from dataclasses import dataclass, field
from typing import Dict, List, Tuple, Union
import torch
from coqpit import Coqpit
from torch import nn
from torch.cuda.amp.autocast_mode import autocast
from TTS.tts.layers.feed_forward.decoder import Decoder
from TTS.tts.layers.feed_forward.encoder import Encoder
from TTS.tts.layers.generic.aligner import AlignmentNetwork
from TTS.tts.layers.generic.pos_encoding import PositionalEncoding
from TTS.tts.layers.glow_tts.duration_predictor import DurationPredictor
from TTS.tts.models.base_tts import BaseTTS
from TTS.tts.utils.helpers import average_over_durations, generate_path, maximum_path, sequence_mask
from TTS.tts.utils.speakers import SpeakerManager
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.tts.utils.visual import plot_alignment, plot_avg_energy, plot_avg_pitch, plot_spectrogram
from TTS.utils.io import load_fsspec
[docs]
@dataclass
class ForwardTTSArgs(Coqpit):
"""ForwardTTS Model arguments.
Args:
num_chars (int):
Number of characters in the vocabulary. Defaults to 100.
out_channels (int):
Number of output channels. Defaults to 80.
hidden_channels (int):
Number of base hidden channels of the model. Defaults to 512.
use_aligner (bool):
Whether to use aligner network to learn the text to speech alignment or use pre-computed durations.
If set False, durations should be computed by `TTS/bin/compute_attention_masks.py` and path to the
pre-computed durations must be provided to `config.datasets[0].meta_file_attn_mask`. Defaults to True.
use_pitch (bool):
Use pitch predictor to learn the pitch. Defaults to True.
use_energy (bool):
Use energy predictor to learn the energy. Defaults to True.
duration_predictor_hidden_channels (int):
Number of hidden channels in the duration predictor. Defaults to 256.
duration_predictor_dropout_p (float):
Dropout rate for the duration predictor. Defaults to 0.1.
duration_predictor_kernel_size (int):
Kernel size of conv layers in the duration predictor. Defaults to 3.
pitch_predictor_hidden_channels (int):
Number of hidden channels in the pitch predictor. Defaults to 256.
pitch_predictor_dropout_p (float):
Dropout rate for the pitch predictor. Defaults to 0.1.
pitch_predictor_kernel_size (int):
Kernel size of conv layers in the pitch predictor. Defaults to 3.
pitch_embedding_kernel_size (int):
Kernel size of the projection layer in the pitch predictor. Defaults to 3.
energy_predictor_hidden_channels (int):
Number of hidden channels in the energy predictor. Defaults to 256.
energy_predictor_dropout_p (float):
Dropout rate for the energy predictor. Defaults to 0.1.
energy_predictor_kernel_size (int):
Kernel size of conv layers in the energy predictor. Defaults to 3.
energy_embedding_kernel_size (int):
Kernel size of the projection layer in the energy predictor. Defaults to 3.
positional_encoding (bool):
Whether to use positional encoding. Defaults to True.
positional_encoding_use_scale (bool):
Whether to use a learnable scale coeff in the positional encoding. Defaults to True.
length_scale (int):
Length scale that multiplies the predicted durations. Larger values result slower speech. Defaults to 1.0.
encoder_type (str):
Type of the encoder module. One of the encoders available in :class:`TTS.tts.layers.feed_forward.encoder`.
Defaults to `fftransformer` as in the paper.
encoder_params (dict):
Parameters of the encoder module. Defaults to ```{"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1}```
decoder_type (str):
Type of the decoder module. One of the decoders available in :class:`TTS.tts.layers.feed_forward.decoder`.
