add flow unified training

cosyvoice/flow/flow.py

@@ -202,6 +202,9 @@ class CausalMaskedDiffWithXvec(torch.nn.Module):
         feat_len = batch['speech_feat_len'].to(device)
         embedding = batch['embedding'].to(device)
 
+        # NOTE unified training, static_chunk_size > 0 or = 0
+        streaming = True if random.random() < 0.5 else False
+
         # xvec projection
         embedding = F.normalize(embedding, dim=1)
         embedding = self.spk_embed_affine_layer(embedding)
@@ -211,7 +214,7 @@ class CausalMaskedDiffWithXvec(torch.nn.Module):
         token = self.input_embedding(torch.clamp(token, min=0)) * mask
 
         # text encode
-        h, h_lengths = self.encoder(token, token_len)
+        h, h_lengths = self.encoder(token, token_len, streaming=streaming)
         h = self.encoder_proj(h)
 
         # get conditions
@@ -230,7 +233,8 @@ class CausalMaskedDiffWithXvec(torch.nn.Module):
             mask.unsqueeze(1),
             h.transpose(1, 2).contiguous(),
             embedding,
-            cond=conds
+            cond=conds,
+            streaming=streaming,
         )
         return {'loss': loss}