Spaces:
Running
Running
File size: 7,225 Bytes
960cd20 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 |
import copy
from typing import Optional, Tuple
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
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)
self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
self.label_embedding = nn.Embedding(num_label_embeddings, 256)
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
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
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
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
x, mask = self.encode(x)
x = self.proj(x)
logits = self.logits(x)
return logits, mask
class HubertSoft(Hubert):
def __init__(self):
super().__init__()
@torch.inference_mode()
def units(self, wav: torch.Tensor) -> torch.Tensor:
wav = F.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
x, _ = self.encode(wav)
return self.proj(x)
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)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = F.gelu(self.norm0(self.conv0(x)))
x = F.gelu(self.conv1(x))
x = F.gelu(self.conv2(x))
x = F.gelu(self.conv3(x))
x = F.gelu(self.conv4(x))
x = F.gelu(self.conv5(x))
x = F.gelu(self.conv6(x))
return x
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)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.norm(x)
x = self.projection(x)
x = self.dropout(x)
return x
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)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.conv(x.transpose(1, 2))
x = F.gelu(x[:, :, :-1])
return x.transpose(1, 2)
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
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
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
if mask_length < 1:
raise ValueError("`mask_length` has to be bigger than 0.")
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}`"
)
# 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)
# make sure num masked indices <= sequence_length
if num_masked_spans * mask_length > sequence_length:
num_masked_spans = sequence_length // mask_length
# SpecAugment mask to fill
mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
# 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
)
# get random indices to mask
mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
# 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
# scatter indices to mask
mask = mask.scatter(1, mask_idxs, True)
return mask
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
|