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Copy pathvariance_encoder.py
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160 lines (142 loc) · 6.86 KB
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import torch
import torch.nn as nn
from torch.nn import functional as F
from modules.commons.common_layers import (
NormalInitEmbedding as Embedding,
XavierUniformInitLinear as Linear,
AdamWLinear,
)
from modules.fastspeech.tts_modules import FastSpeech2Encoder, DurationPredictor
from utils.hparams import hparams
from utils.phoneme_utils import PAD_INDEX
class FastSpeech2Variance(nn.Module):
def __init__(self, vocab_size):
super().__init__()
self.predict_dur = hparams['predict_dur']
self.linguistic_mode = 'word' if hparams['predict_dur'] else 'phoneme'
self.use_lang_id = hparams['use_lang_id']
self.use_variance_scaling = hparams.get('use_variance_scaling', False)
self.txt_embed = Embedding(vocab_size, hparams['hidden_size'], PAD_INDEX)
if self.use_lang_id:
self.lang_embed = Embedding(hparams['num_lang'] + 1, hparams['hidden_size'], padding_idx=0)
if self.predict_dur:
self.onset_embed = Embedding(2, hparams['hidden_size'])
self.word_dur_embed = AdamWLinear(1, hparams['hidden_size'])
else:
self.ph_dur_embed = AdamWLinear(1, hparams['hidden_size'])
self.encoder = FastSpeech2Encoder(
hidden_size=hparams['hidden_size'], num_layers=hparams['enc_layers'],
ffn_kernel_size=hparams['enc_ffn_kernel_size'], ffn_act=hparams['ffn_act'],
dropout=hparams['dropout'], num_heads=hparams['num_heads'],
use_pos_embed=hparams['use_pos_embed'], rel_pos=hparams.get('rel_pos', False),
use_rope=hparams.get('use_rope', False), rope_interleaved=hparams.get('rope_interleaved', True),
rope_theta=hparams.get('rope_theta', 10000)
)
dur_hparams = hparams['dur_prediction_args']
if self.predict_dur:
self.midi_embed = Embedding(128, hparams['hidden_size'])
self.dur_predictor = DurationPredictor(
in_dims=hparams['hidden_size'],
n_chans=dur_hparams['hidden_size'],
n_layers=dur_hparams['num_layers'],
dropout_rate=dur_hparams['dropout'],
kernel_size=dur_hparams['kernel_size'],
offset=dur_hparams['log_offset'],
dur_loss_type=dur_hparams['loss_type'],
arch=dur_hparams['arch']
)
def forward(
self, txt_tokens, midi, ph2word,
ph_dur=None, word_dur=None,
spk_embed=None, languages=None,
infer=True
):
"""
:param txt_tokens: (train, infer) [B, T_ph]
:param midi: (train, infer) [B, T_ph]
:param ph2word: (train, infer) [B, T_ph]
:param ph_dur: (train, [infer]) [B, T_ph]
:param word_dur: (infer) [B, T_w]
:param spk_embed: (train) [B, T_ph, H]
:param languages (train, infer) [B, T_ph]
:param infer: whether inference
:return: encoder_out, ph_dur_pred
"""
txt_embed = self.txt_embed(txt_tokens)
if self.linguistic_mode == 'word':
b = txt_tokens.shape[0]
onset = torch.diff(ph2word, dim=1, prepend=ph2word.new_zeros(b, 1)) > 0
onset_embed = self.onset_embed(onset.long()) # [B, T_ph, H]
if word_dur is None or not infer:
word_dur = ph_dur.new_zeros(b, ph2word.max() + 1).scatter_add(
1, ph2word, ph_dur
)[:, 1:] # [B, T_ph] => [B, T_w]
word_dur = torch.gather(F.pad(word_dur, [1, 0], value=0), 1, ph2word) # [B, T_w] => [B, T_ph]
word_dur_embed = self.word_dur_embed(word_dur.float()[:, :, None])
extra_embed = onset_embed + word_dur_embed
elif self.use_variance_scaling:
extra_embed = self.ph_dur_embed(torch.log(1 + ph_dur.float())[:, :, None])
else:
extra_embed = self.ph_dur_embed(ph_dur.float()[:, :, None])
if self.use_lang_id:
lang_embed = self.lang_embed(languages)
extra_embed += lang_embed
encoder_out = self.encoder(txt_embed, extra_embed, txt_tokens == 0)
if self.predict_dur:
midi_embed = self.midi_embed(midi) # => [B, T_ph, H]
dur_cond = encoder_out + midi_embed
if spk_embed is not None:
dur_cond += spk_embed
ph_dur_pred = self.dur_predictor(dur_cond, x_masks=txt_tokens == PAD_INDEX, infer=infer)
return encoder_out, ph_dur_pred
else:
return encoder_out, None
class MelodyEncoder(nn.Module):
def __init__(self, enc_hparams: dict):
super().__init__()
def get_hparam(key):
return enc_hparams.get(key, hparams.get(key))
# MIDI inputs
hidden_size = get_hparam('hidden_size')
self.use_variance_scaling = hparams.get('use_variance_scaling', False)
self.note_midi_embed = AdamWLinear(1, hidden_size)
self.note_dur_embed = AdamWLinear(1, hidden_size)
# ornament inputs
self.use_glide_embed = hparams['use_glide_embed']
self.glide_embed_scale = hparams['glide_embed_scale']
if self.use_glide_embed:
# 0: none, 1: up, 2: down
self.note_glide_embed = Embedding(len(hparams['glide_types']) + 1, hidden_size, padding_idx=0)
self.encoder = FastSpeech2Encoder(
hidden_size=hidden_size, num_layers=get_hparam('enc_layers'),
ffn_kernel_size=get_hparam('enc_ffn_kernel_size'), ffn_act=get_hparam('ffn_act'),
dropout=get_hparam('dropout'), num_heads=get_hparam('num_heads'),
use_pos_embed=get_hparam('use_pos_embed'), rel_pos=get_hparam('rel_pos'),
use_rope=get_hparam('use_rope'), rope_interleaved=hparams.get('rope_interleaved', True),
rope_theta=hparams.get('rope_theta', 10000)
)
self.out_proj = Linear(hidden_size, hparams['hidden_size'])
def forward(self, note_midi, note_rest, note_dur, glide=None):
"""
:param note_midi: float32 [B, T_n], -1: padding
:param note_rest: bool [B, T_n]
:param note_dur: int64 [B, T_n]
:param glide: int64 [B, T_n]
:return: [B, T_n, H]
"""
if self.use_variance_scaling:
midi_embed = self.note_midi_embed(note_midi[:, :, None] / 128)
dur_embed = self.note_dur_embed(torch.log(1 + note_dur.float())[:, :, None])
else:
midi_embed = self.note_midi_embed(note_midi[:, :, None])
dur_embed = self.note_dur_embed(note_dur.float()[:, :, None])
midi_embed *= ~note_rest[:, :, None]
ornament_embed = 0
if self.use_glide_embed:
ornament_embed += self.note_glide_embed(glide) * self.glide_embed_scale
encoder_out = self.encoder(
midi_embed, dur_embed + ornament_embed,
padding_mask=note_midi < 0
)
encoder_out = self.out_proj(encoder_out)
return encoder_out