Merge pull request #735 from FunAudioLLM/dev/lyuxiang.lx

add text_frontend arg

README.md

@@ -137,6 +137,7 @@ import torchaudio
 ```python
 cosyvoice = CosyVoice2('pretrained_models/CosyVoice2-0.5B', load_jit=True, load_onnx=False, load_trt=False)
 
+# NOTE if you want to reproduce the results on https://funaudiollm.github.io/cosyvoice2, please add text_frontend=False during inference
 # zero_shot usage
 prompt_speech_16k = load_wav('zero_shot_prompt.wav', 16000)
 for i, j in enumerate(cosyvoice.inference_zero_shot('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', '希望你以后能够做的比我还好呦。', prompt_speech_16k, stream=False)):

cosyvoice/cli/cosyvoice.py

@@ -59,8 +59,8 @@ def list_avaliable_spks(self):
         spks = list(self.frontend.spk2info.keys())
         return spks
 
-    def inference_sft(self, tts_text, spk_id, stream=False, speed=1.0):
-        for i in tqdm(self.frontend.text_normalize(tts_text, split=True)):
+    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))
@@ -70,9 +70,9 @@ def inference_sft(self, tts_text, spk_id, stream=False, speed=1.0):
                 yield model_output
                 start_time = time.time()
 
-    def inference_zero_shot(self, tts_text, prompt_text, prompt_speech_16k, stream=False, speed=1.0):
-        prompt_text = self.frontend.text_normalize(prompt_text, split=False)
-        for i in tqdm(self.frontend.text_normalize(tts_text, split=True)):
+    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 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)
@@ -84,10 +84,10 @@ def inference_zero_shot(self, tts_text, prompt_text, prompt_speech_16k, stream=F
                 yield model_output
                 start_time = time.time()
 
-    def inference_cross_lingual(self, tts_text, prompt_speech_16k, stream=False, speed=1.0):
+    def inference_cross_lingual(self, tts_text, prompt_speech_16k, stream=False, speed=1.0, text_frontend=True):
         if self.frontend.instruct is True and isinstance(self.model, CosyVoiceModel):
             raise ValueError('{} do not support cross_lingual inference'.format(self.model_dir))
-        for i in tqdm(self.frontend.text_normalize(tts_text, split=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))
@@ -97,12 +97,12 @@ def inference_cross_lingual(self, tts_text, prompt_speech_16k, stream=False, spe
                 yield model_output
                 start_time = time.time()
 
-    def inference_instruct(self, tts_text, spk_id, instruct_text, stream=False, speed=1.0):
+    def inference_instruct(self, tts_text, spk_id, instruct_text, stream=False, speed=1.0, text_frontend=True):
         assert isinstance(self.model, CosyVoiceModel)
         if self.frontend.instruct is False:
             raise ValueError('{} do not support instruct inference'.format(self.model_dir))
-        instruct_text = self.frontend.text_normalize(instruct_text, split=False)
-        for i in tqdm(self.frontend.text_normalize(tts_text, split=True)):
+        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))
@@ -112,9 +112,9 @@ def inference_instruct(self, tts_text, spk_id, instruct_text, stream=False, spee
                 yield model_output
                 start_time = time.time()
 
-    def inference_instruct2(self, tts_text, instruct_text, prompt_speech_16k, stream=False, speed=1.0):
+    def inference_instruct2(self, tts_text, instruct_text, prompt_speech_16k, stream=False, speed=1.0, text_frontend=True):
         assert isinstance(self.model, CosyVoice2Model)
-        for i in tqdm(self.frontend.text_normalize(tts_text, split=True)):
+        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))

cosyvoice/cli/frontend.py

@@ -107,12 +107,10 @@ def _extract_speech_feat(self, speech):
         speech_feat_len = torch.tensor([speech_feat.shape[1]], dtype=torch.int32).to(self.device)
         return speech_feat, speech_feat_len
 
-    def text_normalize(self, text, split=True):
+    def text_normalize(self, text, split=True, text_frontend=True):
+        if text_frontend is False:
+            return [text] if split is True else text
         text = text.strip()
-        # NOTE(lyuxiang.lx) move this judgement into ttsfrd in the future
-        for token in self.tokenizer.special_tokens['additional_special_tokens']:
-            if token in text:
-                return text if split is False else [text]
         if contains_chinese(text):
             if self.use_ttsfrd:
                 texts = [i["text"] for i in json.loads(self.frd.do_voicegen_frd(text))["sentences"]]