DeepLearningExamples/CUDA-Optimized/FastSpeech/fastspeech/model/fastspeech.py

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from collections import OrderedDict

import numpy as np
import torch
from torch import nn as nn

from fastspeech.model.module import FFTBlocks, LengthRegulator
from fastspeech.utils.pytorch import to_device_async
from fastspeech.utils.nvtx import Nvtx
from torch.nn import functional as F
from fastspeech.utils.logging import tprint
from fastspeech.text_norm.symbols import symbols

class Fastspeech(nn.Module):
    """ FastSpeech """

    def __init__(self, 
                 max_seq_len, 
                 d_model,
                 phoneme_side_n_layer, 
                 phoneme_side_head, 
                 phoneme_side_conv1d_filter_size,
                 phoneme_side_output_size, 
                 mel_side_n_layer, 
                 mel_side_head, 
                 mel_side_conv1d_filter_size,
                 mel_side_output_size,
                 fft_conv1d_kernel, 
                 fft_conv1d_padding,
                 duration_predictor_filter_size, 
                 duration_predictor_kernel_size, 
                 dropout,
                 n_mels,
                 fused_layernorm=False):
        super(Fastspeech, self).__init__()

        self.max_seq_len = max_seq_len
        self.d_model = d_model
        self.phoneme_side_n_layer = phoneme_side_n_layer
        self.phoneme_side_head = phoneme_side_head
        self.phoneme_side_conv1d_filter_size = phoneme_side_conv1d_filter_size
        self.phoneme_side_output_size = phoneme_side_output_size
        self.mel_side_n_layer = mel_side_n_layer
        self.mel_side_head = mel_side_head
        self.mel_side_conv1d_filter_size = mel_side_conv1d_filter_size
        self.mel_side_output_size = mel_side_output_size
        self.fft_conv1d_kernel = fft_conv1d_kernel
        self.fft_conv1d_padding = fft_conv1d_padding
        self.duration_predictor_filter_size = duration_predictor_filter_size
        self.duration_predictor_kernel_size = duration_predictor_kernel_size
        self.dropout = dropout
        self.n_mels = n_mels
        self.fused_layernorm = fused_layernorm
        self.n_phns = len(symbols)+1

        self.word_emb = nn.Embedding(
            self.n_phns, 
            d_model, 
            padding_idx=0)

        self.phoneme_side = FFTBlocks(
            max_seq_len=max_seq_len,
            n_layers=phoneme_side_n_layer,
            n_head=phoneme_side_head,
            d_k=64,
            d_v=64,
            d_model=d_model,
            d_inner=phoneme_side_conv1d_filter_size,
            fft_conv1d_kernel=fft_conv1d_kernel,
            fft_conv1d_padding=fft_conv1d_padding,
            dropout=dropout,
            name="phoneme_side",
            fused_layernorm=fused_layernorm
        )

        self.length_regulator = LengthRegulator(
            input_size=phoneme_side_output_size,
            duration_predictor_filter_size=duration_predictor_filter_size,
            duration_predictor_kernel_size=duration_predictor_kernel_size,
            dropout=dropout,
            fused_layernorm=fused_layernorm
        )

        self.mel_side = FFTBlocks(
            max_seq_len=max_seq_len,
            n_layers=mel_side_n_layer,
            n_head=mel_side_head,
            d_k=64,
            d_v=64,
            d_model=d_model,
            d_inner=mel_side_conv1d_filter_size,
            fft_conv1d_kernel=fft_conv1d_kernel,
            fft_conv1d_padding=fft_conv1d_padding,
            dropout=dropout,
            name="mel_side",
            fused_layernorm=fused_layernorm            
        )

        self.mel_linear = nn.Linear(mel_side_output_size, n_mels, bias=True)

    def forward(self, seq, pos, duration_target=None, alpha=1.0, seq_output_len=None, use_fp16=False, acts=None):

        # Phoneme Embedding
        output = self.word_emb(seq)

        if acts is not None:
            acts["act.emb"] = output

        if use_fp16:
            output = output.half()

        # Phoneme Side FFT Blocks
        output, output_mask = self.phoneme_side(output, pos, acts=acts)

        if acts is not None:
            acts["act.phoneme_side.seq"] = output

        # Length Regulator
        output, pos, duration = self.length_regulator(
            output,
            output_mask,
            target=duration_target,
            alpha=alpha)

        if seq_output_len:
            output = F.pad(output, pad=(0, 0, 0, seq_output_len - output.size(1)))
            pos = F.pad(pos, pad=(0, seq_output_len - pos.size(1)))

        # length of output mel shouldn't exceed max_seq_len
        output = output[:, :self.max_seq_len]
        pos = pos[:, :self.max_seq_len]

        if acts is not None:
            acts["act.length_regulator.seq"] = output
            acts["act.length_regulator.dur"] = torch.round(duration)

        if self.training or output.bool().any():      
            # Mel Side FFT Blocks
            output, output_mask = self.mel_side(output, pos, acts=acts)

            if acts is not None:
                acts["act.mel_side.seq"] = output

            # Linear Layer
            output = self.mel_linear(output)

            if acts is not None:
                acts["out.seq_mask"] = output_mask
                acts["out.seq"] = output
        else:
            # seq length could be zero, in case duration predictor outputs all zeros.
            # In this case, skip feed-forwarding.
            tprint("Duration Predictor outputs all zeros. Output will be zero length.")
            output_shape = (output.size(0), 0, output_mask.size(2))
            output = torch.zeros(size=(output_shape))
            output_mask = torch.ones(size=(output_shape))

        if torch.cuda.device_count() > 1:
            # In a multi-gpu setting, all output mels from devices must have the same length.
            # otherwise, an error occurs in process of gathering output.
            if not seq_output_len:
                seq_output_len = self.max_seq_len
            padding = (0, 0, 0, seq_output_len - output.size(1))

            output = F.pad(output, padding)
            output = output[:, :seq_output_len, :]

            output_mask = F.pad(output_mask, padding)
            output_mask = output_mask[:, :seq_output_len, :]          

        return output, output_mask, duration