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f5d2dd3 7965430 | 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 | # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn.functional as F
from torch import nn
from nemo.core import NeuralModule
from nemo.core.classes import Exportable, NeuralModule, typecheck
from nemo.core.neural_types import LabelsType, NeuralType, SpectrogramType
class RandomProjectionVectorQuantizer(NeuralModule, Exportable):
DIST_FN_LIST = ["l2", "cosine"]
def __init__(
self,
feat_in: int,
code_dim: int,
num_classes: int,
num_books: int,
dist_fn: str = "cosine",
time_ahead: bool = False,
freeze: bool = True,
squeeze_single: bool = False,
combine_time_steps: int = 1,
):
"""Vector quantization using random projection proposed in BEST-RQ paper:
'Self-Supervised Learning with Random-Projection Quantizer for Speech Recognition'
Args:
feat_in: input feature dimension
code_dim: dimension of the codebook features
num_classes: number of classes
num_books: number of codebooks
dist_fn: distance function to use, one of "l2" or "cosine"
time_ahead: if Ture, the input is of shape (B, T, D), otherwise (B, D, T)
freeze: whether to freeze the projection matrix
squeeze_single: if True, squeeze codebook dimension if num_books is 1
"""
super().__init__()
if dist_fn not in self.DIST_FN_LIST:
raise ValueError(f"Unknown distance function {dist_fn}, must be one of {self.DIST_FN_LIST}")
self.feat_in = feat_in
self.code_dim = code_dim
self.num_classes = num_classes
self.num_books = num_books
self.dist_fn = dist_fn
self.time_ahead = time_ahead
self.squeeze_single = squeeze_single
self.combine_time_steps = combine_time_steps
# (B, T, D) -> (B, T, num_books, code_dim)
self.proj = nn.Linear(self.feat_in * combine_time_steps, self.num_books * self.code_dim, bias=False)
torch.nn.init.xavier_normal_(self.proj.weight)
# (num_books, num_classes, hid_dim)
codebooks = torch.randn(self.num_books, self.num_classes, self.code_dim).double()
torch.nn.init.normal_(codebooks, mean=0, std=1)
codebooks = F.normalize(codebooks, dim=-1)
self.codebooks = nn.Parameter(codebooks)
if freeze:
self.freeze()
@property
def input_types(self):
"""Returns definitions of module input ports."""
if self.time_ahead:
return {"input_signal": NeuralType(('B', 'T', 'D'), SpectrogramType())}
return {"input_signal": NeuralType(('B', 'D', 'T'), SpectrogramType())}
@property
def output_types(self):
"""Returns definitions of module output ports."""
if self.time_ahead:
if self.num_books == 1 and self.squeeze_single:
return {
"xq": NeuralType(('B', 'T', 'D'), SpectrogramType()),
"xid": NeuralType(('B', 'T'), LabelsType()),
}
return {
"xq": NeuralType(('B', 'T', 'D', 'H'), SpectrogramType()),
"xid": NeuralType(('B', 'T', 'H'), LabelsType()),
}
if self.num_books == 1 and self.squeeze_single:
return {
"xq": NeuralType(('B', 'D', 'T'), SpectrogramType()),
"xid": NeuralType(('B', 'T'), LabelsType()),
}
return {
"xq": NeuralType(('B', 'D', 'T', 'H'), SpectrogramType()),
"xid": NeuralType(('B', 'T', 'H'), LabelsType()),
}
@typecheck()
def forward(self, input_signal):
"""
Args:
input_signal: input features of shape (B, T, D) or (B, D, T)
Returns:
xq: quantized features of shape (B, T, D, N) or (B, D, T, N)
xid: quantized tokens of shape (B, T, N)
"""
if not self.time_ahead:
# (B, D, T) -> (B, T, D)
input_signal = input_signal.transpose(1, 2)
B, T, _ = input_signal.size()
if self.combine_time_steps > 1:
input_signal = input_signal.contiguous().reshape(B, T // self.combine_time_steps, -1)
T = T // self.combine_time_steps
# (B, T, D) -> (B, T, num_books*code_dim)
x = self.proj(input_signal)
# normalize each feature vector
# (B, T, num_books*code_dim) -> (B, T, num_books, code_dim)
x = F.normalize(x.view(B, T, self.num_books, self.code_dim), dim=-1)
# get tokens (xid) of shape (B, T, num_books)
if self.dist_fn == "cosine":
# (B, T, num_books, code_dim) -> (B, T, num_books, num_classes)
xid = torch.einsum('btdh,dch->btdc', x, self.codebooks)
# (B, T, num_books, num_classes) -> (B, T, num_books)
xid = xid.max(dim=-1)[1]
elif self.dist_fn == "l2":
# (B, T, num_books, code_dim) -> (B, T, num_books, code_dim, num_classes)
xid = x.unsqueeze(-1) - self.codebooks.transpose(1, 2).unsqueeze(0).unsqueeze(0)
xid = xid.norm(dim=-2).argmin(dim=-1)
else:
raise ValueError(f"Unknown distance function {self.dist_fn}, must be one of {self.DIST_FN_LIST}")
# xid2: (B, T, num_books) -> (B, T, num_books)
xid2 = xid + self.num_classes * torch.arange(self.num_books, device=xid.device).unsqueeze(0).unsqueeze(0)
# xid2: (B, T, num_books) -> (B*num_books, T)
xid2 = xid2.transpose(1, 2).contiguous().view(-1, T)
# get quantized vector (xq) of shape (B, T, code_dim, num_books)
# codebook: (num_books, num_classes, code_dim) -> (num_books*num_classes, code_dim)
xq = F.embedding(xid2.view(-1), self.codebooks.view(-1, self.code_dim)).view(
B, T, self.code_dim, self.num_books
)
if not self.time_ahead:
# (B, T, D) -> (B, D, T)
xq = xq.transpose(1, 2)
if self.num_books == 1 and self.squeeze_single:
xq = xq.squeeze(-1)
xid = xid.squeeze(-1)
return xq, xid
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