初步合并vllm支持，异步推理的通道处理还存在bug

cosyvoice/cli/cosyvoice.py

@@ -19,7 +19,7 @@ from hyperpyyaml import load_hyperpyyaml
 from modelscope import snapshot_download
 import torch
 from cosyvoice.cli.frontend import CosyVoiceFrontEnd
-from cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model
+from cosyvoice.cli.model import CosyVoiceModel, CosyVoice2Model, VllmCosyVoice2Model
 from cosyvoice.utils.file_utils import logging
 from cosyvoice.utils.class_utils import get_model_type
 
@@ -63,6 +63,9 @@ class CosyVoice:
         spks = list(self.frontend.spk2info.keys())
         return spks
 
+    def add_spk_info(self, spk_id, spk_info):
+        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)
@@ -88,6 +91,22 @@ class CosyVoice:
                 yield model_output
                 start_time = time.time()
 
+    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
+
     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)
@@ -126,7 +145,7 @@ class CosyVoice:
 
 class CosyVoice2(CosyVoice):
 
-    def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False):
+    def __init__(self, model_dir, load_jit=False, load_trt=False, fp16=False, use_vllm=False):
         self.instruct = True if '-Instruct' in model_dir else False
         self.model_dir = model_dir
         self.fp16 = fp16
@@ -145,7 +164,14 @@ class CosyVoice2(CosyVoice):
         if torch.cuda.is_available() is False and (load_jit is True or load_trt is True or fp16 is True):
             load_jit, load_trt, fp16 = False, False, False
             logging.warning('no cuda device, set load_jit/load_trt/fp16 to False')
-        self.model = CosyVoice2Model(configs['llm'], configs['flow'], configs['hift'], fp16)
+        if use_vllm:
+            try:
+                self.model = VllmCosyVoice2Model(model_dir, configs['flow'], configs['hift'], fp16)
+            except Exception as e:
+                logging.warning(f'use vllm inference failed. \n{e}')
+                raise e
+        else:
+            self.model = CosyVoice2Model(configs['llm'], configs['flow'], configs['hift'], fp16)
         self.model.load('{}/llm.pt'.format(model_dir),
                         '{}/flow.pt'.format(model_dir),
                         '{}/hift.pt'.format(model_dir))
@@ -171,3 +197,14 @@ class CosyVoice2(CosyVoice):
                 logging.info('yield speech len {}, rtf {}'.format(speech_len, (time.time() - start_time) / speech_len))
                 yield model_output
                 start_time = time.time()
+
+    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
+                start_time = time.time()

cosyvoice/cli/frontend.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from functools import partial
-from typing import Generator
+from typing import Generator, Optional
 import json
 import onnxruntime
 import torch
@@ -24,6 +24,8 @@ import torchaudio
 import os
 import re
 import inflect
+from pydantic import BaseModel, ConfigDict
+
 try:
     import ttsfrd
     use_ttsfrd = True
@@ -36,6 +38,18 @@ from cosyvoice.utils.file_utils import logging
 from cosyvoice.utils.frontend_utils import contains_chinese, replace_blank, replace_corner_mark, remove_bracket, spell_out_number, split_paragraph, is_only_punctuation
 
 
+class SpeakerInfo(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    name: Optional[str] = None
+    spk_id: str
+    prompt_text: str
+    prompt_text_token: torch.Tensor
+    speech_feat: torch.Tensor
+    speech_token: torch.Tensor
+    embedding: torch.Tensor
+
+
 class CosyVoiceFrontEnd:
 
     def __init__(self,
@@ -55,8 +69,9 @@ class CosyVoiceFrontEnd:
         self.speech_tokenizer_session = onnxruntime.InferenceSession(speech_tokenizer_model, sess_options=option,
                                                                      providers=["CUDAExecutionProvider" if torch.cuda.is_available() else
                                                                                 "CPUExecutionProvider"])
+        self.spk2info_path = spk2info
         if os.path.exists(spk2info):
-            self.spk2info = torch.load(spk2info, map_location=self.device)
+            self.spk2info = torch.load(spk2info, map_location=self.device, weights_only=False)
         else:
             self.spk2info = {}
         self.allowed_special = allowed_special
@@ -68,7 +83,8 @@ class CosyVoiceFrontEnd:
                 'failed to initialize ttsfrd resource'
             self.frd.set_lang_type('pinyinvg')
         else:
-            self.zh_tn_model = ZhNormalizer(remove_erhua=False, full_to_half=False, overwrite_cache=True)
+            # self.zh_tn_model = ZhNormalizer(remove_erhua=False, full_to_half=False, overwrite_cache=True)
+            self.zh_tn_model = ZhNormalizer(remove_erhua=False, full_to_half=False, overwrite_cache=False)
             self.en_tn_model = EnNormalizer()
             self.inflect_parser = inflect.engine()
 
@@ -86,8 +102,9 @@ class CosyVoiceFrontEnd:
     def _extract_text_token_generator(self, text_generator):
         for text in text_generator:
             text_token, _ = self._extract_text_token(text)
-            for i in range(text_token.shape[1]):
-                yield text_token[:, i: i + 1]
+            # for i in range(text_token.shape[1]):
+            #     yield text_token[:, i: i + 1]
+            yield text_token
 
