mirror of https://github.com/coqui-ai/TTS.git
378 lines
15 KiB
Python
378 lines
15 KiB
Python
from dataclasses import dataclass, field
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import torch
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import torch.nn.functional as F
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from coqpit import Coqpit
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from torch import nn
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from TTS.tts.layers.feed_forward.decoder import Decoder
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from TTS.tts.layers.feed_forward.encoder import Encoder
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from TTS.tts.layers.generic.pos_encoding import PositionalEncoding
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from TTS.tts.layers.glow_tts.duration_predictor import DurationPredictor
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from TTS.tts.layers.glow_tts.monotonic_align import generate_path
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from TTS.tts.models.base_tts import BaseTTS
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from TTS.tts.utils.data import sequence_mask
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from TTS.tts.utils.measures import alignment_diagonal_score
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from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
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from TTS.utils.audio import AudioProcessor
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@dataclass
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class FastPitchArgs(Coqpit):
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num_chars: int = None
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out_channels: int = 80
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hidden_channels: int = 256
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num_speakers: int = 0
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duration_predictor_hidden_channels: int = 256
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duration_predictor_dropout: float = 0.1
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duration_predictor_kernel_size: int = 3
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duration_predictor_dropout_p: float = 0.1
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pitch_predictor_hidden_channels: int = 256
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pitch_predictor_dropout: float = 0.1
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pitch_predictor_kernel_size: int = 3
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pitch_predictor_dropout_p: float = 0.1
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pitch_embedding_kernel_size: int = 3
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positional_encoding: bool = True
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length_scale: int = 1
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encoder_type: str = "fftransformer"
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encoder_params: dict = field(
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default_factory=lambda: {"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1}
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)
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decoder_type: str = "fftransformer"
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decoder_params: dict = field(
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default_factory=lambda: {"hidden_channels_ffn": 1024, "num_heads": 1, "num_layers": 6, "dropout_p": 0.1}
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)
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use_d_vector: bool = False
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d_vector_dim: int = 0
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class FastPitch(BaseTTS):
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"""FastPitch model. Very similart to SpeedySpeech model but with pitch prediction.
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Paper abstract:
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We present FastPitch, a fully-parallel text-to-speech model based on FastSpeech, conditioned on fundamental
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frequency contours. The model predicts pitch contours during inference. By altering these predictions,
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the generated speech can be more expressive, better match the semantic of the utterance, and in the end
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more engaging to the listener. Uniformly increasing or decreasing pitch with FastPitch generates speech
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that resembles the voluntary modulation of voice. Conditioning on frequency contours improves the overall
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quality of synthesized speech, making it comparable to state-of-the-art. It does not introduce an overhead,
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and FastPitch retains the favorable, fully-parallel Transformer architecture, with over 900x real-time
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factor for mel-spectrogram synthesis of a typical utterance."
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Notes:
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TODO
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Args:
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config (Coqpit): Model coqpit class.
