pipeline_grad_tts.py

""" from https://github.com/jaywalnut310/glow-tts """

import math

import torch
from torch import nn

import tqdm
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import ConfigMixin
from diffusers.modeling_utils import ModelMixin

from .grad_tts_utils import GradTTSTokenizer  # flake8: noqa


def sequence_mask(length, max_length=None):
    if max_length is None:
        max_length = length.max()
    x = torch.arange(int(max_length), dtype=length.dtype, device=length.device)
    return x.unsqueeze(0) < length.unsqueeze(1)


def fix_len_compatibility(length, num_downsamplings_in_unet=2):
    while True:
        if length % (2**num_downsamplings_in_unet) == 0:
            return length
        length += 1


def convert_pad_shape(pad_shape):
    l = pad_shape[::-1]
    pad_shape = [item for sublist in l for item in sublist]
    return pad_shape


def generate_path(duration, mask):
    device = duration.device

    b, t_x, t_y = mask.shape
    cum_duration = torch.cumsum(duration, 1)
    path = torch.zeros(b, t_x, t_y, dtype=mask.dtype).to(device=device)

    cum_duration_flat = cum_duration.view(b * t_x)
    path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
    path = path.view(b, t_x, t_y)
    path = path - torch.nn.functional.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
    path = path * mask
    return path


def duration_loss(logw, logw_, lengths):
    loss = torch.sum((logw - logw_) ** 2) / torch.sum(lengths)
    return loss


class LayerNorm(nn.Module):
    def __init__(self, channels, eps=1e-4):
        super(LayerNorm, self).__init__()
        self.channels = channels
        self.eps = eps

        self.gamma = torch.nn.Parameter(torch.ones(channels))
        self.beta = torch.nn.Parameter(torch.zeros(channels))

    def forward(self, x):
        n_dims = len(x.shape)
        mean = torch.mean(x, 1, keepdim=True)
        variance = torch.mean((x - mean) ** 2, 1, keepdim=True)

        x = (x - mean) * torch.rsqrt(variance + self.eps)

        shape = [1, -1] + [1] * (n_dims - 2)
        x = x * self.gamma.view(*shape) + self.beta.view(*shape)
        return x


class ConvReluNorm(nn.Module):
    def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
        super(ConvReluNorm, self).__init__()
        self.in_channels = in_channels
        self.hidden_channels = hidden_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.n_layers = n_layers
        self.p_dropout = p_dropout

        self.conv_layers = torch.nn.ModuleList()
        self.norm_layers = torch.nn.ModuleList()
        self.conv_layers.append(torch.nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
        self.norm_layers.append(LayerNorm(hidden_channels))
        self.relu_drop = torch.nn.Sequential(torch.nn.ReLU(), torch.nn.Dropout(p_dropout))
        for _ in range(n_layers - 1):
            self.conv_layers.append(
                torch.nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2)
            )
            self.norm_layers.append(LayerNorm(hidden_channels))
        self.proj = torch.nn.Conv1d(hidden_channels, out_channels, 1)
        self.proj.weight.data.zero_()
        self.proj.bias.data.zero_()

    def forward(self, x, x_mask):
        x_org = x
        for i in range(self.n_layers):
            x = self.conv_layers[i](x * x_mask)
            x = self.norm_layers[i](x)
            x = self.relu_drop(x)
        x = x_org + self.proj(x)
        return x * x_mask


class DurationPredictor(nn.Module):
    def __init__(self, in_channels, filter_channels, kernel_size, p_dropout):
        super(DurationPredictor, self).__init__()
        self.in_channels = in_channels
        self.filter_channels = filter_channels
        self.p_dropout = p_dropout

        self.drop = torch.nn.Dropout(p_dropout)
        self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
        self.norm_1 = LayerNorm(filter_channels)
        self.conv_2 = torch.nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
        self.norm_2 = LayerNorm(filter_channels)
        self.proj = torch.nn.Conv1d(filter_channels, 1, 1)

