Enhance CosyVoice with CUDA stream management and estimator handling

- Introduced a queue-based system for managing CUDA streams to improve inference performance.
- Updated inference methods to utilize CUDA streams for asynchronous processing.
- Added an EstimatorWrapper class to manage TensorRT estimators, allowing for efficient execution context handling.
- Modified model loading functions to support estimator count configuration.
- Improved logging and performance tracking during inference operations.

cosyvoice/cli/cosyvoice.py

@@ -22,7 +22,7 @@ from cosyvoice.cli.frontend import CosyVoiceFrontEnd
 from cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model, VllmCosyVoice2Model
 from cosyvoice.utils.file_utils import logging
 from cosyvoice.utils.class_utils import get_model_type
-
+import queue
 
 class CosyVoice:
 
@@ -54,11 +54,18 @@ class CosyVoice:
                                 '{}/llm.llm.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
                                 '{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'))
         if load_trt:
+            self.estimator_count = configs['flow']['decoder']['estimator'].get('estimator_count', 1)
             self.model.load_trt('{}/flow.decoder.estimator.{}.mygpu.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
                                 '{}/flow.decoder.estimator.fp32.onnx'.format(model_dir),
-                                self.fp16)
+                                self.fp16, self.estimator_count)
         del configs
 
+        thread_count = 10
+        self.stream_pool = queue.Queue(maxsize=thread_count)
+        for _ in range(thread_count):
+            self.stream_pool.put(torch.cuda.Stream(self.device))
+
+
     def list_available_spks(self):
         spks = list(self.frontend.spk2info.keys())
         return spks
@@ -67,80 +74,104 @@ class CosyVoice:
         self.frontend.add_spk_info(spk_id, spk_info)
 
     def inference_sft(self, tts_text, spk_id, stream=False, speed=1.0, text_frontend=True):
-        for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
-            model_input = self.frontend.frontend_sft(i, spk_id)
-            start_time = time.time()
-            logging.info('synthesis text {}'.format(i))
-            for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
-                speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
-                logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
-                yield model_output
+        cuda_stream = self.stream_pool.get()
+        with torch.cuda.stream(cuda_stream):
+            for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
+                model_input = self.frontend.frontend_sft(i, spk_id)
                 start_time = time.time()
+                logging.info('synthesis text {}'.format(i))
+                for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+                    speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
+                    logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
+                    yield model_output
+                    start_time = time.time()
+        cuda_stream.synchronize()
+        self.stream_pool.put(cuda_stream)
 
     def inference_zero_shot(self, tts_text, prompt_text, prompt_speech_16k, stream=False, speed=1.0, text_frontend=True):
-        prompt_text = self.frontend.text_normalize(prompt_text, split=False, text_frontend=text_frontend)
-        for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
-            if (not isinstance(i, Generator)) and len(i) < 0.5 * len(prompt_text):
-                logging.warning('synthesis text {} too short than prompt text {}, this may lead to bad performance'.format(i, prompt_text))
-            model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_speech_16k, self.sample_rate)
-            start_time = time.time()
-            logging.info('synthesis text {}'.format(i))
-            for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
-                speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
-                logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
-                yield model_output
+        cuda_stream = self.stream_pool.get()
+        with torch.cuda.stream(cuda_stream):
+            prompt_text = self.frontend.text_normalize(prompt_text, split=False, text_frontend=text_frontend)
+            for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
+                if (not isinstance(i, Generator)) and len(i) < 0.5 * len(prompt_text):
+                    logging.warning('synthesis text {} too short than prompt text {}, this may lead to bad performance'.format(i, prompt_text))
+                model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_speech_16k, self.sample_rate)
                 start_time = time.time()
+                logging.info('synthesis text {}'.format(i))
+                for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+                    speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
+                    logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
+                    yield model_output
+                    start_time = time.time()
+        cuda_stream.synchronize()
+        self.stream_pool.put(cuda_stream)
 