Defaults to `fftransformer` as in the paper.
decoder_params (str):
Parameters of the decoder module. Defaults to ```{"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1}```
detach_duration_predictor (bool):
Detach the input to the duration predictor from the earlier computation graph so that the duraiton loss
does not pass to the earlier layers. Defaults to True.
max_duration (int):
Maximum duration accepted by the model. Defaults to 75.
num_speakers (int):
Number of speakers for the speaker embedding layer. Defaults to 0.
speakers_file (str):
Path to the speaker mapping file for the Speaker Manager. Defaults to None.
speaker_embedding_channels (int):
Number of speaker embedding channels. Defaults to 256.
use_d_vector_file (bool):
Enable/Disable the use of d-vectors for multi-speaker training. Defaults to False.
d_vector_dim (int):
Number of d-vector channels. Defaults to 0.
"""
num_chars: int = None
out_channels: int = 80
hidden_channels: int = 384
use_aligner: bool = True
# pitch params
use_pitch: bool = True
pitch_predictor_hidden_channels: int = 256
pitch_predictor_kernel_size: int = 3
pitch_predictor_dropout_p: float = 0.1
pitch_embedding_kernel_size: int = 3
# energy params
use_energy: bool = False
energy_predictor_hidden_channels: int = 256
energy_predictor_kernel_size: int = 3
energy_predictor_dropout_p: float = 0.1
energy_embedding_kernel_size: int = 3
# duration params
duration_predictor_hidden_channels: int = 256
duration_predictor_kernel_size: int = 3
duration_predictor_dropout_p: float = 0.1
positional_encoding: bool = True
poisitonal_encoding_use_scale: bool = True
length_scale: int = 1
encoder_type: str = "fftransformer"
encoder_params: dict = field(
default_factory=lambda: {"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1}
)
decoder_type: str = "fftransformer"
decoder_params: dict = field(
default_factory=lambda: {"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1}
)
detach_duration_predictor: bool = False
max_duration: int = 75
num_speakers: int = 1
use_speaker_embedding: bool = False
speakers_file: str = None
use_d_vector_file: bool = False
d_vector_dim: int = None
d_vector_file: str = None
[docs]
class ForwardTTS(BaseTTS):
"""General forward TTS model implementation that uses an encoder-decoder architecture with an optional alignment
network and a pitch predictor.
If the alignment network is used, the model learns the text-to-speech alignment
from the data instead of using pre-computed durations.
If the pitch predictor is used, the model trains a pitch predictor that predicts average pitch value for each
input character as in the FastPitch model.
`ForwardTTS` can be configured to one of these architectures,
- FastPitch
- SpeedySpeech
- FastSpeech
- FastSpeech2 (requires average speech energy predictor)
Args:
config (Coqpit): Model coqpit class.
speaker_manager (SpeakerManager): Speaker manager for multi-speaker training. Only used for multi-speaker models.
Defaults to None.
Examples:
>>> from TTS.tts.models.fast_pitch import ForwardTTS, ForwardTTSArgs
>>> config = ForwardTTSArgs()
>>> model = ForwardTTS(config)
"""
# pylint: disable=dangerous-default-value
def __init__(
self,
config: Coqpit,
ap: "AudioProcessor" = None,
tokenizer: "TTSTokenizer" = None,
speaker_manager: SpeakerManager = None,
):
super().__init__(config, ap, tokenizer, speaker_manager)
self._set_model_args(config)
self.init_multispeaker(config)
self.max_duration = self.args.max_duration
self.use_aligner = self.args.use_aligner
self.use_pitch = self.args.use_pitch
self.use_energy = self.args.use_energy
self.binary_loss_weight = 0.0
self.length_scale = (
float(self.args.length_scale) if isinstance(self.args.length_scale, int) else self.args.length_scale
)
self.emb = nn.Embedding(self.args.num_chars, self.args.hidden_channels)
self.encoder = Encoder(
self.args.hidden_channels,
self.args.hidden_channels,
self.args.encoder_type,
