refactor(xtts): use tortoise conditioning encoder

TTS/tts/layers/tortoise/autoregressive.py

@@ -176,7 +176,6 @@ class ConditioningEncoder(nn.Module):
         embedding_dim,
         attn_blocks=6,
         num_attn_heads=4,
-        mean=False,
     ):
         super().__init__()
         attn = []
@@ -185,15 +184,14 @@ class ConditioningEncoder(nn.Module):
             attn.append(AttentionBlock(embedding_dim, num_attn_heads))
         self.attn = nn.Sequential(*attn)
         self.dim = embedding_dim
-        self.mean = mean
 
     def forward(self, x):
+        """
+        x: (b, 80, s)
+        """
         h = self.init(x)
         h = self.attn(h)
-        if self.mean:
+        return h
-            return h.mean(dim=2)
-        else:
-            return h[:, :, 0]
 
 
 class LearnedPositionEmbeddings(nn.Module):
@@ -473,7 +471,7 @@ class UnifiedVoice(nn.Module):
         )
         conds = []
         for j in range(speech_conditioning_input.shape[1]):
-            conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
+            conds.append(self.conditioning_encoder(speech_conditioning_input[:, j])[:, :, 0])
         conds = torch.stack(conds, dim=1)
         conds = conds.mean(dim=1)
         return conds

TTS/tts/layers/xtts/gpt.py

@@ -8,12 +8,12 @@ import torch.nn.functional as F
 from transformers import GPT2Config
 
 from TTS.tts.layers.tortoise.autoregressive import (
+    ConditioningEncoder,
     LearnedPositionEmbeddings,
     _prepare_attention_mask_for_generation,
     build_hf_gpt_transformer,
 )
 from TTS.tts.layers.xtts.gpt_inference import GPT2InferenceModel
-from TTS.tts.layers.xtts.latent_encoder import ConditioningEncoder
 from TTS.tts.layers.xtts.perceiver_encoder import PerceiverResampler
 
 
@@ -235,19 +235,6 @@ class GPT(nn.Module):
         else:
             return first_logits
 
-    def get_conditioning(self, speech_conditioning_input):
-        speech_conditioning_input = (
-            speech_conditioning_input.unsqueeze(1)
-            if len(speech_conditioning_input.shape) == 3
-            else speech_conditioning_input
-        )
-        conds = []
-        for j in range(speech_conditioning_input.shape[1]):
-            conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
-        conds = torch.stack(conds, dim=1)
-        conds = conds.mean(dim=1)
-        return conds
-
     def get_prompts(self, prompt_codes):
         """
         Create a prompt from the mel codes. This is used to condition the model on the mel codes.
@@ -286,6 +273,7 @@ class GPT(nn.Module):
         """
         cond_input: (b, 80, s) or (b, 1, 80, s)
         conds: (b, 1024, s)
+        output: (b, 1024, 32)
         """
         conds = None
         if not return_latent:

TTS/tts/layers/xtts/latent_encoder.py

@@ -93,28 +93,3 @@ class AttentionBlock(nn.Module):
         h = self.proj_out(h)
         xp = self.x_proj(x)
         return (xp + h).reshape(b, xp.shape[1], *spatial)
-
-
-class ConditioningEncoder(nn.Module):
-    def __init__(
-        self,
-        spec_dim,
-        embedding_dim,
-        attn_blocks=6,
-        num_attn_heads=4,
-    ):
-        super().__init__()
-        attn = []
-        self.init = nn.Conv1d(spec_dim, embedding_dim, kernel_size=1)
-        for a in range(attn_blocks):
-            attn.append(AttentionBlock(embedding_dim, num_attn_heads))
-        self.attn = nn.Sequential(*attn)
-        self.dim = embedding_dim
-
-    def forward(self, x):
-        """
-        x: (b, 80, s)
-        """
-        h = self.init(x)
-        h = self.attn(h)
-        return h

TTS/tts/models/xtts.py

@@ -93,25 +93,6 @@ def load_audio(audiopath, sampling_rate):
     return audio
 
 
-def pad_or_truncate(t, length):
-    """
-    Ensure a given tensor t has a specified sequence length by either padding it with zeros or clipping it.
-
-    Args:
-        t (torch.Tensor): The input tensor to be padded or truncated.
-        length (int): The desired length of the tensor.
-
-    Returns:
-        torch.Tensor: The padded or truncated tensor.
-    """
-    tp = t[..., :length]
-    if t.shape[-1] == length:
-        tp = t
-    elif t.shape[-1] < length:
-        tp = F.pad(t, (0, length - t.shape[-1]))
-    return tp
-
-
 @dataclass
 class XttsAudioConfig(Coqpit):
     """