     def inference_zero_shot_by_spk_id(self, tts_text, spk_id, stream=False, speed=1.0, text_frontend=True):
         """使用预定义的说话人执行 zero_shot 推理"""
-        for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
-            model_input = self.frontend.frontend_zero_shot_by_spk_id(i, spk_id)
-            start_time = time.time()
-            last_time = start_time
-            chunk_index = 0
-            logging.info('synthesis text {}'.format(i))
-            for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
-                speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
-                logging.info('yield speech index:{}, len {:.2f}, rtf {:.3f},  cost {:.3f}s,  all cost time {:.3f}s'.format(
-                    chunk_index, speech_len,  (time.time()-last_time)/speech_len, time.time()-last_time, time.time()-start_time))
-                yield model_output
-                last_time = time.time()
-                chunk_index += 1
+        cuda_stream = self.stream_pool.get()
+        with torch.cuda.stream(cuda_stream):
+            for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
+                model_input = self.frontend.frontend_zero_shot_by_spk_id(i, spk_id)
+                start_time = time.time()
+                last_time = start_time
+                chunk_index = 0
+                logging.info('synthesis text {}'.format(i))
+                for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+                    speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
+                    logging.info('yield speech index:{}, len {:.2f}, rtf {:.3f},  cost {:.3f}s,  all cost time {:.3f}s'.format(
+                        chunk_index, speech_len,  (time.time()-last_time)/speech_len, time.time()-last_time, time.time()-start_time))
+                    yield model_output
+                    last_time = time.time()
+                    chunk_index += 1
+        cuda_stream.synchronize()
+        self.stream_pool.put(cuda_stream)
 
     def inference_cross_lingual(self, tts_text, prompt_speech_16k, stream=False, speed=1.0, text_frontend=True):
-        for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
-            model_input = self.frontend.frontend_cross_lingual(i, prompt_speech_16k, self.sample_rate)
-            start_time = time.time()
-            logging.info('synthesis text {}'.format(i))
-            for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
-                speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
-                logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
-                yield model_output
+        cuda_stream = self.stream_pool.get()
+        with torch.cuda.stream(cuda_stream):
+            for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
+                model_input = self.frontend.frontend_cross_lingual(i, prompt_speech_16k, self.sample_rate)
                 start_time = time.time()
+                logging.info('synthesis text {}'.format(i))
+                for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+                    speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
+                        logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
+                        yield model_output
+                        start_time = time.time()
+        cuda_stream.synchronize()
+        self.stream_pool.put(cuda_stream)
 
     def inference_instruct(self, tts_text, spk_id, instruct_text, stream=False, speed=1.0, text_frontend=True):
-        assert isinstance(self.model, CosyVoiceModel), 'inference_instruct is only implemented for CosyVoice!'
-        if self.instruct is False:
-            raise ValueError('{} do not support instruct inference'.format(self.model_dir))
-        instruct_text = self.frontend.text_normalize(instruct_text, split=False, text_frontend=text_frontend)
-        for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
-            model_input = self.frontend.frontend_instruct(i, spk_id, instruct_text)
+        cuda_stream = self.stream_pool.get()
+        with torch.cuda.stream(cuda_stream):
+            assert isinstance(self.model, CosyVoiceModel), 'inference_instruct is only implemented for CosyVoice!'
+            if self.instruct is False:
+                raise ValueError('{} do not support instruct inference'.format(self.model_dir))
+            instruct_text = self.frontend.text_normalize(instruct_text, split=False, text_frontend=text_frontend)
+            for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
+                model_input = self.frontend.frontend_instruct(i, spk_id, instruct_text)
+                start_time = time.time()
+                logging.info('synthesis text {}'.format(i))
+                for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+                    speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
+                    logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
+                    yield model_output
+                    start_time = time.time()
+        cuda_stream.synchronize()
+        self.stream_pool.put(cuda_stream)
+
+    def inference_vc(self, source_speech_16k, prompt_speech_16k, stream=False, speed=1.0):
+        cuda_stream = self.stream_pool.get()
+        with torch.cuda.stream(cuda_stream):
+            model_input = self.frontend.frontend_vc(source_speech_16k, prompt_speech_16k, self.sample_rate)
             start_time = time.time()
-            logging.info('synthesis text {}'.format(i))
-            for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+            for model_output in self.model.vc(**model_input, stream=stream, speed=speed):
                 speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
                 logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
                 yield model_output
                 start_time = time.time()
-
-    def inference_vc(self, source_speech_16k, prompt_speech_16k, stream=False, speed=1.0):
-        model_input = self.frontend.frontend_vc(source_speech_16k, prompt_speech_16k, self.sample_rate)
-        start_time = time.time()
-        for model_output in self.model.vc(**model_input, stream=stream, speed=speed):
-            speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
-            logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
-            yield model_output
-            start_time = time.time()
+        cuda_stream.synchronize()
+        self.stream_pool.put(cuda_stream)
 