self.args.encoder_params,
self.embedded_speaker_dim,
)
if self.args.positional_encoding:
self.pos_encoder = PositionalEncoding(self.args.hidden_channels)
self.decoder = Decoder(
self.args.out_channels,
self.args.hidden_channels,
self.args.decoder_type,
self.args.decoder_params,
)
self.duration_predictor = DurationPredictor(
self.args.hidden_channels,
self.args.duration_predictor_hidden_channels,
self.args.duration_predictor_kernel_size,
self.args.duration_predictor_dropout_p,
)
if self.args.use_pitch:
self.pitch_predictor = DurationPredictor(
self.args.hidden_channels,
self.args.pitch_predictor_hidden_channels,
self.args.pitch_predictor_kernel_size,
self.args.pitch_predictor_dropout_p,
)
self.pitch_emb = nn.Conv1d(
1,
self.args.hidden_channels,
kernel_size=self.args.pitch_embedding_kernel_size,
padding=int((self.args.pitch_embedding_kernel_size - 1) / 2),
)
if self.args.use_energy:
self.energy_predictor = DurationPredictor(
self.args.hidden_channels,
self.args.energy_predictor_hidden_channels,
self.args.energy_predictor_kernel_size,
self.args.energy_predictor_dropout_p,
)
self.energy_emb = nn.Conv1d(
1,
self.args.hidden_channels,
kernel_size=self.args.energy_embedding_kernel_size,
padding=int((self.args.energy_embedding_kernel_size - 1) / 2),
)
if self.args.use_aligner:
self.aligner = AlignmentNetwork(
in_query_channels=self.args.out_channels, in_key_channels=self.args.hidden_channels
)
[docs]
def init_multispeaker(self, config: Coqpit):
"""Init for multi-speaker training.
Args:
config (Coqpit): Model configuration.
"""
self.embedded_speaker_dim = 0
# init speaker manager
if self.speaker_manager is None and (config.use_d_vector_file or config.use_speaker_embedding):
raise ValueError(
" > SpeakerManager is not provided. You must provide the SpeakerManager before initializing a multi-speaker model."
)
# set number of speakers
if self.speaker_manager is not None:
self.num_speakers = self.speaker_manager.num_speakers
# init d-vector embedding
if config.use_d_vector_file:
self.embedded_speaker_dim = config.d_vector_dim
if self.args.d_vector_dim != self.args.hidden_channels:
#self.proj_g = nn.Conv1d(self.args.d_vector_dim, self.args.hidden_channels, 1)
self.proj_g = nn.Linear(in_features=self.args.d_vector_dim, out_features=self.args.hidden_channels)
# init speaker embedding layer
if config.use_speaker_embedding and not config.use_d_vector_file:
print(" > Init speaker_embedding layer.")
self.emb_g = nn.Embedding(self.num_speakers, self.args.hidden_channels)
nn.init.uniform_(self.emb_g.weight, -0.1, 0.1)
[docs]
@staticmethod
def generate_attn(dr, x_mask, y_mask=None):
"""Generate an attention mask from the durations.
Shapes
- dr: :math:`(B, T_{en})`
- x_mask: :math:`(B, T_{en})`
- y_mask: :math:`(B, T_{de})`
"""
# compute decode mask from the durations
if y_mask is None:
y_lengths = dr.sum(1).long()
y_lengths[y_lengths < 1] = 1
y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).to(dr.dtype)
attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2)
attn = generate_path(dr, attn_mask.squeeze(1)).to(dr.dtype)
return attn
[docs]
def expand_encoder_outputs(self, en, dr, x_mask, y_mask):
"""Generate attention alignment map from durations and
expand encoder outputs
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: [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)
return o_en_ex, attn
def _forward_encoder(
self, x: torch.LongTensor, x_mask: torch.FloatTensor, g: torch.FloatTensor = None
) -> Tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
"""Encoding forward pass.
1. Embed speaker IDs if multi-speaker mode.
2. Embed character sequences.
3. Run the encoder network.
4. Sum encoder outputs and speaker embeddings
Args:
x (torch.LongTensor): Input sequence IDs.
x_mask (torch.FloatTensor): Input squence mask.
g (torch.FloatTensor, optional): Conditioning vectors. In general speaker embeddings. Defaults to None.
