init ascend tts

ascend_910-gpt-sovits/GPT-SoVITS/GPT_SoVITS/sv.py

@@ -0,0 +1,32 @@
+import sys
+import os
+import torch
+
+sys.path.append(f"{os.getcwd()}/GPT_SoVITS/eres2net")
+sv_path = os.path.join(os.getenv("MODEL_DIR", "GPT_SoVITS/pretrained_models"), "sv/pretrained_eres2netv2w24s4ep4.ckpt")
+from ERes2NetV2 import ERes2NetV2
+import kaldi as Kaldi
+
+
+class SV:
+    def __init__(self, device, is_half):
+        pretrained_state = torch.load(sv_path, map_location="cpu", weights_only=False)
+        embedding_model = ERes2NetV2(baseWidth=24, scale=4, expansion=4)
+        embedding_model.load_state_dict(pretrained_state)
+        embedding_model.eval()
+        self.embedding_model = embedding_model
+        if is_half == False:
+            self.embedding_model = self.embedding_model.to(device)
+        else:
+            self.embedding_model = self.embedding_model.half().to(device)
+        self.is_half = is_half
+
+    def compute_embedding3(self, wav):
+        with torch.no_grad():
+            if self.is_half == True:
+                wav = wav.half()
+            feat = torch.stack(
+                [Kaldi.fbank(wav0.unsqueeze(0), num_mel_bins=80, sample_frequency=16000, dither=0) for wav0 in wav]
+            )
+            sv_emb = self.embedding_model.forward3(feat)
+        return sv_emb