text<->cond clip

I need that universal clip..

codes/models/gpt_voice/text_cond_clip.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from torch import einsum
+
+from models.gpt_voice.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)