Returns:
Tuple[torch.tensor, torch.tensor, torch.tensor, torch.tensor, torch.tensor]:
encoder output, encoder output for the duration predictor, input sequence mask, speaker embeddings,
character embeddings
Shapes:
- x: :math:`(B, T_{en})`
- x_mask: :math:`(B, 1, T_{en})`
- g: :math:`(B, C)`
"""
if hasattr(self, "emb_g"):
g = g.type(torch.LongTensor)
g = self.emb_g(g) # [B, C, 1]
if g is not None:
g = g.unsqueeze(-1)
# [B, T, C]
x_emb = self.emb(x)
# encoder pass
#o_en = self.encoder(torch.transpose(x_emb, 1, -1), x_mask)
o_en = self.encoder(torch.transpose(x_emb, 1, -1), x_mask, g)
# speaker conditioning
# TODO: try different ways of conditioning
if g is not None:
if hasattr(self, "proj_g"):
g = self.proj_g(g.view(g.shape[0], -1)).unsqueeze(-1)
o_en = o_en + g
return o_en, x_mask, g, x_emb
def _forward_decoder(
self,
o_en: torch.FloatTensor,
dr: torch.IntTensor,
x_mask: torch.FloatTensor,
y_lengths: torch.IntTensor,
g: torch.FloatTensor,
) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
"""Decoding forward pass.
1. Compute the decoder output mask
2. Expand encoder output with the durations.
3. Apply position encoding.
4. Add speaker embeddings if multi-speaker mode.
5. Run the decoder.
Args:
o_en (torch.FloatTensor): Encoder output.
dr (torch.IntTensor): Ground truth durations or alignment network durations.
x_mask (torch.IntTensor): Input sequence mask.
y_lengths (torch.IntTensor): Output sequence lengths.
g (torch.FloatTensor): Conditioning vectors. In general speaker embeddings.
Returns:
Tuple[torch.FloatTensor, torch.FloatTensor]: Decoder output, attention map from durations.
"""
y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).to(o_en.dtype)
# expand o_en with durations
o_en_ex, attn = self.expand_encoder_outputs(o_en, dr, x_mask, y_mask)
# positional encoding
if hasattr(self, "pos_encoder"):
o_en_ex = self.pos_encoder(o_en_ex, y_mask)
# decoder pass
o_de = self.decoder(o_en_ex, y_mask, g=g)
return o_de.transpose(1, 2), attn.transpose(1, 2)
def _forward_pitch_predictor(
self,
o_en: torch.FloatTensor,
x_mask: torch.IntTensor,
pitch: torch.FloatTensor = None,
dr: torch.IntTensor = None,
) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
"""Pitch predictor forward pass.
1. Predict pitch from encoder outputs.
2. In training - Compute average pitch values for each input character from the ground truth pitch values.
3. Embed average pitch values.
Args:
o_en (torch.FloatTensor): Encoder output.
x_mask (torch.IntTensor): Input sequence mask.
pitch (torch.FloatTensor, optional): Ground truth pitch values. Defaults to None.
dr (torch.IntTensor, optional): Ground truth durations. Defaults to None.
Returns:
Tuple[torch.FloatTensor, torch.FloatTensor]: Pitch embedding, pitch prediction.
Shapes:
- o_en: :math:`(B, C, T_{en})`
- x_mask: :math:`(B, 1, T_{en})`
- pitch: :math:`(B, 1, T_{de})`
- dr: :math:`(B, T_{en})`
"""
o_pitch = self.pitch_predictor(o_en, x_mask)
if pitch is not None:
avg_pitch = average_over_durations(pitch, dr)
o_pitch_emb = self.pitch_emb(avg_pitch)
return o_pitch_emb, o_pitch, avg_pitch
o_pitch_emb = self.pitch_emb(o_pitch)
return o_pitch_emb, o_pitch
def _forward_energy_predictor(
self,
o_en: torch.FloatTensor,
x_mask: torch.IntTensor,
energy: torch.FloatTensor = None,
dr: torch.IntTensor = None,
) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
"""Energy predictor forward pass.
