cut paragraph for streaming s2s

funasr/models/llm_asr/diffusion_models/flow_matching.py

@@ -5,6 +5,7 @@ from funasr.models.llm_asr.diffusion_models.matcha_decoder import (Decoder, Cond
 import logging
 from funasr.utils.hinter import hint_once
 import time
+from funasr.train_utils.set_all_random_seed import set_all_random_seed
 
 
 class BASECFM(torch.nn.Module, ABC):
@@ -47,6 +48,7 @@ class BASECFM(torch.nn.Module, ABC):
             sample: generated mel-spectrogram
                 shape: (batch_size, n_feats, mel_timesteps)
         """
+        set_all_random_seed(0)
         z = torch.randn_like(mu) * temperature
         t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device)
         if self.t_scheduler == 'cosine':

funasr/models/llm_asr/model.py

@@ -3142,9 +3142,9 @@ class LLMASRXvecSlotTTS(nn.Module):
             )[0]
             is_last = st + chunk_size >= preds.shape[1]
 
-            new_text = new_text + _resp
+            # new_text = new_text + _resp
             rt_value, states = self.generate_speech_one_step(
-                new_text,
+                new_text, _resp,
                 last_t_size,
                 llm_cur_kv_cache,
                 llm_cur_kv_cache_len,

funasr/models/llm_asr/tts_models/e2e_model.py

@@ -1003,6 +1003,7 @@ class UCTDXvecSlotModel(UpsampleCtcTokenDiffModel):
                 cur_token_len = cur_token_len - token_hop_len
 
             # forward FM model
+            set_all_random_seed(0)
             feat = self.fm_model.inference(
                 cur_token, cur_token_len,
                 xvec, xvec_lengths,