DL-Art-School/codes/models/clip/text_cond_clip.py

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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import einsum
from models.audio.tts.unified_voice2 import ConditioningEncoder
from models.lucidrains.dalle.transformer import Transformer
from trainer.networks import register_model
from utils.util import opt_get
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 VoiceCondCLIP(nn.Module):
"""
CLIP model retrofitted for performing contrastive evaluation between tokenized audio data and an encoded conditioning
clip.
Originally from https://github.com/lucidrains/DALLE-pytorch/blob/main/dalle_pytorch/dalle_pytorch.py
"""
def __init__(
self,
*,
dim_speech=512,
dim_latent=512,
num_speech_tokens=8192,
speech_enc_depth=6,
speech_heads=8,
speech_seq_len=250,
voice_mask_percentage=0,
wav_token_compression=1024,
):
super().__init__()
self.cond_encoder = ConditioningEncoder(80, dim_latent, do_checkpointing=True)
self.speech_emb = nn.Embedding(num_speech_tokens, dim_speech)
self.speech_pos_emb = nn.Embedding(num_speech_tokens, dim_speech)
self.speech_transformer = Transformer(causal=False, seq_len=speech_seq_len, dim=dim_speech,
depth=speech_enc_depth, heads=speech_heads, rotary_emb=False)
self.to_speech_latent = nn.Linear(dim_speech, dim_latent, bias=False)
self.temperature = nn.Parameter(torch.tensor(1.))
self.voice_mask_percentage = voice_mask_percentage
self.wav_token_compression = wav_token_compression
def forward(
self,
cond_mel,
speech_tokens,
wav_lengths,
return_loss=False
):
# This model will receive micro-batches with a ton of padding for the speech tokens. Ameliorate this by
# chopping the inputs by the maximum actual length.
max_mel_len = wav_lengths.max() // self.wav_token_compression
speech_tokens = speech_tokens[:, :max_mel_len]
b, device = speech_tokens.shape[0], speech_tokens.device
if self.training:
voice_mask = torch.rand_like(speech_tokens.float()) > self.voice_mask_percentage
else:
voice_mask = torch.ones_like(speech_tokens.float()).bool()
speech_emb = self.speech_emb(speech_tokens)
speech_emb += self.speech_pos_emb(torch.arange(speech_emb.shape[1], device=device))
cond_latents = self.cond_encoder(cond_mel)
enc_speech = self.speech_transformer(speech_emb, mask=voice_mask)
speech_latents = masked_mean(enc_speech, voice_mask, dim=1)
speech_latents = self.to_speech_latent(speech_latents)
cond_latents, speech_latents = map(lambda t: F.normalize(t, p=2, dim=-1), (cond_latents, speech_latents))
temp = self.temperature.exp()
if not return_loss:
sim = einsum('n d, n d -> n', cond_latents, speech_latents) * temp
return sim
sim = einsum('i d, j d -> i j', cond_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
@register_model
def register_voice_cond_clip(opt_net, opt):
return VoiceCondCLIP(**opt_get(opt_net, ['kwargs'], {}))
if __name__ == '__main__':
clip = VoiceCondCLIP(voice_mask_percentage=.2)
clip(torch.randn(2,80,400),
torch.randint(0,8192,(2,250)),
torch.tensor([101,102]),
return_loss=True)
nonloss = clip(
torch.randn(2, 80, 400),
torch.randint(0,8192,(2,250)),
torch.tensor([101,102]),
return_loss=False)
print(nonloss.shape)