154 lines
6.0 KiB
Python
154 lines
6.0 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from einops import rearrange
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from torch import einsum
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from models.lucidrains.dalle.transformer import Transformer
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from trainer.networks import register_model
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from utils.util import opt_get
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import torch_intermediary as ml
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def exists(val):
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return val is not None
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def masked_mean(t, mask, dim = 1):
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t = t.masked_fill(~mask[:, :, None], 0.)
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return t.sum(dim = 1) / mask.sum(dim = 1)[..., None]
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class MelTextCLIP(nn.Module):
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"""
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CLIP model retrofitted for performing contrastive evaluation between MEL data and the corresponding
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transcribed text.
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Originally from https://github.com/lucidrains/DALLE-pytorch/blob/main/dalle_pytorch/dalle_pytorch.py
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"""
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def __init__(
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self,
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*,
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dim_text=512,
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dim_speech=512,
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dim_latent=512,
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num_text_tokens=256,
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text_enc_depth=6,
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text_seq_len=120,
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text_heads=8,
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num_speech_tokens=8192,
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speech_enc_depth=6,
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speech_heads=8,
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speech_seq_len=250,
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text_mask_percentage=0,
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voice_mask_percentage=0,
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mel_compression=256,
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):
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super().__init__()
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# nn.Embedding
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self.text_emb = ml.Embedding(num_text_tokens, dim_text)
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# nn.Embedding
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self.text_pos_emb = ml.Embedding(text_seq_len, dim_text)
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self.text_transformer = Transformer(causal=False, seq_len=text_seq_len, dim=dim_text, depth=text_enc_depth,
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heads=text_heads, rotary_emb=False)
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self.to_text_latent = ml.Linear(dim_text, dim_latent, bias=False)
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self.speech_enc = nn.Conv1d(80, dim_speech, kernel_size=3, padding=1)
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# nn.Embedding
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self.speech_pos_emb = ml.Embedding(num_speech_tokens, dim_speech)
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self.speech_transformer = Transformer(causal=False, seq_len=speech_seq_len, dim=dim_speech,
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depth=speech_enc_depth, heads=speech_heads, rotary_emb=False)
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self.to_speech_latent = ml.Linear(dim_speech, dim_latent, bias=False)
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self.temperature = nn.Parameter(torch.tensor(1.))
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self.text_mask_percentage = text_mask_percentage
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self.voice_mask_percentage = voice_mask_percentage
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self.mel_compression = mel_compression
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def get_text_projections(self, text, text_mask=None):
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if text_mask is None:
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text_mask = torch.ones_like(text.float()).bool()
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text_emb = self.text_emb(text)
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text_emb += self.text_pos_emb(torch.arange(text.shape[1], device=text.device))
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with torch.autocast(text.device.type):
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enc_text = self.text_transformer(text_emb, mask=text_mask)
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text_latents = masked_mean(enc_text, text_mask, dim=1)
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return self.to_text_latent(text_latents).float()
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def get_speech_projection(self, mel, voice_mask=None):
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if voice_mask is None:
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voice_mask = torch.ones_like(mel[:,0,:].float()).bool()
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speech_emb = self.speech_enc(mel).permute(0,2,1)
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speech_emb += self.speech_pos_emb(torch.arange(speech_emb.shape[1], device=mel.device))
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with torch.autocast(speech_emb.device.type):
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enc_speech = self.speech_transformer(speech_emb, mask=voice_mask)
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speech_latents = masked_mean(enc_speech, voice_mask, dim=1)
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return self.to_speech_latent(speech_latents).float()
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def forward(
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self,
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text,
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text_lengths,
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mel,
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wav_lengths,
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return_loss=False
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):
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# This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
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# chopping the inputs by the maximum actual length.
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max_text_len = text_lengths.max()
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text = text[:, :max_text_len]
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max_mel_len = wav_lengths.max() // self.mel_compression
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mel = mel[:, :, :max_mel_len]
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b, device = text.shape[0], text.device
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if self.training:
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text_mask = torch.rand_like(text.float()) > self.text_mask_percentage
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voice_mask = torch.rand_like(mel[:,0,:].float()) > self.voice_mask_percentage
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else:
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text_mask = torch.ones_like(text.float()).bool()
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voice_mask = torch.ones_like(mel[:,0,:].float()).bool()
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text_emb = self.text_emb(text)
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text_emb += self.text_pos_emb(torch.arange(text.shape[1], device=device))
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speech_emb = self.speech_enc(mel).permute(0,2,1)
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speech_emb += self.speech_pos_emb(torch.arange(speech_emb.shape[1], device=device))
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# Only autocast the transformer part. The MEL encoder loses accuracy if you autcast it.
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with torch.autocast(speech_emb.device.type):
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enc_text = self.text_transformer(text_emb, mask=text_mask)
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enc_speech = self.speech_transformer(speech_emb, mask=voice_mask)
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text_latents = masked_mean(enc_text, text_mask, dim=1)
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speech_latents = masked_mean(enc_speech, voice_mask, dim=1)
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text_latents = self.to_text_latent(text_latents).float()
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speech_latents = self.to_speech_latent(speech_latents).float()
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text_latents, speech_latents = map(lambda t: F.normalize(t, p=2, dim=-1), (text_latents, speech_latents))
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temp = self.temperature.exp()
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if not return_loss:
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sim = einsum('n d, n d -> n', text_latents, speech_latents) * temp
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return sim
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sim = einsum('i d, j d -> i j', text_latents, speech_latents) * temp
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labels = torch.arange(b, device=device)
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loss = (F.cross_entropy(sim, labels) + F.cross_entropy(sim.t(), labels)) / 2
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return loss
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@register_model
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def register_mel_text_clip(opt_net, opt):
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return MelTextCLIP(**opt_get(opt_net, ['kwargs'], {}))
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if __name__ == '__main__':
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clip = MelTextCLIP(text_mask_percentage=.2, voice_mask_percentage=.2)
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clip(torch.randint(0,256,(2,120)),
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torch.tensor([50,100]),
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torch.randn(2,80,400),
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torch.tensor([10100,10200]),
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return_loss=True) |