update params name

funasr/bin/asr_inference_paraformer.py

@@ -245,7 +245,7 @@ class Speech2Text:
             decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
 
         if isinstance(self.asr_model, BiCifParaformer):
-            _, _, us_alphas, us_cif_peak = self.asr_model.calc_predictor_timestamp(enc, enc_len,
+            _, _, us_alphas, us_peaks = self.asr_model.calc_predictor_timestamp(enc, enc_len,
                                                                                    pre_token_length)  # test no bias cif2
 
         results = []
@@ -292,7 +292,7 @@ class Speech2Text:
 
                 if isinstance(self.asr_model, BiCifParaformer):
                     _, timestamp = ts_prediction_lfr6_standard(us_alphas[i], 
-                                                            us_cif_peak[i], 
+                                                            us_peaks[i], 
                                                             copy.copy(token), 
                                                             vad_offset=begin_time)
                     results.append((text, token, token_int, hyp, timestamp, enc_len_batch_total, lfr_factor))

funasr/bin/asr_inference_paraformer_vad_punc.py

@@ -256,7 +256,7 @@ class Speech2Text:
             decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
 
         if isinstance(self.asr_model, BiCifParaformer):
-            _, _, us_alphas, us_cif_peak = self.asr_model.calc_predictor_timestamp(enc, enc_len,
+            _, _, us_alphas, us_peaks = self.asr_model.calc_predictor_timestamp(enc, enc_len,
                                                                                    pre_token_length)  # test no bias cif2
 
         results = []
@@ -303,7 +303,7 @@ class Speech2Text:
 
                 if isinstance(self.asr_model, BiCifParaformer):
                     _, timestamp = ts_prediction_lfr6_standard(us_alphas[i], 
-                                                            us_cif_peak[i], 
+                                                            us_peaks[i], 
                                                             copy.copy(token), 
                                                             vad_offset=begin_time)
                     results.append((text, token, token_int, timestamp, enc_len_batch_total, lfr_factor))

funasr/models/e2e_asr_paraformer.py

@@ -926,10 +926,10 @@ class BiCifParaformer(Paraformer):
     def calc_predictor_timestamp(self, encoder_out, encoder_out_lens, token_num):
         encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
             encoder_out.device)
-        ds_alphas, ds_cif_peak, us_alphas, us_cif_peak = self.predictor.get_upsample_timestamp(encoder_out,
+        ds_alphas, ds_cif_peak, us_alphas, us_peaks = self.predictor.get_upsample_timestamp(encoder_out,
                                                                                                encoder_out_mask,
                                                                                                token_num)
-        return ds_alphas, ds_cif_peak, us_alphas, us_cif_peak
+        return ds_alphas, ds_cif_peak, us_alphas, us_peaks
 
     def forward(
             self,

funasr/models/e2e_tp.py

@@ -150,10 +150,10 @@ class TimestampPredictor(AbsESPnetModel):
     def calc_predictor_timestamp(self, encoder_out, encoder_out_lens, token_num):
         encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
             encoder_out.device)
-        ds_alphas, ds_cif_peak, us_alphas, us_cif_peak = self.predictor.get_upsample_timestamp(encoder_out,
+        ds_alphas, ds_cif_peak, us_alphas, us_peaks = self.predictor.get_upsample_timestamp(encoder_out,
                                                                                                encoder_out_mask,
                                                                                                token_num)
-        return ds_alphas, ds_cif_peak, us_alphas, us_cif_peak
+        return ds_alphas, ds_cif_peak, us_alphas, us_peaks
 
     def collect_feats(
             self,

funasr/utils/timestamp_tools.py

@@ -6,10 +6,9 @@ from typing import Any, List, Tuple, Union
 
 
 def ts_prediction_lfr6_standard(us_alphas, 
-                       us_cif_peak, 
+                       us_peaks, 
                        char_list, 
                        vad_offset=0.0, 
-                       end_time=None, 
                        force_time_shift=-1.5
                        ):
     if not len(char_list):
@@ -18,17 +17,17 @@ def ts_prediction_lfr6_standard(us_alphas,
     MAX_TOKEN_DURATION = 12
     TIME_RATE = 10.0 * 6 / 1000 / 3  #  3 times upsampled
     if len(us_alphas.shape) == 2:
-        alphas, cif_peak = us_alphas[0], us_cif_peak[0]  # support inference batch_size=1 only
+        _, peaks = us_alphas[0], us_peaks[0]  # support inference batch_size=1 only
     else:
-        alphas, cif_peak = us_alphas, us_cif_peak
-    num_frames = cif_peak.shape[0]
+        _, peaks = us_alphas, us_peaks
+    num_frames = peaks.shape[0]
     if char_list[-1] == '</s>':
         char_list = char_list[:-1]
     timestamp_list = []
     new_char_list = []
     # for bicif model trained with large data, cif2 actually fires when a character starts
     # so treat the frames between two peaks as the duration of the former token
-    fire_place = torch.where(cif_peak>1.0-1e-4)[0].cpu().numpy() + force_time_shift  # total offset
+    fire_place = torch.where(peaks>1.0-1e-4)[0].cpu().numpy() + force_time_shift  # total offset
     num_peak = len(fire_place)
     assert num_peak == len(char_list) + 1 # number of peaks is supposed to be number of tokens + 1
     # begin silence