1. Predict energy from encoder outputs.
2. In training - Compute average pitch values for each input character from the ground truth pitch values.
3. Embed average energy values.
Args:
o_en (torch.FloatTensor): Encoder output.
x_mask (torch.IntTensor): Input sequence mask.
energy (torch.FloatTensor, optional): Ground truth energy values. Defaults to None.
dr (torch.IntTensor, optional): Ground truth durations. Defaults to None.
Returns:
Tuple[torch.FloatTensor, torch.FloatTensor]: Energy embedding, energy prediction.
Shapes:
- o_en: :math:`(B, C, T_{en})`
- x_mask: :math:`(B, 1, T_{en})`
- pitch: :math:`(B, 1, T_{de})`
- dr: :math:`(B, T_{en})`
"""
o_energy = self.energy_predictor(o_en, x_mask)
if energy is not None:
avg_energy = average_over_durations(energy, dr)
o_energy_emb = self.energy_emb(avg_energy)
return o_energy_emb, o_energy, avg_energy
o_energy_emb = self.energy_emb(o_energy)
return o_energy_emb, o_energy
def _forward_aligner(
self, x: torch.FloatTensor, y: torch.FloatTensor, x_mask: torch.IntTensor, y_mask: torch.IntTensor
) -> Tuple[torch.IntTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
"""Aligner forward pass.
1. Compute a mask to apply to the attention map.
2. Run the alignment network.
3. Apply MAS to compute the hard alignment map.
4. Compute the durations from the hard alignment map.
Args:
x (torch.FloatTensor): Input sequence.
y (torch.FloatTensor): Output sequence.
x_mask (torch.IntTensor): Input sequence mask.
y_mask (torch.IntTensor): Output sequence mask.
Returns:
Tuple[torch.IntTensor, torch.FloatTensor, torch.FloatTensor, torch.FloatTensor]:
Durations from the hard alignment map, soft alignment potentials, log scale alignment potentials,
hard alignment map.
Shapes:
- x: :math:`[B, T_en, C_en]`
- y: :math:`[B, T_de, C_de]`
- x_mask: :math:`[B, 1, T_en]`
- y_mask: :math:`[B, 1, T_de]`
- o_alignment_dur: :math:`[B, T_en]`
- alignment_soft: :math:`[B, T_en, T_de]`
- alignment_logprob: :math:`[B, 1, T_de, T_en]`
- alignment_mas: :math:`[B, T_en, T_de]`
"""
attn_mask = torch.unsqueeze(x_mask, -1) * torch.unsqueeze(y_mask, 2)
alignment_soft, alignment_logprob = self.aligner(y.transpose(1, 2), x.transpose(1, 2), x_mask, None)
alignment_mas = maximum_path(
alignment_soft.squeeze(1).transpose(1, 2).contiguous(), attn_mask.squeeze(1).contiguous()
)
o_alignment_dur = torch.sum(alignment_mas, -1).int()
alignment_soft = alignment_soft.squeeze(1).transpose(1, 2)
return o_alignment_dur, alignment_soft, alignment_logprob, alignment_mas
def _set_speaker_input(self, aux_input: Dict):
d_vectors = aux_input.get("d_vectors", None)
speaker_ids = aux_input.get("speaker_ids", None)
if d_vectors is not None and speaker_ids is not None:
raise ValueError("[!] Cannot use d-vectors and speaker-ids together.")
if speaker_ids is not None and not hasattr(self, "emb_g"):
raise ValueError("[!] Cannot use speaker-ids without enabling speaker embedding.")
g = speaker_ids if speaker_ids is not None else d_vectors
return g
[docs]
def forward(
self,
x: torch.LongTensor,
x_lengths: torch.LongTensor,
y_lengths: torch.LongTensor,
y: torch.FloatTensor = None,
dr: torch.IntTensor = None,
pitch: torch.FloatTensor = None,
energy: torch.FloatTensor = None,
aux_input: Dict = {"d_vectors": None, "speaker_ids": None}, # pylint: disable=unused-argument
) -> Dict:
"""Model's forward pass.
