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from transformers import PreTrainedModel, PretrainedConfig, Wav2Vec2ForCTC
import json
import torch
from torch import nn
from torch.nn.utils.rnn import pad_sequence
import math
from typing import Optional
# x: torch.FloatTensor [T, B, D]
# mask: torch.BoolTensor [B, T], where True indicates padding
# returns: torch.LongTensor [B]
def get_lengths(x, mask=None):
if mask is not None:
return (~mask).long().sum(dim=1)
else:
return torch.LongTensor([x.size(0)] * x.size(1)).to(x.device)
# lens: torch.LongTensor [B]
# returns: torch.BoolTensor [B, max_lens], where True indicates padding
def lengths_to_padding_mask(lens):
bsz, max_lens = lens.size(0), torch.max(lens).item()
mask = torch.arange(max_lens).to(lens.device).view(1, max_lens)
mask = mask.expand(bsz, -1) >= lens.view(bsz, 1).expand(-1, max_lens)
return mask
# input_lengths: torch.LongTensor [B]
def get_output_lengths(input_lengths):
conv_feature_layers = "[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512,2,2)] + [(512,2,2)]"
conv_cfg_list = eval(conv_feature_layers)
def _conv_out_length(input_length, kernel_size, stride):
return torch.floor((input_length - kernel_size) / stride + 1)
for i in range(len(conv_cfg_list)):
input_lengths = _conv_out_length(
input_lengths, conv_cfg_list[i][1], conv_cfg_list[i][2]
)
return input_lengths.to(torch.long)
class ZeroSwotEncoderConfig(PretrainedConfig):
model_type = "zero_swot_encoder"
def __init__(
self,
wav2vec2_model_name_or_path="",
compression_adapter=None,
embed_dim=1024,
**kwargs
):
super().__init__(**kwargs)
self.wav2vec2_model_name_or_path = wav2vec2_model_name_or_path
self.compression_adapter = compression_adapter
self.embed_dim = embed_dim
@classmethod
def from_json_file(cls, json_file):
with open(json_file, "r") as reader:
text = reader.read()
config_dict = json.loads(text)
return cls(**config_dict)
class ZeroSwotEncoderModel(PreTrainedModel):
config_class = ZeroSwotEncoderConfig
model_type = "zero_swot_encoder"
def __init__(self, config):
super().__init__(config)
self.wav2vec2 = Wav2Vec2ForCTC.from_pretrained(config.wav2vec2_model_name_or_path)
self.compression_adapter = CompressionAdapter(config.compression_adapter)
self.speech_embedder = SpeechEmbedder(config.embed_dim)
def forward(self, input_values, attention_mask=None):
input_lens = get_lengths(input_values, ~attention_mask)
# Forward pass through wav2vec2 encoder
x = self.wav2vec2.wav2vec2(input_values, attention_mask)[0] # [B, T, D]
# CTC predictions
preds = self.wav2vec2.lm_head(x).argmax(-1) # [B, T]
# Get output lengths for x
output_lens = get_output_lengths(input_lens)
# Compression
x, mask, _ = self.compression_adapter(x, preds, output_lens) # [B, N, D] with N << T
# BOS and EOS embeddings
x, mask = self.speech_embedder(x, mask) # [B, N+2, D]
return x, ~mask
class SpeechEmbedder(nn.Module):
def __init__(self, embed_dim):
super().__init__()
self.embed_dim = embed_dim
self.bos_emb = nn.Parameter(torch.empty(embed_dim))
self.eos_emb = nn.Parameter(torch.empty(embed_dim))
self.scale = self.embed_dim ** 0.5
def forward(self, x, padding_mask=None):
"""Add special embedding and positional embedding.