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Examples:
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>>> from TTS.tts.models.fast_pitch import FastPitch, FastPitchArgs
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>>> config = FastPitchArgs()
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>>> model = FastPitch(config)
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"""
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# pylint: disable=dangerous-default-value
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def __init__(self, config: Coqpit):
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super().__init__()
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_, self.config, num_chars = self.get_characters(config)
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config.model_args.num_chars = num_chars
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self.length_scale = (
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float(config.model_args.length_scale)
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if isinstance(config.model_args.length_scale, int)
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else config.model_args.length_scale
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)
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self.emb = nn.Embedding(config.model_args.num_chars, config.model_args.hidden_channels)
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self.encoder = Encoder(
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config.model_args.hidden_channels,
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config.model_args.hidden_channels,
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config.model_args.encoder_type,
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config.model_args.encoder_params,
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config.model_args.d_vector_dim,
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)
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if config.model_args.positional_encoding:
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self.pos_encoder = PositionalEncoding(config.model_args.hidden_channels)
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self.decoder = Decoder(
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config.model_args.out_channels,
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config.model_args.hidden_channels,
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config.model_args.decoder_type,
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config.model_args.decoder_params,
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)
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self.duration_predictor = DurationPredictor(
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config.model_args.hidden_channels + config.model_args.d_vector_dim,
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config.model_args.duration_predictor_hidden_channels,
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config.model_args.duration_predictor_kernel_size,
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config.model_args.duration_predictor_dropout_p,
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)
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self.pitch_predictor = DurationPredictor(
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config.model_args.hidden_channels + config.model_args.d_vector_dim,
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config.model_args.pitch_predictor_hidden_channels,
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config.model_args.pitch_predictor_kernel_size,
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config.model_args.pitch_predictor_dropout_p,
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)
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self.pitch_emb = nn.Conv1d(
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1,
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config.model_args.hidden_channels,
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kernel_size=config.model_args.pitch_embedding_kernel_size,
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padding=int((config.model_args.pitch_embedding_kernel_size - 1) / 2),
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)
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self.register_buffer("pitch_mean", torch.zeros(1))
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self.register_buffer("pitch_std", torch.zeros(1))
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if config.model_args.num_speakers > 1 and not config.model_args.use_d_vector:
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# speaker embedding layer
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self.emb_g = nn.Embedding(config.model_args.num_speakers, config.model_args.d_vector_dim)
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nn.init.uniform_(self.emb_g.weight, -0.1, 0.1)
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if config.model_args.d_vector_dim > 0 and config.model_args.d_vector_dim != config.model_args.hidden_channels:
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self.proj_g = nn.Conv1d(config.model_args.d_vector_dim, config.model_args.hidden_channels, 1)
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@staticmethod
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def expand_encoder_outputs(en, dr, x_mask, y_mask):
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"""Generate attention alignment map from durations and
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expand encoder outputs
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Example:
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encoder output: [a,b,c,d]
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durations: [1, 3, 2, 1]
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expanded: [a, b, b, b, c, c, d]
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attention map: [[0, 0, 0, 0, 0, 0, 1],
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[0, 0, 0, 0, 1, 1, 0],
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[0, 1, 1, 1, 0, 0, 0],
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[1, 0, 0, 0, 0, 0, 0]]
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"""
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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(attn.squeeze(1).transpose(1, 2), en.transpose(1, 2)).transpose(1, 2)
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return o_en_ex, attn
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def format_durations(self, o_dr_log, x_mask):
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o_dr = (torch.exp(o_dr_log) - 1) * x_mask * self.length_scale
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o_dr[o_dr < 1] = 1.0
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o_dr = torch.round(o_dr)
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return o_dr
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@staticmethod
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def _concat_speaker_embedding(o_en, g):
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g_exp = g.expand(-1, -1, o_en.size(-1)) # [B, C, T_en]
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o_en = torch.cat([o_en, g_exp], 1)
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return o_en
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def _sum_speaker_embedding(self, x, g):
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# project g to decoder dim.
