cut paragraph for streaming s2s

funasr/models/llm_asr/model.py

@@ -3007,7 +3007,7 @@ class LLMASRXvecSlotTTS(nn.Module):
 
     def generate_speech_one_step(
         self,
-        text: str,
+        text: str, preds: str,
         last_t_size,
         llm_cur_kv_cache,
         llm_cur_kv_cache_len,
@@ -3016,24 +3016,38 @@ class LLMASRXvecSlotTTS(nn.Module):
         tts_text_chunk_size,
         chunk_idx,
         is_last,
-        para_len=30,
+        para_phone_len=200,
     ):
         device = llm_cur_kv_cache.device
         pounc = ["。", "？", "！", "；", "：", ".", "?", "!", ";", "\n"]
 
         # remove duplicated pounctuations
-        normed_text = []
-        for i, c in enumerate(text):
+        normed_preds = []
+        for i, c in enumerate(preds):
             if i > 0 and text[i - 1] in pounc and text[i] in pounc:
                 continue
-            normed_text.append(c)
-        text = "".join(normed_text)
+            normed_preds.append(c)
+        preds = self.split_characters_and_words("".join(normed_preds))
+        idx = -1
+        for p in pounc:
+            idx = preds.index(p)
+            if idx > -1:
+                break
+
+        _text = f"<|endofprompt|><|sil|>{text}" + ("<|sil|>" if is_last else "")
+        para_end = False
+        if idx > -1 and not is_last:
+            pre_part = "".join(preds[:idx+1])
+            if len(self.tts_tokenizer_warpper(text+pre_part)) >= para_phone_len:
+                _text = f"<|endofprompt|><|sil|>{text+pre_part}<|sil|>"
+                para_end = True
+                text = "".join(preds[idx+1:])
+                last_t_size = 0
 
         cur_token, feat, wav = None, None, None
-        _text = f"<|endofprompt|><|sil|>{text}" + ("<|sil|>" if is_last else "")
         text_token = self.tts_tokenizer_warpper(_text)
         t_size = len(text_token)
-        if (t_size - last_t_size) >= tts_text_chunk_size or is_last:
+        if (t_size - last_t_size) >= tts_text_chunk_size or is_last or para_end:
             text_token = torch.tensor([text_token], dtype=torch.long, device=device)
             text_token_len = torch.tensor([text_token.shape[1]], dtype=torch.long, device=device)
             cur_token, feat = self.tts_model.streaming_one_step(
@@ -3072,19 +3086,8 @@ class LLMASRXvecSlotTTS(nn.Module):
                 cur_token, feat, wav = None, None, None
 
             # post process
-            last_t_size = t_size
-            # restart a new paragraph
-            # char_words = self.split_characters_and_words(text)
-            # if len(char_words) > para_len:
-            #     # find the last pounc to split paragraph
-            #     idx = -1
-            #     for i in range(len(char_words)-1, -1, -1):
-            #         if char_words[i] in pounc:
-            #             idx = i
-            #             break
-            #     if idx > 0:
-            #         text = text[idx+1:]
-            #         last_t_size = len(self.tts_tokenizer_warpper(text))
+            if not para_end:
+                last_t_size = t_size
 
         return ((cur_token, feat, wav), (text, last_t_size, prompt_token, prompt_audio, chunk_idx))
 
@@ -3187,9 +3190,9 @@ class LLMASRXvecSlotTTS(nn.Module):
             states["prompt_audio"],
             states["chunk_idx"],
         )
-        new_text = new_text + preds
+        # new_text = new_text + preds
         rt_value, states_ret = self.generate_speech_one_step(
-            new_text,
+            new_text, preds,
             last_t_size,
             llm_cur_kv_cache,
             llm_cur_kv_cache_len,