Args:
x (FloatTensor): (B, T, C)
padding_mask (ByteTensor): (B, T)
Outputs:
x (FloatTensor): (B, T+2, C)
padding_mask (ByteTensor): (B, T+2)
"""
B = x.size(0)
lengths = get_lengths(x.transpose(0, 1), padding_mask)
assert B == len(lengths)
if padding_mask is not None:
x = x * (1 - padding_mask.unsqueeze(-1).type_as(x))
# prepend bos
x = torch.cat([self.bos_emb.view(1, 1, -1).expand(B, 1, -1), x], dim=1)
lengths += 1
# append padding (zeros) and then convert first padding to eos
x = torch.cat([x, torch.zeros(B, 1, x.size(-1), device=x.device, dtype=x.dtype)], dim=1)
for i in range(B):
x[i, lengths[i], :] = self.eos_emb
lengths += 1
padding_mask = lengths_to_padding_mask(lengths)
x = x * self.scale
return x, padding_mask
class PositionalEmbedding(nn.Module):
def __init__(self, num_embeddings, embedding_dim, padding_idx):
super().__init__()
self.embedding_dim = embedding_dim
self.padding_idx = padding_idx if padding_idx is not None else 0
num_embeddings += padding_idx + 1
self.weights = PositionalEmbedding.get_embedding(
num_embeddings, embedding_dim, padding_idx
)
self.register_buffer("_float_tensor", torch.FloatTensor(1))
self.max_positions = int(1e5)
@staticmethod
def get_embedding(
num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None
):
half_dim = embedding_dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
if embedding_dim % 2 == 1:
# zero pad
emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
if padding_idx is not None:
emb[padding_idx, :] = 0
return emb
def make_positions(self, x, padding_idx: int):
mask = x.ne(padding_idx).int()
return (torch.cumsum(mask, dim=1).type_as(mask) * mask).long() + padding_idx
def forward(self, input):
"""Input is expected to be of size [bsz x seqlen]."""
bsz, seq_len = input.size()
max_pos = self.padding_idx + 1 + seq_len
if self.weights is None or max_pos > self.weights.size(0):
# recompute/expand embeddings if needed
self.weights = PositionalEmbedding.get_embedding(
max_pos, self.embedding_dim, self.padding_idx
)
self.weights = self.weights.to(self._float_tensor)
positions = self.make_positions(input, self.padding_idx)
return (
self.weights.index_select(0, positions.view(-1))
.view(bsz, seq_len, -1)
.detach()
)
class CLSPooling(nn.Module):
def __init__(self, embed_dim, num_transformer_layers, dropout_rate):
super().__init__()
self.cls_token = nn.Parameter(torch.empty(1, 1, embed_dim))
nn.init.normal_(self.cls_token, mean=0.0, std=0.25)
self.transformer = nn.TransformerEncoder(
nn.TransformerEncoderLayer(
embed_dim,
nhead=16 if embed_dim == 1024 else 8,
dim_feedforward=4*embed_dim,
dropout=dropout_rate,
activation="relu",
batch_first=True,
norm_first=True
),
num_layers=num_transformer_layers,
)
self.pos_emb = PositionalEmbedding(512, embed_dim, 1)
self.scale = math.sqrt(embed_dim)
def forward(self, x, lens):
# x: [B, N, D]
# lens: [B]
# prepend cls token
x = torch.cat(
[
self.cls_token.to(dtype=x.dtype, device=x.device).repeat(x.size(0), 1, 1), # B x 1 x D
x
],
dim=1) # [B, N+1, D]
mask = lengths_to_padding_mask(lens+1)
x = x + self.pos_emb(mask.long()) / self.scale
x = self.transformer(x, src_key_padding_mask=mask) # [B, N+1, D]
x = x[:, 0] # [B, D]
return x
class CompressionAdapter(nn.Module):
def __init__(self, cfg):
super().__init__()
self.embed_dim = cfg["embed_dim"]
self.transformer_layers = cfg["transformer_layers"]
self.dropout = cfg["dropout"]
self.blank_idx = cfg["blank_idx"]
self.sep_idx = cfg["sep_idx"]
self.token_pooling_module = CLSPooling(
self.embed_dim, self.transformer_layers, self.dropout
)
def char_compression(self, x, preds, lens):
# x: B x T x D
# preds: B x T
# lens: B
B, T, D = x.size()
device = x.device
dtype = x.dtype
# zero-out the padding
mask = lengths_to_padding_mask(lens) # B x T
x = x.masked_fill(mask.unsqueeze(-1), 0)
