Merge pull request #124 from alibaba-damo-academy/dev_lzr

remove useless code

funasr/bin/asr_inference_paraformer_vad.py

@@ -38,7 +38,6 @@ from funasr.utils.types import str_or_none
 from funasr.utils import asr_utils, wav_utils, postprocess_utils
 from funasr.models.frontend.wav_frontend import WavFrontend
 from funasr.tasks.vad import VADTask
-from funasr.utils.timestamp_tools import time_stamp_lfr6
 from funasr.bin.punctuation_infer import Text2Punc
 from funasr.bin.asr_inference_paraformer_vad_punc import Speech2Text
 from funasr.bin.asr_inference_paraformer_vad_punc import Speech2VadSegment

funasr/bin/asr_inference_paraformer_vad_punc.py

@@ -39,7 +39,7 @@ from funasr.utils.types import str_or_none
 from funasr.utils import asr_utils, wav_utils, postprocess_utils
 from funasr.models.frontend.wav_frontend import WavFrontend
 from funasr.tasks.vad import VADTask
-from funasr.utils.timestamp_tools import time_stamp_lfr6, time_stamp_lfr6_pl
+from funasr.utils.timestamp_tools import time_stamp_lfr6_pl
 from funasr.bin.punctuation_infer import Text2Punc
 from funasr.models.e2e_asr_paraformer import BiCifParaformer
 
@@ -282,12 +282,8 @@ class Speech2Text:
                 else:
                     text = None
 
-                if isinstance(self.asr_model, BiCifParaformer):
-                    timestamp = time_stamp_lfr6_pl(us_alphas[i], us_cif_peak[i], copy.copy(token), begin_time, end_time)
-                    results.append((text, token, token_int, timestamp, enc_len_batch_total, lfr_factor))
-                else:
-                    time_stamp = time_stamp_lfr6(alphas[i:i + 1, ], enc_len[i:i + 1, ], copy.copy(token), begin_time, end_time)
-                    results.append((text, token, token_int, time_stamp, enc_len_batch_total, lfr_factor))
+                timestamp = time_stamp_lfr6_pl(us_alphas[i], us_cif_peak[i], copy.copy(token), begin_time, end_time)
+                results.append((text, token, token_int, timestamp, enc_len_batch_total, lfr_factor))
 
         # assert check_return_type(results)
         return results
@@ -617,7 +613,7 @@ def inference_modelscope(
                 result = result_segments[0]
                 text, token, token_int = result[0], result[1], result[2]
                 time_stamp = None if len(result) < 4 else result[3]
-   
+
                 if use_timestamp and time_stamp is not None: 
                     postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
                 else:

funasr/utils/timestamp_tools.py

@@ -4,88 +4,6 @@ import logging
 import numpy as np
 from typing import Any, List, Tuple, Union
 
-def cut_interval(alphas: torch.Tensor, start: int, end: int, tail: bool):
-    if not tail:
-        if end == start + 1:
-            cut = (end + start) / 2.0
-        else:
-            alpha = alphas[start+1: end].tolist()
-            reverse_steps = 1
-            for reverse_alpha in alpha[::-1]:
-                if reverse_alpha > 0.35:
-                    reverse_steps += 1
-                else:
-                    break
-            cut = end - reverse_steps
-    else:
-        if end != len(alphas) - 1:
-            cut = end + 1
-        else:
-            cut = start + 1
-    return float(cut)
-
-def time_stamp_lfr6(alphas: torch.Tensor, speech_lengths: torch.Tensor, raw_text: List[str], begin: int = 0, end: int = None):
-    time_stamp_list = []
-    alphas = alphas[0]
-    text = copy.deepcopy(raw_text)
-    if end is None:
-        time = speech_lengths * 60 / 1000
-        sacle_rate = (time / speech_lengths[0]).tolist()
-    else:
-        time = (end - begin) / 1000
-        sacle_rate = (time / speech_lengths[0]).tolist()
-
-    predictor = (alphas > 0.5).int()
-    fire_places = torch.nonzero(predictor == 1).squeeze(1).tolist()
-    
-    cuts = []
-    npeak = int(predictor.sum())
-    nchar = len(raw_text)
-    if npeak - 1 == nchar:
-        fire_places = torch.where((alphas > 0.5) == 1)[0].tolist()
-        for i in range(len(fire_places)):
-            if fire_places[i] < len(alphas) - 1:
-                if 0.05 < alphas[fire_places[i]+1] < 0.5:
-                    fire_places[i] += 1
-    elif npeak < nchar:
-        lost_num = nchar - npeak
-        lost_fire = speech_lengths[0].tolist() - fire_places[-1]
-        interval_distance = lost_fire // (lost_num + 1)
-        for i in range(1, lost_num + 1):
-            fire_places.append(fire_places[-1] + interval_distance)
-    elif npeak - 1 > nchar:
-        redundance_num = npeak - 1 - nchar
-        for i in range(redundance_num):
-            fire_places.pop() 
-
-    cuts.append(0)
-    start_sil = True
-    if start_sil:
-        text.insert(0, '<sil>')
-
-    for i in range(len(fire_places)-1):
-        cuts.append(cut_interval(alphas, fire_places[i], fire_places[i+1], tail=(i==len(fire_places)-2)))
-
-    for i in range(2, len(fire_places)-2):
-        if fire_places[i-2] == fire_places[i-1] - 1 and fire_places[i-1] != fire_places[i] - 1:
-            cuts[i-1] += 1
-
-    if cuts[-1] != len(alphas) - 1:
-        text.append('<sil>')
-        cuts.append(speech_lengths[0].tolist())
-    cuts.insert(-1, (cuts[-1] + cuts[-2]) * 0.5)
-    sec_fire_places = np.array(cuts) * sacle_rate
-    for i in range(1, len(sec_fire_places) - 1):
-        start, end = sec_fire_places[i], sec_fire_places[i+1]
-        if i == len(sec_fire_places) - 2:
-            end = time
-        time_stamp_list.append([int(round(start, 2) * 1000) + begin, int(round(end, 2) * 1000) + begin])
-        text = text[1:]
-    if npeak - 1 == nchar or npeak > nchar:
-        return time_stamp_list[:-1]
-    else:
-        return time_stamp_list
-
 def time_stamp_lfr6_pl(us_alphas, us_cif_peak, char_list, begin_time=0.0, end_time=None):
     START_END_THRESHOLD = 5
     TIME_RATE = 10.0 * 6 / 1000 / 3  #  3 times upsampled