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import copy | ||
import random | ||
from typing import Optional, Tuple | ||
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import torch | ||
import torch.nn as nn | ||
import torch.nn.functional as t_func | ||
from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present | ||
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class Hubert(nn.Module): | ||
def __init__(self, num_label_embeddings: int = 100, mask: bool = True): | ||
super().__init__() | ||
self._mask = mask | ||
self.feature_extractor = FeatureExtractor() | ||
self.feature_projection = FeatureProjection() | ||
self.positional_embedding = PositionalConvEmbedding() | ||
self.norm = nn.LayerNorm(768) | ||
self.dropout = nn.Dropout(0.1) | ||
self.encoder = TransformerEncoder( | ||
nn.TransformerEncoderLayer( | ||
768, 12, 3072, activation="gelu", batch_first=True | ||
), | ||
12, | ||
) | ||
self.proj = nn.Linear(768, 256) | ||
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self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_()) | ||
self.label_embedding = nn.Embedding(num_label_embeddings, 256) | ||
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def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: | ||
mask = None | ||
if self.training and self._mask: | ||
mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2) | ||
x[mask] = self.masked_spec_embed.to(x.dtype) | ||
return x, mask | ||
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def encode( | ||
self, x: torch.Tensor, layer: Optional[int] = None | ||
) -> Tuple[torch.Tensor, torch.Tensor]: | ||
x = self.feature_extractor(x) | ||
x = self.feature_projection(x.transpose(1, 2)) | ||
x, mask = self.mask(x) | ||
x = x + self.positional_embedding(x) | ||
x = self.dropout(self.norm(x)) | ||
x = self.encoder(x, output_layer=layer) | ||
return x, mask | ||
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def logits(self, x: torch.Tensor) -> torch.Tensor: | ||
logits = torch.cosine_similarity( | ||
x.unsqueeze(2), | ||
self.label_embedding.weight.unsqueeze(0).unsqueeze(0), | ||
dim=-1, | ||
) | ||
return logits / 0.1 | ||
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class HubertSoft(Hubert): | ||
def __init__(self): | ||
super().__init__() | ||
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def units(self, wav: torch.Tensor) -> torch.Tensor: | ||
wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2)) | ||
x, _ = self.encode(wav) | ||
return self.proj(x) | ||
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def forward(self, x): | ||
x_tst = self.units(x) | ||
x_tst = torch.repeat_interleave(x_tst, repeats=2, dim=1) | ||
return x_tst | ||
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class FeatureExtractor(nn.Module): | ||
def __init__(self): | ||
super().__init__() | ||
self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False) | ||
self.norm0 = nn.GroupNorm(512, 512) | ||
self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False) | ||
self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False) | ||
self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False) | ||
self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False) | ||
self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False) | ||
self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False) | ||
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def forward(self, x: torch.Tensor) -> torch.Tensor: | ||
x = t_func.gelu(self.norm0(self.conv0(x))) | ||
x = t_func.gelu(self.conv1(x)) | ||
x = t_func.gelu(self.conv2(x)) | ||
x = t_func.gelu(self.conv3(x)) | ||
x = t_func.gelu(self.conv4(x)) | ||
x = t_func.gelu(self.conv5(x)) | ||
x = t_func.gelu(self.conv6(x)) | ||
return x | ||
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class FeatureProjection(nn.Module): | ||
def __init__(self): | ||
super().__init__() | ||
self.norm = nn.LayerNorm(512) | ||
self.projection = nn.Linear(512, 768) | ||
self.dropout = nn.Dropout(0.1) | ||
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def forward(self, x: torch.Tensor) -> torch.Tensor: | ||
x = self.norm(x) | ||
x = self.projection(x) | ||
x = self.dropout(x) | ||
return x | ||
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class PositionalConvEmbedding(nn.Module): | ||
def __init__(self): | ||
super().__init__() | ||
self.conv = nn.Conv1d( | ||
768, | ||
768, | ||
kernel_size=128, | ||
padding=128 // 2, | ||
groups=16, | ||
) | ||
self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2) | ||
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def forward(self, x: torch.Tensor) -> torch.Tensor: | ||
x = self.conv(x.transpose(1, 2)) | ||
x = t_func.gelu(x[:, :, :-1]) | ||
return x.transpose(1, 2) | ||
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class TransformerEncoder(nn.Module): | ||
def __init__( | ||
self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int | ||
) -> None: | ||
super(TransformerEncoder, self).__init__() | ||
self.layers = nn.ModuleList( | ||
[copy.deepcopy(encoder_layer) for _ in range(num_layers)] | ||
) | ||
self.num_layers = num_layers | ||
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def forward( | ||
self, | ||
src: torch.Tensor, | ||
mask: torch.Tensor = None, | ||
src_key_padding_mask: torch.Tensor = None, | ||
output_layer: Optional[int] = None, | ||
) -> torch.Tensor: | ||
output = src | ||
for layer in self.layers[:output_layer]: | ||
output = layer( | ||
output, src_mask=mask, src_key_padding_mask=src_key_padding_mask | ||
) | ||
return output | ||
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def _compute_mask( | ||
shape: Tuple[int, int], | ||
mask_prob: float, | ||
mask_length: int, | ||
device: torch.device, | ||
min_masks: int = 0, | ||
) -> torch.Tensor: | ||
batch_size, sequence_length = shape | ||
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if mask_length < 1: | ||
raise ValueError("`mask_length` has to be bigger than 0.") | ||
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if mask_length > sequence_length: | ||
raise ValueError( | ||
f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`" | ||
) | ||
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# compute number of masked spans in batch | ||
num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random()) | ||
num_masked_spans = max(num_masked_spans, min_masks) | ||
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# make sure num masked indices <= sequence_length | ||
if num_masked_spans * mask_length > sequence_length: | ||
num_masked_spans = sequence_length // mask_length | ||
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# SpecAugment mask to fill | ||
mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool) | ||
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# uniform distribution to sample from, make sure that offset samples are < sequence_length | ||
uniform_dist = torch.ones( | ||
(batch_size, sequence_length - (mask_length - 1)), device=device | ||
) | ||
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# get random indices to mask | ||
mask_indices = torch.multinomial(uniform_dist, num_masked_spans) | ||
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# expand masked indices to masked spans | ||
mask_indices = ( | ||
mask_indices.unsqueeze(dim=-1) | ||
.expand((batch_size, num_masked_spans, mask_length)) | ||
.reshape(batch_size, num_masked_spans * mask_length) | ||
) | ||
offsets = ( | ||
torch.arange(mask_length, device=device)[None, None, :] | ||
.expand((batch_size, num_masked_spans, mask_length)) | ||
.reshape(batch_size, num_masked_spans * mask_length) | ||
) | ||
mask_idxs = mask_indices + offsets | ||
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# scatter indices to mask | ||
mask = mask.scatter(1, mask_idxs, True) | ||
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return mask | ||
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def hubert_soft( | ||
path: str, | ||
) -> HubertSoft: | ||
r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`. | ||
Args: | ||
path (str): path of a pretrained model | ||
""" | ||
hubert = HubertSoft() | ||
checkpoint = torch.load(path) | ||
consume_prefix_in_state_dict_if_present(checkpoint, "module.") | ||
hubert.load_state_dict(checkpoint) | ||
hubert.eval() | ||
return hubert |