preds = preds.masked_fill(mask, self.blank_idx)
# add a vector of -1 to know where each example ends after flattening the batch
preds = torch.cat([-torch.ones(B, 1, device=device, dtype=torch.long), preds], dim=1).view(-1)
x = torch.cat([torch.zeros(B, 1, D, device=device, dtype=dtype), x], dim=1).view(-1, D)
# get points of consecutive preds
preds, counts = preds.unique_consecutive(return_counts=True)
# split in representations of same chars
x = torch.split(x, counts.tolist())
# remove blanks
valid_mask = preds != self.blank_idx
preds = preds[valid_mask]
counts = counts[valid_mask] # [N]
x = [x_i for x_i, v_i in zip(x, valid_mask) if v_i]
# pack into tensor
x = pad_sequence(x, batch_first=True, padding_value=0)
# char pooling
x = torch.sum(x, dim=1) / counts.to(dtype=x.dtype).unsqueeze(1) # [B, N, D] -> [B, D]
# find split points for retrieving the examples
split_points = (preds == -1).nonzero(as_tuple=True)[0]
split_points = torch.cat([split_points, torch.tensor([len(preds)], device=device)])
split_points = (split_points[1:] - split_points[:-1]).tolist()
# split into examples
x = torch.split(x, split_points)
preds = torch.split(preds, split_points)
lens = torch.tensor([len(x_i) for x_i in x], device=device)
# pack into tensors
x = pad_sequence(x, batch_first=True, padding_value=0)
preds = pad_sequence(preds, batch_first=True, padding_value=self.blank_idx)
# remove the parts we add to identify the bounds for each example
x = x[:, 1:]
preds = preds[:, 1:]
lens -= 1
mask = lengths_to_padding_mask(lens)
# account for empty examples (just a sep token)
empty_examples = lens == 0
num_empty_examples = empty_examples.sum()
if num_empty_examples > 0:
mask[empty_examples, 0] = True
lens[empty_examples] = 1
preds[empty_examples, 0] = self.sep_idx
return x, mask, lens, preds, num_empty_examples
def token_compression(self, x, preds, lens):
# x: B x T x D
# preds: B x T
# lens: B
B, T, D = x.size()
device = x.device
dtype = x.dtype
# new lengths after compression
new_lens = preds.eq(self.sep_idx).sum(dim=1)
# unpad and unpack to list of tensors
preds = [preds[i, :lens[i]] for i in range(B)]
x = [x[i, :lens[i]] for i in range(B)]
# make sure every example ends with a separator
num_examples_without_ending_sep = torch.tensor(0, device=device, dtype=torch.long)
for i in range(B):
if preds[i][-1] != self.sep_idx:
preds[i] = torch.cat([preds[i], torch.tensor([self.sep_idx], device=device, dtype=torch.long)])
x[i] = torch.cat([x[i], torch.zeros(1, D, device=device, dtype=dtype)])
new_lens[i] += 1
num_examples_without_ending_sep += 1
# flatten
preds = torch.cat(preds)
x = torch.cat(x)
# split points according to separators
split_points = preds.eq(self.sep_idx).nonzero(as_tuple=True)[0] + 1
split_points = torch.cat([torch.tensor([0], device=device, dtype=torch.long), split_points])
split_points = (split_points[1:] - split_points[:-1]).tolist()
# re-arrange in 3d [total_num_tokens x max(count) x D]
x = torch.split(x, split_points) # Tuple[2d tensor]
counts = torch.tensor([len(x_i) for x_i in x], device=device, dtype=torch.long)
x = pad_sequence(x, batch_first=True, padding_value=0)
# reduce dim 1
x = self.token_pooling_module(x, counts)
# reconstruct the batch
split_points = new_lens.cumsum(dim=0)
split_points = torch.cat([torch.tensor([0], device=device, dtype=torch.long), split_points])
split_points = (split_points[1:] - split_points[:-1]).tolist()
x = torch.split(x, split_points)
x = pad_sequence(x, batch_first=True, padding_value=0) # B x ? x D
mask = lengths_to_padding_mask(new_lens)
return x, mask, new_lens, num_examples_without_ending_sep
def forward(self, x, preds, lens):
x, mask, lens, preds, _ = self.char_compression(x, preds, lens)
x, mask, lens, _ = self.token_compression(x, preds, lens)
return x, mask, lens |