    def forward(self, x, x_mask):
        x = self.conv_1(x * x_mask)
        x = torch.relu(x)
        x = self.norm_1(x)
        x = self.drop(x)
        x = self.conv_2(x * x_mask)
        x = torch.relu(x)
        x = self.norm_2(x)
        x = self.drop(x)
        x = self.proj(x * x_mask)
        return x * x_mask


class MultiHeadAttention(nn.Module):
    def __init__(
        self,
        channels,
        out_channels,
        n_heads,
        window_size=None,
        heads_share=True,
        p_dropout=0.0,
        proximal_bias=False,
        proximal_init=False,
    ):
        super(MultiHeadAttention, self).__init__()
        assert channels % n_heads == 0

        self.channels = channels
        self.out_channels = out_channels
        self.n_heads = n_heads
        self.window_size = window_size
        self.heads_share = heads_share
        self.proximal_bias = proximal_bias
        self.p_dropout = p_dropout
        self.attn = None

        self.k_channels = channels // n_heads
        self.conv_q = torch.nn.Conv1d(channels, channels, 1)
        self.conv_k = torch.nn.Conv1d(channels, channels, 1)
        self.conv_v = torch.nn.Conv1d(channels, channels, 1)
        if window_size is not None:
            n_heads_rel = 1 if heads_share else n_heads
            rel_stddev = self.k_channels**-0.5
            self.emb_rel_k = torch.nn.Parameter(
                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev
            )
            self.emb_rel_v = torch.nn.Parameter(
                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev
            )
        self.conv_o = torch.nn.Conv1d(channels, out_channels, 1)
        self.drop = torch.nn.Dropout(p_dropout)

        torch.nn.init.xavier_uniform_(self.conv_q.weight)
        torch.nn.init.xavier_uniform_(self.conv_k.weight)
        if proximal_init:
            self.conv_k.weight.data.copy_(self.conv_q.weight.data)
            self.conv_k.bias.data.copy_(self.conv_q.bias.data)
        torch.nn.init.xavier_uniform_(self.conv_v.weight)

    def forward(self, x, c, attn_mask=None):
        q = self.conv_q(x)
        k = self.conv_k(c)
        v = self.conv_v(c)

        x, self.attn = self.attention(q, k, v, mask=attn_mask)

        x = self.conv_o(x)
        return x

    def attention(self, query, key, value, mask=None):
        b, d, t_s, t_t = (*key.size(), query.size(2))
        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)

        scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels)
        if self.window_size is not None:
            assert t_s == t_t, "Relative attention is only available for self-attention."
            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
            rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings)
            rel_logits = self._relative_position_to_absolute_position(rel_logits)
            scores_local = rel_logits / math.sqrt(self.k_channels)
            scores = scores + scores_local
        if self.proximal_bias:
            assert t_s == t_t, "Proximal bias is only available for self-attention."
            scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
        if mask is not None:
            scores = scores.masked_fill(mask == 0, -1e4)
        p_attn = torch.nn.functional.softmax(scores, dim=-1)
        p_attn = self.drop(p_attn)
        output = torch.matmul(p_attn, value)
        if self.window_size is not None:
            relative_weights = self._absolute_position_to_relative_position(p_attn)
            value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
            output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
        output = output.transpose(2, 3).contiguous().view(b, d, t_t)
        return output, p_attn

    def _matmul_with_relative_values(self, x, y):
        ret = torch.matmul(x, y.unsqueeze(0))
        return ret

    def _matmul_with_relative_keys(self, x, y):
        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
        return ret

    def _get_relative_embeddings(self, relative_embeddings, length):
        pad_length = max(length - (self.window_size + 1), 0)
        slice_start_position = max((self.window_size + 1) - length, 0)
        slice_end_position = slice_start_position + 2 * length - 1
        if pad_length > 0:
            padded_relative_embeddings = torch.nn.functional.pad(
                relative_embeddings, convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]])
            )
        else:
            padded_relative_embeddings = relative_embeddings
        used_relative_embeddings = padded_relative_embeddings[:, slice_start_position:slice_end_position]
        return used_relative_embeddings

