force set use_flow_cache

Author: aluminumbox

Diff for: cosyvoice/cli/model.py

@@ -401,17 +401,17 @@ def tts(self, text, flow_embedding, llm_embedding=torch.zeros(0, 192),
             prompt_speech_feat=torch.zeros(1, 0, 80), stream=False, speed=1.0, **kwargs):
         # this_uuid is used to track variables related to this inference thread
         this_uuid = str(uuid.uuid1())
-        # NOTE in cache mode, trim flow_prompt to same size as flow_decoder_required_cache_size
-        if self.use_flow_cache is True:
-            flow_prompt_speech_token = flow_prompt_speech_token[:, -self.flow_decoder_required_cache_size:]
-            prompt_speech_feat = prompt_speech_feat[:, -self.flow_decoder_required_cache_size * 2:]
         with self.lock:
             self.tts_speech_token_dict[this_uuid], self.llm_end_dict[this_uuid] = [], False
             self.hift_cache_dict[this_uuid] = None
             self.flow_cache_dict[this_uuid] = self.init_flow_cache()
         p = threading.Thread(target=self.llm_job, args=(text, prompt_text, llm_prompt_speech_token, llm_embedding, this_uuid))
         p.start()
         if stream is True:
+            assert self.use_flow_cache is True, "set use_flow_cache=True if you want to use stream inference to avoid OOM"
+            # NOTE in cache mode, trim flow_prompt to same size as flow_decoder_required_cache_size
+            flow_prompt_speech_token = flow_prompt_speech_token[:, -self.flow_decoder_required_cache_size:]
+            prompt_speech_feat = prompt_speech_feat[:, -self.flow_decoder_required_cache_size * 2:]
             while True:
                 time.sleep(0.1)
                 if len(self.tts_speech_token_dict[this_uuid]) >= self.token_hop_len + self.flow.pre_lookahead_len:
@@ -442,6 +442,7 @@ def tts(self, text, flow_embedding, llm_embedding=torch.zeros(0, 192),
             yield {'tts_speech': this_tts_speech.cpu()}
         else:
             # deal with all tokens
+            assert self.use_flow_cache is False, "set use_flow_cache=False for nonstream inference"
             p.join()
             this_tts_speech_token = torch.tensor(self.tts_speech_token_dict[this_uuid]).unsqueeze(dim=0)
             this_tts_speech = self.token2wav(token=this_tts_speech_token,