from math import sqrt
import torch
from munch import munchify
from torch.autograd import Variable
from torch import nn
from torch.nn import functional as F, Flatten
from models.arch_util import ConvGnSilu
from models.diffusion.unet_diffusion import UNetModel, AttentionPool2d
from models.tacotron2.layers import ConvNorm, LinearNorm
from models.tacotron2.hparams import create_hparams
from models.tacotron2.tacotron2 import Prenet, Attention, Encoder
from trainer.networks import register_model
from models.tacotron2.taco_utils import get_mask_from_lengths
from utils.util import opt_get, checkpoint
class WavDecoder(nn.Module):
def __init__(self, dec_channels, K_ms=40, sample_rate=8000, dropout_probability=.1):
super().__init__()
self.dec_channels = dec_channels
self.K = int(sample_rate * (K_ms/1000))
self.clarifier = UNetModel(image_size=self.K,
in_channels=1,
model_channels=dec_channels // 4, # This is a requirement to enable to load the embedding produced by the decoder into the unet model.
out_channels=2, # 2 channels: eps_pred and variance_pred
num_res_blocks=2,
attention_resolutions=(8,),
dims=1,
dropout=.1,
channel_mult=(1,2,4,8),
use_raw_y_as_embedding=True)
assert self.K % 64 == 0 # Otherwise the UNetModel breaks.
self.pre_rnn = nn.Sequential(ConvGnSilu(1,32,kernel_size=5,convnd=nn.Conv1d),
ConvGnSilu(32,64,kernel_size=5,stride=4,convnd=nn.Conv1d),
ConvGnSilu(64,128,kernel_size=5,stride=4,convnd=nn.Conv1d),
ConvGnSilu(128,256,kernel_size=5,stride=4,convnd=nn.Conv1d),
ConvGnSilu(256,dec_channels,kernel_size=1,convnd=nn.Conv1d),
AttentionPool2d(self.K//64,dec_channels,dec_channels//4))
self.attention_rnn = nn.LSTMCell(dec_channels*2, dec_channels)
self.attention_layer = Attention(dec_channels, dec_channels, dec_channels)
self.decoder_rnn = nn.LSTMCell(dec_channels*2, dec_channels, 1)
self.linear_projection = LinearNorm(dec_channels*2, self.dec_channels)
self.gate_layer = LinearNorm(self.dec_channels*2, 1, bias=True, w_init_gain='sigmoid')
self.dropout_probability = dropout_probability
def chunk_wav(self, wav):
wavs = list(torch.split(wav, self.K, dim=-1))
# Pad the last chunk as needed.
padding_needed = self.K - wavs[-1].shape[-1]
if padding_needed > 0:
wavs[-1] = F.pad(wavs[-1], (0,padding_needed))
wavs = torch.stack(wavs, dim=1) # wavs.shape = (b,s,K) where s=decoder sequence length
return wavs, padding_needed
def prepare_decoder_inputs(self, inp):
# inp.shape = (b,s,K) chunked waveform.
b,s,K = inp.shape
first_frame = torch.zeros(b,1,K).to(inp.device)
x = torch.cat([first_frame, inp[:,:-1]], dim=1) # It is now aligned for teacher forcing.
return x
def initialize_decoder_states(self, memory, mask):
""" Initializes attention rnn states, decoder rnn states, attention
weights, attention cumulative weights, attention context, stores memory
and stores processed memory
PARAMS
------
memory: Encoder outputs
mask: Mask for padded data if training, expects None for inference
"""
B = memory.size(0)
MAX_TIME = memory.size(1)
self.attention_hidden = Variable(memory.data.new(B, self.dec_channels).zero_())
self.attention_cell = Variable(memory.data.new(B, self.dec_channels).zero_())
self.decoder_hidden = Variable(memory.data.new(B, self.dec_channels).zero_())
self.decoder_cell = Variable(memory.data.new(B, self.dec_channels).zero_())
self.attention_weights = Variable(memory.data.new(B, MAX_TIME).zero_())
self.attention_weights_cum = Variable(memory.data.new(B, MAX_TIME).zero_())
self.attention_context = Variable(memory.data.new(B, self.dec_channels).zero_())
self.memory = memory
self.processed_memory = checkpoint(self.attention_layer.memory_layer, memory)
self.mask = mask
def teardown_states(self):
self.attention_hidden = None
self.attention_cell = None
self.decoder_hidden = None
self.decoder_cell = None
self.attention_weights = None
self.attention_weights_cum = None
self.attention_context = None
self.memory = None
self.processed_memory = None
def produce_context(self, decoder_input):
""" Produces a context and a stop token prediction using the built-in RNN.
PARAMS
------
decoder_input: prior diffusion step that has been resolved.