Args:
x (torch.LongTensor): Input character sequences.
x_lengths (torch.LongTensor): Input sequence lengths.
y_lengths (torch.LongTensor): Output sequnce lengths. Defaults to None.
y (torch.FloatTensor): Spectrogram frames. Only used when the alignment network is on. Defaults to None.
dr (torch.IntTensor): Character durations over the spectrogram frames. Only used when the alignment network is off. Defaults to None.
pitch (torch.FloatTensor): Pitch values for each spectrogram frame. Only used when the pitch predictor is on. Defaults to None.
energy (torch.FloatTensor): energy values for each spectrogram frame. Only used when the energy predictor is on. Defaults to None.
aux_input (Dict): Auxiliary model inputs for multi-speaker training. Defaults to `{"d_vectors": 0, "speaker_ids": None}`.
Shapes:
- x: :math:`[B, T_max]`
- x_lengths: :math:`[B]`
- y_lengths: :math:`[B]`
- y: :math:`[B, T_max2]`
- dr: :math:`[B, T_max]`
- g: :math:`[B, C]`
- pitch: :math:`[B, 1, T]`
"""
g = self._set_speaker_input(aux_input)
# compute sequence masks
y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).float()
x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.shape[1]), 1).float()
# encoder pass
o_en, x_mask, g, x_emb = self._forward_encoder(x, x_mask, g)
# duration predictor pass
if self.args.detach_duration_predictor:
o_dr_log = self.duration_predictor(o_en.detach(), x_mask)
else:
o_dr_log = self.duration_predictor(o_en, x_mask)
o_dr = torch.clamp(torch.exp(o_dr_log) - 1, 0, self.max_duration)
# generate attn mask from predicted durations
o_attn = self.generate_attn(o_dr.squeeze(1), x_mask)
# aligner
o_alignment_dur = None
alignment_soft = None
alignment_logprob = None
alignment_mas = None
if self.use_aligner:
o_alignment_dur, alignment_soft, alignment_logprob, alignment_mas = self._forward_aligner(
x_emb, y, x_mask, y_mask
)
alignment_soft = alignment_soft.transpose(1, 2)
alignment_mas = alignment_mas.transpose(1, 2)
dr = o_alignment_dur
# pitch predictor pass
o_pitch = None
avg_pitch = None
if self.args.use_pitch:
o_pitch_emb, o_pitch, avg_pitch = self._forward_pitch_predictor(o_en, x_mask, pitch, dr)
o_en = o_en + o_pitch_emb
# energy predictor pass
o_energy = None
avg_energy = None
if self.args.use_energy:
o_energy_emb, o_energy, avg_energy = self._forward_energy_predictor(o_en, x_mask, energy, dr)
o_en = o_en + o_energy_emb
# decoder pass
o_de, attn = self._forward_decoder(
o_en, dr, x_mask, y_lengths, g=None
) # TODO: maybe pass speaker embedding (g) too
outputs = {
"model_outputs": o_de, # [B, T, C]
"durations_log": o_dr_log.squeeze(1), # [B, T]
"durations": o_dr.squeeze(1), # [B, T]
"attn_durations": o_attn, # for visualization [B, T_en, T_de']
"pitch_avg": o_pitch,
"pitch_avg_gt": avg_pitch,
"energy_avg": o_energy,
"energy_avg_gt": avg_energy,
"alignments": attn, # [B, T_de, T_en]
"alignment_soft": alignment_soft,
"alignment_mas": alignment_mas,
"o_alignment_dur": o_alignment_dur,
"alignment_logprob": alignment_logprob,
"x_mask": x_mask,
"y_mask": y_mask,
}
return outputs
[docs]
@torch.no_grad()
def inference(self, x, aux_input={"d_vectors": None, "speaker_ids": None}): # pylint: disable=unused-argument
"""Model's inference pass.
