DL-Art-School/codes/models/clip/mel_text_clip.py
2022-03-03 21:53:32 -07:00

150 lines
5.9 KiB
Python

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from torch import einsum
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 MelTextCLIP(nn.Module):
"""
CLIP model retrofitted for performing contrastive evaluation between MEL 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,
mel_compression=256,
):
super().__init__()
self.text_emb = nn.Embedding(num_text_tokens, dim_text)
self.text_pos_emb = nn.Embedding(text_seq_len, dim_text)
self.text_transformer = Transformer(causal=False, seq_len=text_seq_len, dim=dim_text, depth=text_enc_depth,
heads=text_heads, rotary_emb=False)
self.to_text_latent = nn.Linear(dim_text, dim_latent, bias=False)
self.speech_enc = nn.Conv1d(80, dim_speech, kernel_size=3, padding=1)
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.text_mask_percentage = text_mask_percentage
self.voice_mask_percentage = voice_mask_percentage
self.mel_compression = mel_compression
def get_text_projections(self, text, text_mask=None):
if text_mask is None:
text_mask = torch.ones_like(text.float()).bool()
text_emb = self.text_emb(text)
text_emb += self.text_pos_emb(torch.arange(text.shape[1], device=text.device))
with torch.autocast(text.device.type):
enc_text = self.text_transformer(text_emb, mask=text_mask)
text_latents = masked_mean(enc_text, text_mask, dim=1)
return self.to_text_latent(text_latents).float()
def get_speech_projection(self, mel, voice_mask=None):
if voice_mask is None:
voice_mask = torch.ones_like(mel[:,0,:].float()).bool()
speech_emb = self.speech_enc(mel).permute(0,2,1)
speech_emb += self.speech_pos_emb(torch.arange(speech_emb.shape[1], device=mel.device))
with torch.autocast(speech_emb.device.type):
enc_speech = self.speech_transformer(speech_emb, mask=voice_mask)
speech_latents = masked_mean(enc_speech, voice_mask, dim=1)
return self.to_speech_latent(speech_latents).float()
def forward(
self,
text,
text_lengths,
mel,
wav_lengths,
return_loss=False
):
# This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
# chopping the inputs by the maximum actual length.
max_text_len = text_lengths.max()
text = text[:, :max_text_len]
max_mel_len = wav_lengths.max() // self.mel_compression
mel = mel[:, :, :max_mel_len]
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(mel[:,0,:].float()) > self.voice_mask_percentage
else:
text_mask = torch.ones_like(text.float()).bool()
voice_mask = torch.ones_like(mel[:,0,:].float()).bool()
text_emb = self.text_emb(text)
text_emb += self.text_pos_emb(torch.arange(text.shape[1], device=device))
speech_emb = self.speech_enc(mel).permute(0,2,1)
speech_emb += self.speech_pos_emb(torch.arange(speech_emb.shape[1], device=device))
# Only autocast the transformer part. The MEL encoder loses accuracy if you autcast it.
with torch.autocast(speech_emb.device.type):
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).float()
speech_latents = self.to_speech_latent(speech_latents).float()
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
@register_model
def register_mel_text_clip(opt_net, opt):
return MelTextCLIP(**opt_get(opt_net, ['kwargs'], {}))
if __name__ == '__main__':
clip = MelTextCLIP(text_mask_percentage=.2, voice_mask_percentage=.2)
clip(torch.randint(0,256,(2,120)),
torch.tensor([50,100]),
torch.randn(2,80,400),
torch.tensor([10100,10200]),
return_loss=True)