forked from mrq/DL-Art-School
112 lines
3.9 KiB
Python
112 lines
3.9 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.gpt_voice.unified_voice2 import ConditioningEncoder
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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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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 VoiceCondCLIP(nn.Module):
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"""
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CLIP model retrofitted for performing contrastive evaluation between tokenized audio data and an encoded conditioning
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clip.
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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_speech=512,
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dim_latent=512,
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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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voice_mask_percentage=0,
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wav_token_compression=1024,
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):
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super().__init__()
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self.cond_encoder = ConditioningEncoder(80, dim_latent, do_checkpointing=True)
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self.speech_emb = nn.Embedding(num_speech_tokens, dim_speech)
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self.speech_pos_emb = nn.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 = nn.Linear(dim_speech, dim_latent, bias=False)
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self.temperature = nn.Parameter(torch.tensor(1.))
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self.voice_mask_percentage = voice_mask_percentage
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self.wav_token_compression = wav_token_compression
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def forward(
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self,
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cond_mel,
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speech_tokens,
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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 the speech tokens. Ameliorate this by
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# chopping the inputs by the maximum actual length.
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max_mel_len = wav_lengths.max() // self.wav_token_compression
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speech_tokens = speech_tokens[:, :max_mel_len]
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b, device = speech_tokens.shape[0], speech_tokens.device
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if self.training:
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voice_mask = torch.rand_like(speech_tokens.float()) > self.voice_mask_percentage
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else:
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voice_mask = torch.ones_like(speech_tokens.float()).bool()
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speech_emb = self.speech_emb(speech_tokens)
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speech_emb += self.speech_pos_emb(torch.arange(speech_emb.shape[1], device=device))
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cond_latents = self.cond_encoder(cond_mel)
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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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speech_latents = self.to_speech_latent(speech_latents)
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cond_latents, speech_latents = map(lambda t: F.normalize(t, p=2, dim=-1), (cond_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', cond_latents, speech_latents) * temp
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return sim
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sim = einsum('i d, j d -> i j', cond_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_voice_cond_clip(opt_net, opt):
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return VoiceCondCLIP(**opt_get(opt_net, ['kwargs'], {}))
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if __name__ == '__main__':
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clip = VoiceCondCLIP(voice_mask_percentage=.2)
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clip(torch.randn(2,80,400),
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torch.randint(0,8192,(2,250)),
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torch.tensor([101,102]),
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return_loss=True)
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nonloss = clip(
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torch.randn(2, 80, 400),
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torch.randint(0,8192,(2,250)),
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torch.tensor([101,102]),
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return_loss=False)
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print(nonloss.shape) |