RETURNS
-------
mel_output:
gate_output: gate output energies
attention_weights:
"""
cell_input = torch.cat((decoder_input, self.attention_context), -1)
self.attention_hidden, self.attention_cell = self.attention_rnn(cell_input, (self.attention_hidden, self.attention_cell))
self.attention_hidden = F.dropout(self.attention_hidden, self.dropout_probability, self.training)
attention_weights_cat = torch.cat((self.attention_weights.unsqueeze(1), self.attention_weights_cum.unsqueeze(1)), dim=1)
self.attention_context, self.attention_weights = checkpoint(self.attention_layer, self.attention_hidden, self.memory,
self.processed_memory, attention_weights_cat, self.mask)
self.attention_weights_cum += self.attention_weights
decoder_input = torch.cat((self.attention_hidden, self.attention_context), -1)
self.decoder_hidden, self.decoder_cell = self.decoder_rnn(decoder_input, (self.decoder_hidden, self.decoder_cell))
self.decoder_hidden = F.dropout(self.decoder_hidden, self.dropout_probability, self.training)
decoder_hidden_attention_context = torch.cat((self.decoder_hidden, self.attention_context), dim=1)
decoder_output = checkpoint(self.linear_projection, decoder_hidden_attention_context)
gate_prediction = self.gate_layer(decoder_hidden_attention_context)
return decoder_output, gate_prediction, self.attention_weights
def recombine(self, diffusion_eps, gate_outputs, alignments, padding_added):
# (T_out, B) -> (B, T_out)
alignments = torch.stack(alignments, dim=1).repeat(1, self.K, 1)
# (T_out, B) -> (B, T_out)
gate_outputs = torch.stack(gate_outputs, dim=1).repeat(1, self.K)
b,s,_,K = diffusion_eps.shape
# (B, S, 2, K) -> (B, 2, S*K)
diffusion_eps = diffusion_eps.permute(0,2,1,3).reshape(b, 2, s*K)
return diffusion_eps[:,:,:-padding_added], gate_outputs[:,:-padding_added], alignments[:,:-padding_added]
def forward(self, wav_noised, wav_real, timesteps, text_enc, memory_lengths):
'''
Performs a training forward pass with the given data.
:param wav_noised: (b,n) diffused waveform tensor on the interval [-1,1]
:param wav_real: (b,n) actual waveform tensor
:param text_enc: (b,e) embedding post-encoder with e=self.dec_channels
'''
# Start by splitting up the provided waveforms into discrete segments.
wav_noised, padding_added = self.chunk_wav(wav_noised)
wav_real, _ = self.chunk_wav(wav_real)
wav_real = self.prepare_decoder_inputs(wav_real)
b,s,K = wav_real.shape
wav_real = checkpoint(self.pre_rnn, wav_real.reshape(b*s,1,K)).reshape(b,s,self.dec_channels)
self.initialize_decoder_states(text_enc, mask=~get_mask_from_lengths(memory_lengths))
decoder_contexts, gate_outputs, alignments = [], [], []
while len(decoder_contexts) < wav_real.size(1):
decoder_input = wav_real[:, len(decoder_contexts)]
dec_context, gate_output, attention_weights = self.produce_context(decoder_input)
decoder_contexts += [dec_context.squeeze(1)]
gate_outputs += [gate_output.squeeze(1)]
alignments += [attention_weights]
self.teardown_states()
# diffusion_inputs and wavs needs to have the sequence and batch dimensions combined, and needs a channel dimension
diffusion_emb = torch.stack(decoder_contexts, dim=1)
b,s,c = diffusion_emb.shape
diffusion_emb = diffusion_emb.reshape(b*s,c)
wav_noised = wav_noised.reshape(b*s,1,self.K)
diffusion_eps = self.clarifier(wav_noised, timesteps.repeat(s), diffusion_emb).reshape(b,s,2,self.K)
# Recombine diffusion outputs across the sequence into a single prediction.
diffusion_eps, gate_outputs, alignments = self.recombine(diffusion_eps, gate_outputs, alignments, padding_added)
return diffusion_eps, gate_outputs, alignments
class WaveTacotron2(nn.Module):
def __init__(self, hparams):
super().__init__()
self.mask_padding = hparams.mask_padding
self.fp16_run = hparams.fp16_run
self.n_mel_channels = hparams.n_mel_channels
self.n_frames_per_step = hparams.n_frames_per_step
self.embedding = nn.Embedding(
hparams.n_symbols, hparams.symbols_embedding_dim)
std = sqrt(2.0 / (hparams.n_symbols + hparams.symbols_embedding_dim))
val = sqrt(3.0) * std # uniform bounds for std
self.embedding.weight.data.uniform_(-val, val)
self.encoder = Encoder(hparams)
self.decoder = WavDecoder(hparams.encoder_embedding_dim)
def parse_output(self, outputs, output_lengths=None):
if self.mask_padding and output_lengths is not None:
mask_fill = outputs[0].shape[-1]
mask = ~get_mask_from_lengths(output_lengths, mask_fill)
mask = mask.unsqueeze(1).repeat(1,2,1)
outputs[0].data.masked_fill_(mask, 0.0)
outputs[0] = outputs[0].unsqueeze(1) # Re-add channel dimension.
outputs[1].data.masked_fill_(mask[:,0], 1e3) # gate energies
return outputs
def forward(self, wavs_diffused, wavs_corrected, timesteps, text_inputs, text_lengths, output_lengths):
# Squeeze the channel dimension out of the input wavs - we only handle single-channel audio here.
wavs_diffused = wavs_diffused.squeeze(dim=1)
wavs_corrected = wavs_corrected.squeeze(dim=1)
text_lengths, output_lengths = text_lengths.data, output_lengths.data
embedded_inputs = self.embedding(text_inputs).transpose(1, 2)
encoder_outputs = checkpoint(self.encoder, embedded_inputs, text_lengths)
eps_pred, gate_outputs, alignments = self.decoder(
wavs_diffused, wavs_corrected, timesteps, encoder_outputs, memory_lengths=text_lengths)
return self.parse_output([eps_pred, gate_outputs, alignments], output_lengths)
@register_model
def register_diffusion_wavetron(opt_net, opt):
hparams = create_hparams()
hparams.update(opt_net)
hparams = munchify(hparams)
return WaveTacotron2(hparams)
if __name__ == '__main__':
tron = register_diffusion_wavetron({}, {})
out = tron(wavs_diffused=torch.randn(2, 1, 22000),
wavs_corrected=torch.randn(2, 1, 22000),
timesteps=torch.LongTensor([555, 543]),
text_inputs=torch.randint(high=24, size=(2,12)),
text_lengths=torch.tensor([12, 12]),
output_lengths=torch.tensor([21995]))
print([o.shape for o in out])