 
 class CosyVoice2(CosyVoice):
@@ -178,33 +209,48 @@ class CosyVoice2(CosyVoice):
         if load_jit:
             self.model.load_jit('{}/flow.encoder.{}.zip'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'))
         if load_trt:
+            self.estimator_count = configs['flow']['decoder']['estimator'].get('estimator_count', 1)
             self.model.load_trt('{}/flow.decoder.estimator.{}.mygpu.plan'.format(model_dir, 'fp16' if self.fp16 is True else 'fp32'),
                                 '{}/flow.decoder.estimator.fp32.onnx'.format(model_dir),
-                                self.fp16)
+                                self.fp16, self.estimator_count)
         del configs
 
+        thread_count = 10
+        self.stream_pool = queue.Queue(maxsize=thread_count)
+        for _ in range(thread_count):
+            self.stream_pool.put(torch.cuda.Stream(self.device))
+
     def inference_instruct(self, *args, **kwargs):
         raise NotImplementedError('inference_instruct is not implemented for CosyVoice2!')
 
     def inference_instruct2(self, tts_text, instruct_text, prompt_speech_16k, stream=False, speed=1.0, text_frontend=True):
-        assert isinstance(self.model, CosyVoice2Model), 'inference_instruct2 is only implemented for CosyVoice2!'
-        for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
-            model_input = self.frontend.frontend_instruct2(i, instruct_text, prompt_speech_16k, self.sample_rate)
-            start_time = time.time()
-            logging.info('synthesis text {}'.format(i))
-            for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
-                speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
-                logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
-                yield model_output
+        cuda_stream = self.stream_pool.get()
+        with torch.cuda.stream(cuda_stream):
+            assert isinstance(self.model, CosyVoice2Model), 'inference_instruct2 is only implemented for CosyVoice2!'
+            for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
+                model_input = self.frontend.frontend_instruct2(i, instruct_text, prompt_speech_16k, self.sample_rate)
                 start_time = time.time()
+                logging.info('synthesis text {}'.format(i))
+                for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+                    speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
+                    logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
+                    yield model_output
+                    start_time = time.time()
+        cuda_stream.synchronize()
+        self.stream_pool.put(cuda_stream)
 
     def inference_instruct2_by_spk_id(self, tts_text, instruct_text, spk_id, stream=False, speed=1.0, text_frontend=True):
-        for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
-            model_input = self.frontend.frontend_instruct2_by_spk_id(i, instruct_text, spk_id)
-            start_time = time.time()
-            logging.info('synthesis text {}'.format(i))
-            for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
-                speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
-                logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
-                yield model_output
+        cuda_stream = self.stream_pool.get()
+        with torch.cuda.stream(cuda_stream):
+            assert isinstance(self.model, CosyVoice2Model), 'inference_instruct2 is only implemented for CosyVoice2!'
+            for i in tqdm(self.frontend.text_normalize(tts_text, split=True, text_frontend=text_frontend)):
+                model_input = self.frontend.frontend_instruct2_by_spk_id(i, instruct_text, spk_id)
                 start_time = time.time()
+                logging.info('synthesis text {}'.format(i))
+                for model_output in self.model.tts(**model_input, stream=stream, speed=speed):
+                        speech_len = model_output['tts_speech'].shape[1] / self.sample_rate
+                        logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
+                        yield model_output
+                        start_time = time.time()
+        cuda_stream.synchronize()
+        self.stream_pool.put(cuda_stream)

cosyvoice/cli/model.py

@@ -22,7 +22,7 @@ from contextlib import nullcontext
 import uuid
 from cosyvoice.utils.common import fade_in_out
 from cosyvoice.utils.file_utils import convert_onnx_to_trt
-
+from cosyvoice.flow.flow_matching import EstimatorWrapper
 
 class CosyVoiceModel:
 
@@ -84,7 +84,7 @@ class CosyVoiceModel:
         flow_encoder = torch.jit.load(flow_encoder_model, map_location=self.device)
         self.flow.encoder = flow_encoder
 