     def _extract_speech_token(self, speech):
         assert speech.shape[1] / 16000 <= 30, 'do not support extract speech token for audio longer than 30s'
@@ -138,11 +155,15 @@ class CosyVoiceFrontEnd:
                 text = text.replace(" - ", "，")
                 text = remove_bracket(text)
                 text = re.sub(r'[，,、]+$', '。', text)
+                if not split:
+                    return text
                 texts = list(split_paragraph(text, partial(self.tokenizer.encode, allowed_special=self.allowed_special), "zh", token_max_n=80,
                                              token_min_n=60, merge_len=20, comma_split=False))
             else:
                 text = self.en_tn_model.normalize(text)
                 text = spell_out_number(text, self.inflect_parser)
+                if not split:
+                    return text
                 texts = list(split_paragraph(text, partial(self.tokenizer.encode, allowed_special=self.allowed_special), "en", token_max_n=80,
                                              token_min_n=60, merge_len=20, comma_split=False))
         texts = [i for i in texts if not is_only_punctuation(i)]
@@ -151,6 +172,7 @@ class CosyVoiceFrontEnd:
     def frontend_sft(self, tts_text, spk_id):
         tts_text_token, tts_text_token_len = self._extract_text_token(tts_text)
         embedding = self.spk2info[spk_id]['embedding']
+        assert embedding is not None
         model_input = {'text': tts_text_token, 'text_len': tts_text_token_len, 'llm_embedding': embedding, 'flow_embedding': embedding}
         return model_input
 
@@ -209,3 +231,60 @@ class CosyVoiceFrontEnd:
                        'prompt_speech_feat': prompt_speech_feat, 'prompt_speech_feat_len': prompt_speech_feat_len,
                        'flow_embedding': embedding}
         return model_input
+
+    def generate_spk_info(self, spk_id: str, prompt_text: str, prompt_speech_16k: torch.Tensor, resample_rate:int=24000, name: str=None):
+        assert isinstance(spk_id, str)
+        assert spk_id not in self.spk2info, "spk_id already exists"
+        prompt_text_token, _ = self._extract_text_token(prompt_text)
+        prompt_speech_resample = torchaudio.transforms.Resample(orig_freq=16000, new_freq=resample_rate)(prompt_speech_16k)
+        speech_feat, _ = self._extract_speech_feat(prompt_speech_resample)
+        speech_token, speech_token_len = self._extract_speech_token(prompt_speech_16k)
+        if resample_rate == 24000:
+            # cosyvoice2, force speech_feat % speech_token = 2
+            token_len = min(int(speech_feat.shape[1] / 2), speech_token.shape[1])
+            speech_feat = speech_feat[:, :2 * token_len]
+            speech_token = speech_token[:, :token_len]
+        embedding = self._extract_spk_embedding(prompt_speech_16k)
+        spk_info = SpeakerInfo(
+            name=name,
+            spk_id=spk_id,
+            prompt_text=prompt_text,
+            prompt_text_token=prompt_text_token,
+            speech_feat=speech_feat,
+            speech_token=speech_token,
+            embedding=embedding,
+        )
+        self.add_spk_info(spk_id, spk_info)
+
+    def add_spk_info(self, spk_id: str, spk_info: dict|SpeakerInfo):
+        if isinstance(spk_info, BaseModel):
+            spk_info = spk_info.model_dump()
+        self.spk2info[spk_id] = spk_info
+        if self.spk2info_path:
+            torch.save(self.spk2info, self.spk2info_path)
+
+    def frontend_instruct2_by_spk_id(self, tts_text, instruct_text, spk_id):
+        assert spk_id in self.spk2info
+        tts_text_token, _ = self._extract_text_token(tts_text)
+        prompt_text_token, _ = self._extract_text_token(instruct_text + '<|endofprompt|>')
+        model_input = {'text': tts_text_token,
+                       'prompt_text': prompt_text_token,
+                       'flow_prompt_speech_token': self.spk2info[spk_id]['speech_token'],
+                       'prompt_speech_feat': self.spk2info[spk_id]['speech_feat'],
+                       'llm_embedding': self.spk2info[spk_id]['embedding'],
+                       'flow_embedding': self.spk2info[spk_id]['embedding'],
+        }
+        return model_input
+
+    def frontend_zero_shot_by_spk_id(self, tts_text, spk_id):
+        assert spk_id in self.spk2info
+        tts_text_token, _ = self._extract_text_token(tts_text)
+        model_input = {'text': tts_text_token,
+                       'prompt_text': self.spk2info[spk_id]['prompt_text_token'],
+                       'llm_prompt_speech_token': self.spk2info[spk_id]['speech_token'],
+                       'flow_prompt_speech_token': self.spk2info[spk_id]['speech_token'],
+                       'prompt_speech_feat': self.spk2info[spk_id]['speech_feat'],
+                       'llm_embedding': self.spk2info[spk_id]['embedding'],
+                       'flow_embedding': self.spk2info[spk_id]['embedding']
+        }
+        return model_input

cosyvoice/cli/model.py

@@ -409,3 +409,26 @@ class CosyVoice2Model(CosyVoiceModel):
             self.tts_speech_token_dict.pop(this_uuid)
             self.llm_end_dict.pop(this_uuid)
         torch.cuda.empty_cache()
+
+
+class VllmCosyVoice2Model(CosyVoice2Model):
+    def __init__(self,
+                 model_dir: str,
+                 flow: torch.nn.Module,
+                 hift: torch.nn.Module,
+                 fp16: bool):
+        try:
+            from cosyvoice.llm.llm_vllm import VllmQwen2LM
+        except Exception as e:
+            raise e
+        llm = VllmQwen2LM(model_dir)
+        super().__init__(llm,flow,hift,fp16)
+
+    def load(self, llm_model, flow_model, hift_model):
+        self.flow.load_state_dict(torch.load(flow_model, weights_only=True, map_location=self.device), strict=True)
+        self.flow.to(self.device).eval()
+        # in case hift_model is a hifigan model
+        hift_state_dict = {k.replace('generator.', ''): v for k, v in
+                           torch.load(hift_model, weights_only=True, map_location=self.device).items()}
+        self.hift.load_state_dict(hift_state_dict, strict=True)
+        self.hift.to(self.device).eval()