release timestasmp related tools

funasr/utils/timestamp_tools.py

@@ -1,3 +1,4 @@
+from pydoc import TextRepr
 from scipy.fftpack import shift
 import torch
 import copy
@@ -5,6 +6,7 @@ import codecs
 import logging
 import edit_distance
 import argparse
+import pdb
 import numpy as np
 from typing import Any, List, Tuple, Union
 
@@ -13,7 +15,8 @@ def ts_prediction_lfr6_standard(us_alphas,
                        us_peaks, 
                        char_list, 
                        vad_offset=0.0, 
-                       force_time_shift=-1.5
+                       force_time_shift=-1.5,
+                       sil_in_str=True
                        ):
     if not len(char_list):
         return []
@@ -66,6 +69,8 @@ def ts_prediction_lfr6_standard(us_alphas,
             timestamp_list[i][1] = timestamp_list[i][1] + vad_offset / 1000.0
     res_txt = ""
     for char, timestamp in zip(new_char_list, timestamp_list):
+        #if char != '<sil>':
+        if not sil_in_str and char == '<sil>': continue
         res_txt += "{} {} {};".format(char, str(timestamp[0]+0.0005)[:5], str(timestamp[1]+0.0005)[:5])
     res = []
     for char, timestamp in zip(new_char_list, timestamp_list):
@@ -233,13 +238,54 @@ class AverageShiftCalculator():
         return self._accumlated_shift / self._accumlated_tokens
 
 
-SUPPORTED_MODES = ['cal_aas']
+def convert_external_alphas(alphas_file, text_file, output_file):
+    from funasr.models.predictor.cif import cif_wo_hidden
+    with open(alphas_file, 'r') as f1, open(text_file, 'r') as f2, open(output_file, 'w') as f3:
+        for line1, line2 in zip(f1.readlines(), f2.readlines()):
+            line1 = line1.rstrip()
+            line2 = line2.rstrip()
+            assert line1.split()[0] == line2.split()[0]
+            uttid = line1.split()[0]
+            alphas = [float(i) for i in line1.split()[1:]]
+            new_alphas = np.array(remove_chunk_padding(alphas))
+            new_alphas[-1] += 1e-4
+            text = line2.split()[1:]
+            if len(text) + 1 != int(new_alphas.sum()):
+                # force resize
+                new_alphas *= (len(text) + 1) / int(new_alphas.sum())
+            peaks = cif_wo_hidden(torch.Tensor(new_alphas).unsqueeze(0), 1.0-1e-4)
+            if " " in text:
+                text = text.split()
+            else:
+                text = [i for i in text]
+            res_str, _ = ts_prediction_lfr6_standard(new_alphas, peaks[0], text, 
+                                                     force_time_shift=-7.0, 
+                                                     sil_in_str=False)
+            f3.write("{} {}\n".format(uttid, res_str))
+
+
+def remove_chunk_padding(alphas):
+    # remove the padding part in alphas if using chunk paraformer for GPU
+    START_ZERO = 45
+    MID_ZERO = 75
+    REAL_FRAMES = 360  # for chunk based encoder 10-120-10 and fsmn padding 5
+    alphas = alphas[START_ZERO:]  # remove the padding at beginning
+    new_alphas = []
+    while True:
+        new_alphas = new_alphas + alphas[:REAL_FRAMES]
+        alphas = alphas[REAL_FRAMES+MID_ZERO:]
+        if len(alphas) < REAL_FRAMES: break
+    return new_alphas
+
+SUPPORTED_MODES = ['cal_aas', 'read_ext_alphas']
 
 
 def main(args):
     if args.mode == 'cal_aas':
         asc = AverageShiftCalculator()
         asc(args.input, args.input2)
+    elif args.mode == 'read_ext_alphas':
+        convert_external_alphas(args.input, args.input2, args.output)
     else:
         logging.error("Mode {} not in SUPPORTED_MODES: {}.".format(args.mode, SUPPORTED_MODES))