-    def load_trt(self, flow_decoder_estimator_model, flow_decoder_onnx_model, fp16):
+    def load_trt(self, flow_decoder_estimator_model, flow_decoder_onnx_model, fp16, estimator_count=1):
         assert torch.cuda.is_available(), 'tensorrt only supports gpu!'
         if not os.path.exists(flow_decoder_estimator_model):
             convert_onnx_to_trt(flow_decoder_estimator_model, flow_decoder_onnx_model, fp16)
@@ -96,7 +96,7 @@ class CosyVoiceModel:
             self.flow.decoder.estimator_engine = trt.Runtime(trt.Logger(trt.Logger.INFO)).deserialize_cuda_engine(f.read())
         if self.flow.decoder.estimator_engine is None:
             raise ValueError('failed to load trt {}'.format(flow_decoder_estimator_model))
-        self.flow.decoder.estimator = self.flow.decoder.estimator_engine.create_execution_context()
+        self.flow.decoder.estimator = EstimatorWrapper(self.flow.decoder.estimator_engine, estimator_count=estimator_count)
 
     def llm_job(self, text, prompt_text, llm_prompt_speech_token, llm_embedding, uuid):
         with self.llm_context:
@@ -122,13 +122,13 @@ class CosyVoiceModel:
 
     def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, finalize=False, speed=1.0):
         tts_mel, flow_cache = self.flow.inference(token=token.to(self.device),
-                                                  token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
-                                                  prompt_token=prompt_token.to(self.device),
-                                                  prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
-                                                  prompt_feat=prompt_feat.to(self.device),
-                                                  prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
-                                                  embedding=embedding.to(self.device),
-                                                  flow_cache=self.flow_cache_dict[uuid])
+                                                token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
+                                                prompt_token=prompt_token.to(self.device),
+                                                prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
+                                                prompt_feat=prompt_feat.to(self.device),
+                                                prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
+                                                embedding=embedding.to(self.device),
+                                                flow_cache=self.flow_cache_dict[uuid])
         self.flow_cache_dict[uuid] = flow_cache
 
         # mel overlap fade in out
@@ -148,8 +148,8 @@ class CosyVoiceModel:
             if self.hift_cache_dict[uuid] is not None:
                 tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
             self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:],
-                                          'source': tts_source[:, :, -self.source_cache_len:],
-                                          'speech': tts_speech[:, -self.source_cache_len:]}
+                                        'source': tts_source[:, :, -self.source_cache_len:],
+                                        'speech': tts_speech[:, -self.source_cache_len:]}
             tts_speech = tts_speech[:, :-self.source_cache_len]
         else:
             if speed != 1.0:
@@ -319,14 +319,15 @@ class CosyVoice2Model(CosyVoiceModel):
         self.flow.encoder = flow_encoder
 
     def token2wav(self, token, prompt_token, prompt_feat, embedding, uuid, token_offset, finalize=False, speed=1.0):
+
         tts_mel, _ = self.flow.inference(token=token.to(self.device),
-                                         token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
-                                         prompt_token=prompt_token.to(self.device),
-                                         prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
-                                         prompt_feat=prompt_feat.to(self.device),
-                                         prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
-                                         embedding=embedding.to(self.device),
-                                         finalize=finalize)
+                                        token_len=torch.tensor([token.shape[1]], dtype=torch.int32).to(self.device),
+                                        prompt_token=prompt_token.to(self.device),
+                                        prompt_token_len=torch.tensor([prompt_token.shape[1]], dtype=torch.int32).to(self.device),
+                                        prompt_feat=prompt_feat.to(self.device),
+                                        prompt_feat_len=torch.tensor([prompt_feat.shape[1]], dtype=torch.int32).to(self.device),
+                                        embedding=embedding.to(self.device),
+                                        finalize=finalize)
         tts_mel = tts_mel[:, :, token_offset * self.flow.token_mel_ratio:]
         # append hift cache
         if self.hift_cache_dict[uuid] is not None:
@@ -340,8 +341,8 @@ class CosyVoice2Model(CosyVoiceModel):
             if self.hift_cache_dict[uuid] is not None:
                 tts_speech = fade_in_out(tts_speech, self.hift_cache_dict[uuid]['speech'], self.speech_window)
             self.hift_cache_dict[uuid] = {'mel': tts_mel[:, :, -self.mel_cache_len:],
-                                          'source': tts_source[:, :, -self.source_cache_len:],
-                                          'speech': tts_speech[:, -self.source_cache_len:]}
+                                        'source': tts_source[:, :, -self.source_cache_len:],
+                                        'speech': tts_speech[:, -self.source_cache_len:]}
             tts_speech = tts_speech[:, :-self.source_cache_len]
         else:
             if speed != 1.0:

cosyvoice/flow/flow_matching.py

@@ -15,7 +15,26 @@ import threading
 import torch
 import torch.nn.functional as F
 from matcha.models.components.flow_matching import BASECFM
+import queue
 
