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Running on Zero
Running on Zero
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4a9b62b | 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 | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Portions of this code are a modification of the fairseq project.
# The original source code can be found at:
# https://github.com/facebookresearch/fairseq/blob/d13e14a800bb588e5a77fb4e551f554ff9b24a72/fairseq/models/wav2vec/wav2vec.py
#
# Copyright (c) 2025 Yiwei Guo
# Licensed under the MIT license.
import math
import torch.nn as nn
from lscodec.models.fairseq_modules.fp32_group_norm import Fp32GroupNorm
from lscodec.models.fairseq_modules.layer_norm import Fp32LayerNorm
from lscodec.models.fairseq_modules.transpose_last import TransposeLast
import torch
def norm_block(is_layer_norm, dim, affine=True):
if is_layer_norm:
mod = nn.Sequential(
TransposeLast(),
Fp32LayerNorm(dim, elementwise_affine=affine),
TransposeLast(),
)
else:
mod = Fp32GroupNorm(1, dim, affine=affine)
return mod
class ZeroPad1d(nn.Module):
def __init__(self, pad_left, pad_right):
super().__init__()
self.pad_left = pad_left
self.pad_right = pad_right
def forward(self, x):
return nn.functional.pad(x, (self.pad_left, self.pad_right))
class ConvFeatureExtractionModel(nn.Module):
def __init__(
self,
conv_layers,
dropout,
log_compression,
skip_connections,
residual_scale,
non_affine_group_norm,
activation,
):
super().__init__()
def block(n_in, n_out, k, stride):
return nn.Sequential(
nn.Conv1d(n_in, n_out, k, stride=stride, bias=False),
nn.Dropout(p=dropout),
norm_block(
is_layer_norm=False, dim=n_out, affine=not non_affine_group_norm
),
activation,
)
in_d = 1
self.conv_layers = nn.ModuleList()
for dim, k, stride in conv_layers:
self.conv_layers.append(block(in_d, dim, k, stride))
in_d = dim
self.log_compression = log_compression
self.skip_connections = skip_connections
self.residual_scale = math.sqrt(residual_scale)
def forward(self, x): # x is audio of shape [B, 1, T]
# Bx1xT -> BxCxT
for conv in self.conv_layers:
residual = x
x = conv(x)
if self.skip_connections and x.size(1) == residual.size(1):
tsz = x.size(2)
r_tsz = residual.size(2)
residual = residual[..., :: r_tsz // tsz][..., :tsz]
x = (x + residual) * self.residual_scale
if self.log_compression:
x = x.abs()
x = x + 1
x = x.log()
return x
class ConvAggregator(nn.Module):
def __init__(
self,
conv_layers,
embed,
dropout,
skip_connections,
residual_scale,
non_affine_group_norm,
conv_bias,
zero_pad,
activation,
):
super().__init__()
def block(n_in, n_out, k, stride):
# padding dims only really make sense for stride = 1
ka = k // 2
kb = ka - 1 if k % 2 == 0 else ka
pad = (
ZeroPad1d(ka + kb, 0) if zero_pad else nn.ReplicationPad1d((ka + kb, 0))
)
return nn.Sequential(
pad,
nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias),
nn.Dropout(p=dropout),
norm_block(False, n_out, affine=not non_affine_group_norm),
activation,
)
in_d = embed
self.conv_layers = nn.ModuleList()
self.residual_proj = nn.ModuleList()
for dim, k, stride in conv_layers:
if in_d != dim and skip_connections:
self.residual_proj.append(nn.Conv1d(in_d, dim, 1, bias=False))
else:
self.residual_proj.append(None)
self.conv_layers.append(block(in_d, dim, k, stride))
in_d = dim
self.conv_layers = nn.Sequential(*self.conv_layers)
self.skip_connections = skip_connections
self.residual_scale = math.sqrt(residual_scale)
def forward(self, x):
for rproj, conv in zip(self.residual_proj, self.conv_layers):
residual = x
x = conv(x)
if self.skip_connections:
if rproj is not None:
residual = rproj(residual)
x = (x + residual) * self.residual_scale
return x
class LSCodecEncoder(nn.Module):
def __init__(self, conv_extraction_model: ConvFeatureExtractionModel,
conv_aggregation_model: ConvAggregator,
dropout_p: float = 0.1):
super().__init__()
self.conv_models = nn.Sequential(conv_extraction_model, conv_aggregation_model)
self.dropout = nn.Dropout(dropout_p)
def forward(self, waveform, frame_mask, length_tolerance=3):
# waveform: [B, 1, T]. Frame mask is 1 at valid values.
conv_features = self.conv_models(waveform) # [B, 1024, L]
conv_features = self.dropout(conv_features)
B, D, L = conv_features.shape
if frame_mask is None:
frame_mask = torch.ones(size=(B, L)).bool().to(conv_features.device)
if (frame_mask is not None) and (0 < frame_mask.shape[-1] - L <= length_tolerance):
pad_tensor = torch.zeros(size=(B, D, frame_mask.shape[-1] - L)).to(conv_features.device)
# NOTE: we don't pad these values with 999, but instead treat those as valid data points
# NOTE: this is because the mask is longer than the valid data points, and changing the mask is inconvenient.
conv_features = torch.concat([conv_features, pad_tensor], dim=-1)
return conv_features
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