Update autoregressive to support type inputs

models/autoregressive.py

@@ -278,9 +278,10 @@ class MelEncoder(nn.Module):
 class UnifiedVoice(nn.Module):
     def __init__(self, layers=8, model_dim=512, heads=8, max_text_tokens=120, max_mel_tokens=250, max_conditioning_inputs=1,
                  mel_length_compression=1024, number_text_tokens=256,
-                 start_text_token=255, stop_text_token=0, number_mel_codes=8194, start_mel_token=8192,
+                 start_text_token=None, number_mel_codes=8194, start_mel_token=8192,
                  stop_mel_token=8193, train_solo_embeddings=False, use_mel_codes_as_input=True,
-                 checkpointing=True, average_conditioning_embeddings=False):
+                 checkpointing=True, average_conditioning_embeddings=False,
+                 types=1):
         """
         Args:
             layers: Number of layers in transformer stack.
@@ -304,8 +305,8 @@ class UnifiedVoice(nn.Module):
         super().__init__()
 
         self.number_text_tokens = number_text_tokens
-        self.start_text_token = start_text_token
-        self.stop_text_token = stop_text_token
+        self.start_text_token = number_text_tokens * types if start_text_token is None else start_text_token
+        self.stop_text_token = 0
         self.number_mel_codes = number_mel_codes
         self.start_mel_token = start_mel_token
         self.stop_mel_token = stop_mel_token
@@ -318,7 +319,7 @@ class UnifiedVoice(nn.Module):
         self.mel_length_compression = mel_length_compression
         self.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=heads)
         self.average_conditioning_embeddings = average_conditioning_embeddings
-        self.text_embedding = nn.Embedding(self.number_text_tokens, model_dim)
+        self.text_embedding = nn.Embedding(self.number_text_tokens*types+1, model_dim)
         if use_mel_codes_as_input:
             self.mel_embedding = nn.Embedding(self.number_mel_codes, model_dim)
         else:
@@ -333,7 +334,7 @@ class UnifiedVoice(nn.Module):
             self.text_solo_embedding = 0
 
         self.final_norm = nn.LayerNorm(model_dim)
-        self.text_head = nn.Linear(model_dim, self.number_text_tokens)
+        self.text_head = nn.Linear(model_dim, self.number_text_tokens*types+1)
         self.mel_head = nn.Linear(model_dim, self.number_mel_codes)
 
         # Initialize the embeddings per the GPT-2 scheme
@@ -389,7 +390,7 @@ class UnifiedVoice(nn.Module):
         else:
             return first_logits
 
-    def forward(self, speech_conditioning_input, text_inputs, text_lengths, mel_codes, wav_lengths, text_first=True, raw_mels=None, return_attentions=False,
+    def forward(self, speech_conditioning_input, text_inputs, text_lengths, mel_codes, wav_lengths, types=None, text_first=True, raw_mels=None, return_attentions=False,
                 return_latent=False, clip_inputs=True):
         """
         Forward pass that uses both text and voice in either text conditioning mode or voice conditioning mode
@@ -406,6 +407,10 @@ class UnifiedVoice(nn.Module):
         If return_latent is specified, loss & logits are not computed or returned. Only the predicted latents are returned.
         If clip_inputs is True, the inputs will be clipped to the smallest input size across each input modality.
         """
+        # Types are expressed by expanding the text embedding space.
+        if types is not None:
+            text_inputs = text_inputs * (1+types).unsqueeze(-1)
+			
         if clip_inputs:
             # This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
             # chopping the inputs by the maximum actual length.