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if hasattr(self, "proj_g"):
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g = self.proj_g(g)
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return x + g
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def _forward_encoder(self, x, x_lengths, g=None):
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if hasattr(self, "emb_g"):
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g = nn.functional.normalize(self.emb_g(g)) # [B, C, 1]
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if g is not None:
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g = g.unsqueeze(-1)
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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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x_emb = torch.transpose(x_emb, 1, -1)
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# compute sequence masks
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x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.shape[1]), 1).to(x.dtype)
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# encoder pass
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o_en = self.encoder(x_emb, x_mask)
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# speaker conditioning for duration predictor
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if g is not None:
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o_en_dp = self._concat_speaker_embedding(o_en, g)
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else:
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o_en_dp = o_en
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return o_en, o_en_dp, x_mask, g
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def _forward_decoder(self, o_en, o_en_dp, dr, x_mask, y_lengths, g):
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y_mask = torch.unsqueeze(sequence_mask(y_lengths, None), 1).to(o_en_dp.dtype)
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# expand o_en with durations
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o_en_ex, attn = self.expand_encoder_outputs(o_en, dr, x_mask, y_mask)
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# positional encoding
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if hasattr(self, "pos_encoder"):
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o_en_ex = self.pos_encoder(o_en_ex, y_mask)
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# speaker embedding
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if g is not None:
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o_en_ex = self._sum_speaker_embedding(o_en_ex, g)
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# decoder pass
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o_de = self.decoder(o_en_ex, y_mask, g=g)
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return o_de, attn.transpose(1, 2)
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def _forward_pitch_predictor(self, o_en, x_mask, pitch=None, dr=None):
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o_pitch = self.pitch_predictor(o_en, x_mask)
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if pitch is not None:
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avg_pitch = average_pitch(pitch, dr)
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o_pitch_emb = self.pitch_emb(avg_pitch)
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return o_pitch_emb, o_pitch, avg_pitch
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o_pitch_emb = self.pitch_emb(o_pitch)
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return o_pitch_emb, o_pitch
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def forward(
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self, x, x_lengths, y_lengths, dr, pitch, aux_input={"d_vectors": None, "speaker_ids": None}
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): # pylint: disable=unused-argument
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"""
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Shapes:
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x: :math:`[B, T_max]`
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x_lengths: :math:`[B]`
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y_lengths: :math:`[B]`
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dr: :math:`[B, T_max]`
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g: :math:`[B, C]`
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pitch: :math:`[B, 1, T]`
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"""
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g = aux_input["d_vectors"] if "d_vectors" in aux_input else None
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o_en, o_en_dp, x_mask, g = self._forward_encoder(x, x_lengths, g)
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o_dr_log = self.duration_predictor(o_en_dp.detach(), x_mask)
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o_pitch_emb, o_pitch, avg_pitch = self._forward_pitch_predictor(o_en_dp, x_mask, pitch, dr)
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o_en = o_en + o_pitch_emb
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o_de, attn = self._forward_decoder(o_en, o_en_dp, dr, x_mask, y_lengths, g=g)
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outputs = {
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"model_outputs": o_de.transpose(1, 2),
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"durations_log": o_dr_log.squeeze(1),
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"pitch": o_pitch,
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"pitch_gt": avg_pitch,
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"alignments": attn,
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}
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return outputs
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def inference(self, x, aux_input={"d_vectors": None, "speaker_ids": None}): # pylint: disable=unused-argument
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"""
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Shapes:
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x: [B, T_max]
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x_lengths: [B]
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g: [B, C]
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"""
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g = aux_input["d_vectors"] if "d_vectors" in aux_input else None
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x_lengths = torch.tensor(x.shape[1:2]).to(x.device)
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# input sequence should be greated than the max convolution size
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inference_padding = 5
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if x.shape[1] < 13:
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inference_padding += 13 - x.shape[1]
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# pad input to prevent dropping the last word
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x = torch.nn.functional.pad(x, pad=(0, inference_padding), mode="constant", value=0)
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o_en, o_en_dp, x_mask, g = self._forward_encoder(x, x_lengths, g)
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# duration predictor pass
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o_dr_log = self.duration_predictor(o_en_dp.detach(), x_mask)
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o_dr = self.format_durations(o_dr_log, x_mask).squeeze(1)
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# pitch predictor pass
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o_pitch_emb, o_pitch = self._forward_pitch_predictor(o_en_dp, x_mask)
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# if pitch_transform is not None:
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# if self.pitch_std[0] == 0.0:
