[to #42322933]Add lock to tts's AM and model loaded since its AR part not lockfree

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10464620
        Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10464620

modelscope/models/audio/tts/voice.py

@@ -2,6 +2,7 @@
 
 import os
 import pickle as pkl
+from threading import Lock
 
 import json
 import numpy as np
@@ -27,6 +28,7 @@ class Voice:
         self.__am_config = AttrDict(**am_config)
         self.__voc_config = AttrDict(**voc_config)
         self.__model_loaded = False
+        self.__lock = Lock()
         if 'am' not in self.__am_config:
             raise TtsModelConfigurationException(
                 'modelscope error: am configuration invalid')
@@ -71,34 +73,35 @@ class Voice:
         self.__generator.remove_weight_norm()
 
     def __am_forward(self, symbol_seq):
-        with torch.no_grad():
-            inputs_feat_lst = self.__ling_unit.encode_symbol_sequence(
-                symbol_seq)
-            inputs_sy = torch.from_numpy(inputs_feat_lst[0]).long().to(
-                self.__device)
-            inputs_tone = torch.from_numpy(inputs_feat_lst[1]).long().to(
-                self.__device)
-            inputs_syllable = torch.from_numpy(inputs_feat_lst[2]).long().to(
-                self.__device)
-            inputs_ws = torch.from_numpy(inputs_feat_lst[3]).long().to(
-                self.__device)
-            inputs_ling = torch.stack(
-                [inputs_sy, inputs_tone, inputs_syllable, inputs_ws],
-                dim=-1).unsqueeze(0)
-            inputs_emo = torch.from_numpy(inputs_feat_lst[4]).long().to(
-                self.__device).unsqueeze(0)
-            inputs_spk = torch.from_numpy(inputs_feat_lst[5]).long().to(
-                self.__device).unsqueeze(0)
-            inputs_len = torch.zeros(1).to(self.__device).long(
-            ) + inputs_emo.size(1) - 1  # minus 1 for "~"
-            res = self.__am_net(inputs_ling[:, :-1, :], inputs_emo[:, :-1],
-                                inputs_spk[:, :-1], inputs_len)
-            postnet_outputs = res['postnet_outputs']
-            LR_length_rounded = res['LR_length_rounded']
-            valid_length = int(LR_length_rounded[0].item())
-            postnet_outputs = postnet_outputs[
-                0, :valid_length, :].cpu().numpy()
-            return postnet_outputs
+        with self.__lock:
+            with torch.no_grad():
+                inputs_feat_lst = self.__ling_unit.encode_symbol_sequence(
+                    symbol_seq)
+                inputs_sy = torch.from_numpy(inputs_feat_lst[0]).long().to(
+                    self.__device)
+                inputs_tone = torch.from_numpy(inputs_feat_lst[1]).long().to(
+                    self.__device)
+                inputs_syllable = torch.from_numpy(
+                    inputs_feat_lst[2]).long().to(self.__device)
+                inputs_ws = torch.from_numpy(inputs_feat_lst[3]).long().to(
+                    self.__device)
+                inputs_ling = torch.stack(
+                    [inputs_sy, inputs_tone, inputs_syllable, inputs_ws],
+                    dim=-1).unsqueeze(0)
+                inputs_emo = torch.from_numpy(inputs_feat_lst[4]).long().to(
+                    self.__device).unsqueeze(0)
+                inputs_spk = torch.from_numpy(inputs_feat_lst[5]).long().to(
+                    self.__device).unsqueeze(0)
+                inputs_len = torch.zeros(1).to(self.__device).long(
+                ) + inputs_emo.size(1) - 1  # minus 1 for "~"
+                res = self.__am_net(inputs_ling[:, :-1, :], inputs_emo[:, :-1],
+                                    inputs_spk[:, :-1], inputs_len)
+                postnet_outputs = res['postnet_outputs']
+                LR_length_rounded = res['LR_length_rounded']
+                valid_length = int(LR_length_rounded[0].item())
+                postnet_outputs = postnet_outputs[
+                    0, :valid_length, :].cpu().numpy()
+                return postnet_outputs
 
     def __vocoder_forward(self, melspec):
         dim0 = list(melspec.shape)[-1]
@@ -118,14 +121,15 @@ class Voice:
             return audio
 
     def forward(self, symbol_seq):
-        if not self.__model_loaded:
-            torch.manual_seed(self.__am_config.seed)
-            if torch.cuda.is_available():
+        with self.__lock:
+            if not self.__model_loaded:
                 torch.manual_seed(self.__am_config.seed)
-                self.__device = torch.device('cuda')
-            else:
-                self.__device = torch.device('cpu')
-            self.__load_am()
-            self.__load_vocoder()
-            self.__model_loaded = True
+                if torch.cuda.is_available():
+                    torch.manual_seed(self.__am_config.seed)
+                    self.__device = torch.device('cuda')
+                else:
+                    self.__device = torch.device('cpu')
+                self.__load_am()
+                self.__load_vocoder()
+                self.__model_loaded = True
         return self.__vocoder_forward(self.__am_forward(symbol_seq))