT CHANGE
# dataset configs
compute_f0: bool = True
f0_cache_path: str = None
# testing
test_sentences: List[str] = field(
default_factory=lambda: [
"It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
"Be a voice, not an echo.",
"I'm sorry Dave. I'm afraid I can't do that.",
"This cake is great. It's so delicious and moist.",
"Prior to November 22, 1963.",
]
)

5
TTS/tts/configs/speedy_speech_config.py
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@ -119,6 +119,7 @@ class SpeedySpeechConfig(BaseTTSConfig):
hidden_channels=128,
num_speakers=0,
positional_encoding=True,
detach_duration_predictor=True
)
# multi-speaker settings
@ -128,7 +129,7 @@ class SpeedySpeechConfig(BaseTTSConfig):
d_vector_dim: int = 0
# optimizer parameters
optimizer: str = "RAdam"
optimizer: str = "Adam"
optimizer_params: dict = field(default_factory=lambda: {"betas": [0.9, 0.998], "weight_decay": 1e-6})
lr_scheduler: str = "NoamLR"
lr_scheduler_params: dict = field(default_factory=lambda: {"warmup_steps": 4000})
@ -138,7 +139,7 @@ class SpeedySpeechConfig(BaseTTSConfig):
# loss params
spec_loss_type: str = "l1"
duration_loss_type: str = "huber"
use_ssim_loss: bool = True
use_ssim_loss: bool = False
ssim_loss_alpha: float = 1.0
dur_loss_alpha: float = 1.0
spec_loss_alpha: float = 1.0

17
TTS/tts/models/forward_tts.py
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@ -99,6 +99,7 @@ class ForwardTTSArgs(Coqpit):
max_duration (int):
Maximum duration accepted by the model. Defaults to 75.
"""
num_chars: int = None
@ -264,18 +265,22 @@ class ForwardTTS(BaseTTS):
"""Generate attention alignment map from durations and
expand encoder outputs
Shapes
Shapes:
- en: :math:`(B, D_{en}, T_{en})`
- dr: :math:`(B, T_{en})`
- x_mask: :math:`(B, T_{en})`
- y_mask: :math:`(B, T_{de})`
Examples:
- encoder output: :math:`[a,b,c,d]`
- durations: :math:`[1, 3, 2, 1]`
Examples::
- expanded: :math:`[a, b, b, b, c, c, d]`
- attention map: :math:`[[0, 0, 0, 0, 0, 0, 1], [0, 0, 0, 0, 1, 1, 0], [0, 1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0]]`
encoder output: [a,b,c,d]
durations: [1, 3, 2, 1]
expanded: [a, b, b, b, c, c, d]
attention map: [[0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 1, 1, 0],
[0, 1, 1, 1, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0]]
"""
attn = self.generate_attn(dr, x_mask, y_mask)
o_en_ex = torch.matmul(attn.squeeze(1).transpose(1, 2).to(en.dtype), en.transpose(1, 2)).transpose(1, 2)