VoiceCLIP model

codes/models/gpt_voice/voice_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.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 VoiceCLIP(nn.Module):
+    """
+    CLIP model retrofitted for performing contrastive evaluation between tokenized audio 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=10000,
+            text_enc_depth=6,
+            text_seq_len=200,
+            text_heads=8,
+            num_speech_tokens=8192,
+            speech_enc_depth=6,
+            speech_heads=8,
+            speech_seq_len=250,
+    ):
+        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_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.))
+
+    def forward(
+            self,
+            text,
+            speech_tokens,
+            text_mask=None,
+            return_loss=False
+    ):
+        b, device = text.shape[0], text.device
+
+        text_emb = self.text_emb(text)
+        text_emb += self.text_pos_emb(torch.arange(text.shape[1], device=device))
+
+        speech_emb = self.speech_emb(speech_tokens)
+        speech_emb += self.speech_pos_emb(torch.arange(speech_emb.shape[1], device=device))
+
+        enc_text = self.text_transformer(text_emb, mask=text_mask)
+        enc_speech = self.speech_transformer(speech_emb)
+
+        if exists(text_mask):
+            text_latents = masked_mean(enc_text, text_mask, dim=1)
+        else:
+            text_latents = enc_text.mean(dim=1)
+
+        speech_latents = enc_speech.mean(dim=1)
+
+        text_latents = self.to_text_latent(text_latents)
+        speech_latents = self.to_speech_latent(speech_latents)
+
+        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_voice_clip(opt_net, opt):
+    return VoiceCLIP(**opt_get(opt_net, ['kwargs'], {}))
+
+
+if __name__ == '__main__':
+    clip = VoiceCLIP()
+    clip(torch.randint(0,1000,(2,200)),
+         torch.randint(0,8192,(2,250)),
+         return_loss=True)