Args:
x (torch.LongTensor): Input character sequence.
aux_input (Dict): Auxiliary model inputs. Defaults to `{"d_vectors": None, "speaker_ids": None}`.
Shapes:
- x: [B, T_max]
- x_lengths: [B]
- g: [B, C]
"""
g = self._set_speaker_input(aux_input)
x_lengths = torch.tensor(x.shape[1:2]).to(x.device)
x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.shape[1]), 1).to(x.dtype).float()
# encoder pass
o_en, x_mask, g, _ = self._forward_encoder(x, x_mask, g)
# duration predictor pass
o_dr_log = self.duration_predictor(o_en.squeeze(), x_mask)
o_dr = self.format_durations(o_dr_log, x_mask).squeeze(1)
y_lengths = o_dr.sum(1)
# pitch predictor pass
o_pitch = None
if self.args.use_pitch:
o_pitch_emb, o_pitch = self._forward_pitch_predictor(o_en, x_mask)
o_en = o_en + o_pitch_emb
# energy predictor pass
o_energy = None
if self.args.use_energy:
o_energy_emb, o_energy = self._forward_energy_predictor(o_en, x_mask)
o_en = o_en + o_energy_emb
# decoder pass
o_de, attn = self._forward_decoder(o_en, o_dr, x_mask, y_lengths, g=None)
outputs = {
"model_outputs": o_de,
"alignments": attn,
"pitch": o_pitch,
"energy": o_energy,
"durations_log": o_dr_log,
}
return outputs
def train_step(self, batch: dict, criterion: nn.Module):
text_input = batch["text_input"]
text_lengths = batch["text_lengths"]
mel_input = batch["mel_input"]
mel_lengths = batch["mel_lengths"]
pitch = batch["pitch"] if self.args.use_pitch else None
energy = batch["energy"] if self.args.use_energy else None
d_vectors = batch["d_vectors"]
speaker_ids = batch["speaker_ids"]
durations = batch["durations"]
aux_input = {"d_vectors": d_vectors, "speaker_ids": speaker_ids}
# forward pass
outputs = self.forward(
text_input,
text_lengths,
mel_lengths,
y=mel_input,
dr=durations,
pitch=pitch,
energy=energy,
aux_input=aux_input,
)
# use aligner's output as the duration target
if self.use_aligner:
durations = outputs["o_alignment_dur"]
# use float32 in AMP
with autocast(enabled=False):
# compute loss
loss_dict = criterion(
decoder_output=outputs["model_outputs"],
decoder_target=mel_input,
decoder_output_lens=mel_lengths,
dur_output=outputs["durations_log"],
dur_target=durations,
pitch_output=outputs["pitch_avg"] if self.use_pitch else None,
pitch_target=outputs["pitch_avg_gt"] if self.use_pitch else None,
energy_output=outputs["energy_avg"] if self.use_energy else None,
energy_target=outputs["energy_avg_gt"] if self.use_energy else None,
input_lens=text_lengths,
alignment_logprob=outputs["alignment_logprob"] if self.use_aligner else None,
alignment_soft=outputs["alignment_soft"],
alignment_hard=outputs["alignment_mas"],
binary_loss_weight=self.binary_loss_weight,
)
# compute duration error
durations_pred = outputs["durations"]
duration_error = torch.abs(durations - durations_pred).sum() / text_lengths.sum()
loss_dict["duration_error"] = duration_error
return outputs, loss_dict
def _create_logs(self, batch, outputs, ap):
"""Create common logger outputs."""