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# # XXX LJSpeech-1.1 defaults
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# mean, std = 218.14, 67.24
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# else:
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# mean, std = self.pitch_mean[0], self.pitch_std[0]
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# pitch_pred = pitch_transform(pitch_pred, enc_mask.sum(dim=(1,2)), mean, std)
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# if pitch_tgt is None:
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# pitch_emb = self.pitch_emb(pitch_pred.unsqueeze(1)).transpose(1, 2)
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# else:
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# pitch_emb = self.pitch_emb(pitch_tgt.unsqueeze(1)).transpose(1, 2)
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o_en = o_en + o_pitch_emb
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y_lengths = o_dr.sum(1)
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o_de, attn = self._forward_decoder(o_en, o_en_dp, o_dr, x_mask, y_lengths, g=g)
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outputs = {"model_outputs": o_de.transpose(1, 2), "alignments": attn, "pitch": o_pitch, "durations_log": None}
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return outputs
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def train_step(self, batch: dict, criterion: nn.Module):
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text_input = batch["text_input"]
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text_lengths = batch["text_lengths"]
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mel_input = batch["mel_input"]
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mel_lengths = batch["mel_lengths"]
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pitch = batch["pitch"]
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d_vectors = batch["d_vectors"]
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speaker_ids = batch["speaker_ids"]
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durations = batch["durations"]
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aux_input = {"d_vectors": d_vectors, "speaker_ids": speaker_ids}
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outputs = self.forward(text_input, text_lengths, mel_lengths, durations, pitch, aux_input)
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# compute loss
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loss_dict = criterion(
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outputs["model_outputs"],
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mel_input,
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mel_lengths,
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outputs["durations_log"],
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torch.log(1 + durations),
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outputs["pitch"],
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outputs["pitch_gt"],
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text_lengths,
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)
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# compute alignment error (the lower the better )
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align_error = 1 - alignment_diagonal_score(outputs["alignments"], binary=True)
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loss_dict["align_error"] = align_error
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return outputs, loss_dict
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def train_log(self, ap: AudioProcessor, batch: dict, outputs: dict): # pylint: disable=no-self-use
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model_outputs = outputs["model_outputs"]
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alignments = outputs["alignments"]
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mel_input = batch["mel_input"]
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pred_spec = model_outputs[0].data.cpu().numpy()
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gt_spec = mel_input[0].data.cpu().numpy()
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align_img = alignments[0].data.cpu().numpy()
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figures = {
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"prediction": plot_spectrogram(pred_spec, ap, output_fig=False),
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"ground_truth": plot_spectrogram(gt_spec, ap, output_fig=False),
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"alignment": plot_alignment(align_img, output_fig=False),
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}
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# Sample audio
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train_audio = ap.inv_melspectrogram(pred_spec.T)
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return figures, {"audio": train_audio}
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def eval_step(self, batch: dict, criterion: nn.Module):
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return self.train_step(batch, criterion)
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def eval_log(self, ap: AudioProcessor, batch: dict, outputs: dict):
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return self.train_log(ap, batch, outputs)
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def load_checkpoint(
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self, config, checkpoint_path, eval=False
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): # pylint: disable=unused-argument, redefined-builtin
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state = torch.load(checkpoint_path, map_location=torch.device("cpu"))
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self.load_state_dict(state["model"])
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if eval:
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self.eval()
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assert not self.training
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def get_criterion(self):
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from TTS.tts.layers.losses import FastPitchLoss # pylint: disable=import-outside-toplevel
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return FastPitchLoss(self.config)
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def average_pitch(pitch, durs):
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durs_cums_ends = torch.cumsum(durs, dim=1).long()
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durs_cums_starts = torch.nn.functional.pad(durs_cums_ends[:, :-1], (1, 0))
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pitch_nonzero_cums = torch.nn.functional.pad(torch.cumsum(pitch != 0.0, dim=2), (1, 0))
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pitch_cums = torch.nn.functional.pad(torch.cumsum(pitch, dim=2), (1, 0))
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bs, l = durs_cums_ends.size()
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n_formants = pitch.size(1)
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dcs = durs_cums_starts[:, None, :].expand(bs, n_formants, l)
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dce = durs_cums_ends[:, None, :].expand(bs, n_formants, l)
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pitch_sums = (torch.gather(pitch_cums, 2, dce) - torch.gather(pitch_cums, 2, dcs)).float()
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pitch_nelems = (torch.gather(pitch_nonzero_cums, 2, dce) - torch.gather(pitch_nonzero_cums, 2, dcs)).float()
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pitch_avg = torch.where(pitch_nelems == 0.0, pitch_nelems, pitch_sums / pitch_nelems)
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return pitch_avg
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