    def _relative_position_to_absolute_position(self, x):
        batch, heads, length, _ = x.size()
        x = torch.nn.functional.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
        x_flat = x.view([batch, heads, length * 2 * length])
        x_flat = torch.nn.functional.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]))
        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1 :]
        return x_final

    def _absolute_position_to_relative_position(self, x):
        batch, heads, length, _ = x.size()
        x = torch.nn.functional.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]))
        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
        x_flat = torch.nn.functional.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
        return x_final

    def _attention_bias_proximal(self, length):
        r = torch.arange(length, dtype=torch.float32)
        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)


class FFN(nn.Module):
    def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0.0):
        super(FFN, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.filter_channels = filter_channels
        self.kernel_size = kernel_size
        self.p_dropout = p_dropout

        self.conv_1 = torch.nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
        self.conv_2 = torch.nn.Conv1d(filter_channels, out_channels, kernel_size, padding=kernel_size // 2)
        self.drop = torch.nn.Dropout(p_dropout)

    def forward(self, x, x_mask):
        x = self.conv_1(x * x_mask)
        x = torch.relu(x)
        x = self.drop(x)
        x = self.conv_2(x * x_mask)
        return x * x_mask


class Encoder(nn.Module):
    def __init__(
        self,
        hidden_channels,
        filter_channels,
        n_heads,
        n_layers,
        kernel_size=1,
        p_dropout=0.0,
        window_size=None,
        **kwargs,
    ):
        super(Encoder, self).__init__()
        self.hidden_channels = hidden_channels
        self.filter_channels = filter_channels
        self.n_heads = n_heads
        self.n_layers = n_layers
        self.kernel_size = kernel_size
        self.p_dropout = p_dropout
        self.window_size = window_size

        self.drop = torch.nn.Dropout(p_dropout)
        self.attn_layers = torch.nn.ModuleList()
        self.norm_layers_1 = torch.nn.ModuleList()
        self.ffn_layers = torch.nn.ModuleList()
        self.norm_layers_2 = torch.nn.ModuleList()
        for _ in range(self.n_layers):
            self.attn_layers.append(
                MultiHeadAttention(
                    hidden_channels, hidden_channels, n_heads, window_size=window_size, p_dropout=p_dropout
                )
            )
            self.norm_layers_1.append(LayerNorm(hidden_channels))
            self.ffn_layers.append(
                FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout)
            )
            self.norm_layers_2.append(LayerNorm(hidden_channels))

    def forward(self, x, x_mask):
        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
        for i in range(self.n_layers):
            x = x * x_mask
            y = self.attn_layers[i](x, x, attn_mask)
            y = self.drop(y)
            x = self.norm_layers_1[i](x + y)
            y = self.ffn_layers[i](x, x_mask)
            y = self.drop(y)
            x = self.norm_layers_2[i](x + y)
        x = x * x_mask
        return x


class TextEncoder(ModelMixin, ConfigMixin):
    def __init__(
        self,
        n_vocab,
        n_feats,
        n_channels,
        filter_channels,
        filter_channels_dp,
        n_heads,
        n_layers,
        kernel_size,
        p_dropout,
        window_size=None,
        spk_emb_dim=64,
        n_spks=1,
    ):
        super(TextEncoder, self).__init__()

        self.register_to_config(
            n_vocab=n_vocab,
            n_feats=n_feats,
            n_channels=n_channels,
            filter_channels=filter_channels,
            filter_channels_dp=filter_channels_dp,
            n_heads=n_heads,
            n_layers=n_layers,
            kernel_size=kernel_size,
            p_dropout=p_dropout,
            window_size=window_size,
            spk_emb_dim=spk_emb_dim,
            n_spks=n_spks,
        )

        self.n_vocab = n_vocab
        self.n_feats = n_feats
        self.n_channels = n_channels
        self.filter_channels = filter_channels
        self.filter_channels_dp = filter_channels_dp
        self.n_heads = n_heads
        self.n_layers = n_layers
        self.kernel_size = kernel_size
        self.p_dropout = p_dropout
        self.window_size = window_size
        self.spk_emb_dim = spk_emb_dim
        self.n_spks = n_spks

