File size: 5,783 Bytes
b36e9ec |
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 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import einsum
from tortoise.models.arch_util import CheckpointedXTransformerEncoder
from tortoise.models.transformer import Transformer
from tortoise.models.xtransformers import Encoder
def exists(val):
return val is not None
def masked_mean(t, mask, dim = 1):
t = t.masked_fill(~mask[:, :, None], 0.)
return t.sum(dim = 1) / mask.sum(dim = 1)[..., None]
class CLVP(nn.Module):
"""
CLIP model retrofitted for performing contrastive evaluation between tokenized audio data and the corresponding
transcribed text.
Originally from https://github.com/lucidrains/DALLE-pytorch/blob/main/dalle_pytorch/dalle_pytorch.py
"""
def __init__(
self,
*,
dim_text=512,
dim_speech=512,
dim_latent=512,
num_text_tokens=256,
text_enc_depth=6,
text_seq_len=120,
text_heads=8,
num_speech_tokens=8192,
speech_enc_depth=6,
speech_heads=8,
speech_seq_len=250,
text_mask_percentage=0,
voice_mask_percentage=0,
wav_token_compression=1024,
use_xformers=False,
):
super().__init__()
self.text_emb = nn.Embedding(num_text_tokens, dim_text)
self.to_text_latent = nn.Linear(dim_text, dim_latent, bias=False)
self.speech_emb = nn.Embedding(num_speech_tokens, dim_speech)
self.to_speech_latent = nn.Linear(dim_speech, dim_latent, bias=False)
if use_xformers:
self.text_transformer = CheckpointedXTransformerEncoder(
needs_permute=False,
exit_permute=False,
max_seq_len=-1,
attn_layers=Encoder(
dim=dim_text,
depth=text_enc_depth,
heads=text_heads,
ff_dropout=.1,
ff_mult=2,
attn_dropout=.1,
use_rmsnorm=True,
ff_glu=True,
rotary_pos_emb=True,
))
self.speech_transformer = CheckpointedXTransformerEncoder(
needs_permute=False,
exit_permute=False,
max_seq_len=-1,
attn_layers=Encoder(
dim=dim_speech,
depth=speech_enc_depth,
heads=speech_heads,
ff_dropout=.1,
ff_mult=2,
attn_dropout=.1,
use_rmsnorm=True,
ff_glu=True,
rotary_pos_emb=True,
))
else:
self.text_transformer = Transformer(causal=False, seq_len=text_seq_len, dim=dim_text, depth=text_enc_depth,
heads=text_heads)
self.speech_transformer = Transformer(causal=False, seq_len=speech_seq_len, dim=dim_speech,
depth=speech_enc_depth, heads=speech_heads)
self.temperature = nn.Parameter(torch.tensor(1.))
self.text_mask_percentage = text_mask_percentage
self.voice_mask_percentage = voice_mask_percentage
self.wav_token_compression = wav_token_compression
self.xformers = use_xformers
if not use_xformers:
self.text_pos_emb = nn.Embedding(text_seq_len, dim_text)
self.speech_pos_emb = nn.Embedding(num_speech_tokens, dim_speech)
def forward(
self,
text,
speech_tokens,
return_loss=False
):
b, device = text.shape[0], text.device
if self.training:
text_mask = torch.rand_like(text.float()) > self.text_mask_percentage
voice_mask = torch.rand_like(speech_tokens.float()) > self.voice_mask_percentage
else:
text_mask = torch.ones_like(text.float()).bool()
voice_mask = torch.ones_like(speech_tokens.float()).bool()
text_emb = self.text_emb(text)
speech_emb = self.speech_emb(speech_tokens)
if not self.xformers:
text_emb += self.text_pos_emb(torch.arange(text.shape[1], device=device))
speech_emb += self.speech_pos_emb(torch.arange(speech_emb.shape[1], device=device))
enc_text = self.text_transformer(text_emb, mask=text_mask)
enc_speech = self.speech_transformer(speech_emb, mask=voice_mask)
text_latents = masked_mean(enc_text, text_mask, dim=1)
speech_latents = masked_mean(enc_speech, voice_mask, dim=1)
text_latents = self.to_text_latent(text_latents)
speech_latents = self.to_speech_latent(speech_latents)
text_latents, speech_latents = map(lambda t: F.normalize(t, p=2, dim=-1), (text_latents, speech_latents))
temp = self.temperature.exp()
if not return_loss:
sim = einsum('n d, n d -> n', text_latents, speech_latents) * temp
return sim
sim = einsum('i d, j d -> i j', text_latents, speech_latents) * temp
labels = torch.arange(b, device=device)
loss = (F.cross_entropy(sim, labels) + F.cross_entropy(sim.t(), labels)) / 2
return loss
if __name__ == '__main__':
clip = CLVP(text_mask_percentage=.2, voice_mask_percentage=.2)
clip(torch.randint(0,256,(2,120)),
torch.tensor([50,100]),
torch.randint(0,8192,(2,250)),
torch.tensor([101,102]),
return_loss=True)
nonloss = clip(torch.randint(0,256,(2,120)),
torch.tensor([50,100]),
torch.randint(0,8192,(2,250)),
torch.tensor([101,102]),
return_loss=False)
print(nonloss.shape) |