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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], 0.0)
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=0.1,
ff_mult=2,
attn_dropout=0.1,
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=0.1,
ff_mult=2,
attn_dropout=0.1,
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(1.0))
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=0.2, voice_mask_percentage=0.2)
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)
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