model_outputs = outputs["model_outputs"]
alignments = outputs["alignments"]
mel_input = batch["mel_input"]
pred_spec = model_outputs[0].data.cpu().numpy()
gt_spec = mel_input[0].data.cpu().numpy()
align_img = alignments[0].data.cpu().numpy()
figures = {
"prediction": plot_spectrogram(pred_spec, ap, output_fig=False),
"ground_truth": plot_spectrogram(gt_spec, ap, output_fig=False),
"alignment": plot_alignment(align_img, output_fig=False),
}
# plot pitch figures
if self.args.use_pitch:
pitch_avg = abs(outputs["pitch_avg_gt"][0, 0].data.cpu().numpy())
pitch_avg_hat = abs(outputs["pitch_avg"][0, 0].data.cpu().numpy())
chars = self.tokenizer.decode(batch["text_input"][0].data.cpu().numpy())
pitch_figures = {
"pitch_ground_truth": plot_avg_pitch(pitch_avg, chars, output_fig=False),
"pitch_avg_predicted": plot_avg_pitch(pitch_avg_hat, chars, output_fig=False),
}
figures.update(pitch_figures)
# plot energy figures
if self.args.use_energy:
energy_avg = abs(outputs["energy_avg_gt"][0, 0].data.cpu().numpy())
energy_avg_hat = abs(outputs["energy_avg"][0, 0].data.cpu().numpy())
chars = self.tokenizer.decode(batch["text_input"][0].data.cpu().numpy())
energy_figures = {
"energy_ground_truth": plot_avg_energy(energy_avg, chars, output_fig=False),
"energy_avg_predicted": plot_avg_energy(energy_avg_hat, chars, output_fig=False),
}
figures.update(energy_figures)
# plot the attention mask computed from the predicted durations
if "attn_durations" in outputs:
alignments_hat = outputs["attn_durations"][0].data.cpu().numpy()
figures["alignment_hat"] = plot_alignment(alignments_hat.T, output_fig=False)
# Sample audio
train_audio = ap.inv_melspectrogram(pred_spec.T)
return figures, {"audio": train_audio}
def train_log(
self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int
) -> None: # pylint: disable=no-self-use
figures, audios = self._create_logs(batch, outputs, self.ap)
logger.train_figures(steps, figures)
logger.train_audios(steps, audios, self.ap.sample_rate)
def eval_step(self, batch: dict, criterion: nn.Module):
return self.train_step(batch, criterion)
def eval_log(self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int) -> None:
figures, audios = self._create_logs(batch, outputs, self.ap)
logger.eval_figures(steps, figures)
logger.eval_audios(steps, audios, self.ap.sample_rate)
def load_checkpoint(
self, config, checkpoint_path, eval=False, cache=False
): # pylint: disable=unused-argument, redefined-builtin
state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"), cache=cache)
self.load_state_dict(state["model"])
if eval:
self.eval()
assert not self.training
def get_criterion(self):
from TTS.tts.layers.losses import ForwardTTSLoss # pylint: disable=import-outside-toplevel
return ForwardTTSLoss(self.config)
[docs]
def on_train_step_start(self, trainer):
"""Schedule binary loss weight."""
self.binary_loss_weight = min(trainer.epochs_done / self.config.binary_loss_warmup_epochs, 1.0) * 1.0
[docs]
@staticmethod
def init_from_config(config: "ForwardTTSConfig", samples: Union[List[List], List[Dict]] = None):
"""Initiate model from config
Args:
config (ForwardTTSConfig): Model config.
samples (Union[List[List], List[Dict]]): Training samples to parse speaker ids for training.
Defaults to None.
"""
from TTS.utils.audio import AudioProcessor
ap = AudioProcessor.init_from_config(config)
tokenizer, new_config = TTSTokenizer.init_from_config(config)
speaker_manager = SpeakerManager.init_from_config(config, samples)
return ForwardTTS(new_config, ap, tokenizer, speaker_manager)