        self.emb = torch.nn.Embedding(n_vocab, n_channels)
        torch.nn.init.normal_(self.emb.weight, 0.0, n_channels**-0.5)

        self.prenet = ConvReluNorm(n_channels, n_channels, n_channels, kernel_size=5, n_layers=3, p_dropout=0.5)

        self.encoder = Encoder(
            n_channels + (spk_emb_dim if n_spks > 1 else 0),
            filter_channels,
            n_heads,
            n_layers,
            kernel_size,
            p_dropout,
            window_size=window_size,
        )

        self.proj_m = torch.nn.Conv1d(n_channels + (spk_emb_dim if n_spks > 1 else 0), n_feats, 1)
        self.proj_w = DurationPredictor(
            n_channels + (spk_emb_dim if n_spks > 1 else 0), filter_channels_dp, kernel_size, p_dropout
        )

    def forward(self, x, x_lengths, spk=None):
        x = self.emb(x) * math.sqrt(self.n_channels)
        x = torch.transpose(x, 1, -1)
        x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)

        x = self.prenet(x, x_mask)
        if self.n_spks > 1:
            x = torch.cat([x, spk.unsqueeze(-1).repeat(1, 1, x.shape[-1])], dim=1)
        x = self.encoder(x, x_mask)
        mu = self.proj_m(x) * x_mask

        x_dp = torch.detach(x)
        logw = self.proj_w(x_dp, x_mask)

        return mu, logw, x_mask


class GradTTS(DiffusionPipeline):
    def __init__(self, unet, text_encoder, noise_scheduler, tokenizer):
        super().__init__()
        noise_scheduler = noise_scheduler.set_format("pt")
        self.register_modules(
            unet=unet, text_encoder=text_encoder, noise_scheduler=noise_scheduler, tokenizer=tokenizer
        )

    @torch.no_grad()
    def __call__(
        self, text, num_inference_steps=50, temperature=1.3, length_scale=0.91, speaker_id=15, torch_device=None
    ):
        if torch_device is None:
            torch_device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

        self.unet.to(torch_device)
        self.text_encoder.to(torch_device)

        x, x_lengths = self.tokenizer(text)
        x = x.to(torch_device)
        x_lengths = x_lengths.to(torch_device)

        if speaker_id is not None:
            speaker_id = torch.LongTensor([speaker_id]).to(torch_device)

        # Get encoder_outputs `mu_x` and log-scaled token durations `logw`
        mu_x, logw, x_mask = self.text_encoder(x, x_lengths)

        w = torch.exp(logw) * x_mask
        w_ceil = torch.ceil(w) * length_scale
        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
        y_max_length = int(y_lengths.max())
        y_max_length_ = fix_len_compatibility(y_max_length)

        # Using obtained durations `w` construct alignment map `attn`
        y_mask = sequence_mask(y_lengths, y_max_length_).unsqueeze(1).to(x_mask.dtype)
        attn_mask = x_mask.unsqueeze(-1) * y_mask.unsqueeze(2)
        attn = generate_path(w_ceil.squeeze(1), attn_mask.squeeze(1)).unsqueeze(1)

        # Align encoded text and get mu_y
        mu_y = torch.matmul(attn.squeeze(1).transpose(1, 2), mu_x.transpose(1, 2))
        mu_y = mu_y.transpose(1, 2)

        # Sample latent representation from terminal distribution N(mu_y, I)
        z = mu_y + torch.randn_like(mu_y, device=mu_y.device) / temperature

        xt = z * y_mask
        h = 1.0 / num_inference_steps
        for t in tqdm.tqdm(range(num_inference_steps), total=num_inference_steps):
            t = (1.0 - (t + 0.5) * h) * torch.ones(z.shape[0], dtype=z.dtype, device=z.device)
            time = t.unsqueeze(-1).unsqueeze(-1)

            residual = self.unet(xt, t, mu_y, y_mask, speaker_id)

            xt = self.noise_scheduler.step(xt, residual, mu_y, h, time)
            xt = xt * y_mask

        return xt[:, :, :y_max_length]