+class EstimatorWrapper:
+    def __init__(self, estimator_engine, estimator_count=2,):
+        self.estimators = queue.Queue()
+        self.estimator_engine = estimator_engine
+        for _ in range(estimator_count):
+            estimator = estimator_engine.create_execution_context()
+            if estimator is not None:
+                self.estimators.put(estimator)
+
+        if self.estimators.empty():
+            raise Exception("No available estimator")
+    
+    def acquire_estimator(self):
+        return self.estimators.get(), self.estimator_engine
+
+    def release_estimator(self, estimator):
+        self.estimators.put(estimator)
+        return 
 
 class ConditionalCFM(BASECFM):
     def __init__(self, in_channels, cfm_params, n_spks=1, spk_emb_dim=64, estimator: torch.nn.Module = None):
@@ -125,22 +144,50 @@ class ConditionalCFM(BASECFM):
         if isinstance(self.estimator, torch.nn.Module):
             return self.estimator.forward(x, mask, mu, t, spks, cond)
         else:
-            with self.lock:
-                self.estimator.set_input_shape('x', (2, 80, x.size(2)))
-                self.estimator.set_input_shape('mask', (2, 1, x.size(2)))
-                self.estimator.set_input_shape('mu', (2, 80, x.size(2)))
-                self.estimator.set_input_shape('t', (2,))
-                self.estimator.set_input_shape('spks', (2, 80))
-                self.estimator.set_input_shape('cond', (2, 80, x.size(2)))
+            if isinstance(self.estimator, EstimatorWrapper):
+                estimator, engine = self.estimator.acquire_estimator()
+
+                estimator.set_input_shape('x', (2, 80, x.size(2)))
+                estimator.set_input_shape('mask', (2, 1, x.size(2)))
+                estimator.set_input_shape('mu', (2, 80, x.size(2)))
+                estimator.set_input_shape('t', (2,))
+                estimator.set_input_shape('spks', (2, 80))
+                estimator.set_input_shape('cond', (2, 80, x.size(2)))
+
+                data_ptrs = [x.contiguous().data_ptr(),
+                                            mask.contiguous().data_ptr(),
+                                            mu.contiguous().data_ptr(),
+                                            t.contiguous().data_ptr(),
+                                            spks.contiguous().data_ptr(),
+                                            cond.contiguous().data_ptr(),
+                                            x.data_ptr()]
+
+                for idx, data_ptr in enumerate(data_ptrs):
+                    estimator.set_tensor_address(engine.get_tensor_name(idx), data_ptr)
+
                 # run trt engine
-                self.estimator.execute_v2([x.contiguous().data_ptr(),
-                                           mask.contiguous().data_ptr(),
-                                           mu.contiguous().data_ptr(),
-                                           t.contiguous().data_ptr(),
-                                           spks.contiguous().data_ptr(),
-                                           cond.contiguous().data_ptr(),
-                                           x.data_ptr()])
-            return x
+                estimator.execute_async_v3(torch.cuda.current_stream().cuda_stream)
+
+                torch.cuda.current_stream().synchronize()
+                self.estimator.release_estimator(estimator)
+                return x
+            else:
+                with self.lock:
+                    self.estimator.set_input_shape('x', (2, 80, x.size(2)))
+                    self.estimator.set_input_shape('mask', (2, 1, x.size(2)))
+                    self.estimator.set_input_shape('mu', (2, 80, x.size(2)))
+                    self.estimator.set_input_shape('t', (2,))
+                    self.estimator.set_input_shape('spks', (2, 80))
+                    self.estimator.set_input_shape('cond', (2, 80, x.size(2)))
+                    # run trt engine
+                    self.estimator.execute_v2([x.contiguous().data_ptr(),
+                                            mask.contiguous().data_ptr(),
+                                            mu.contiguous().data_ptr(),
+                                            t.contiguous().data_ptr(),
+                                            spks.contiguous().data_ptr(),
+                                            cond.contiguous().data_ptr(),
+                                            x.data_ptr()])
+                    return x
 
     def compute_loss(self, x1, mask, mu, spks=None, cond=None):
         """Computes diffusion loss