Merge pull request #179 from alibaba-damo-academy/dev_gzf

torchscripts

funasr/runtime/python/libtorch/torch_paraformer/paraformer_bin.py

@@ -12,7 +12,7 @@ from .utils.utils import (CharTokenizer, Hypothesis,
                           read_yaml)
 from .utils.postprocess_utils import sentence_postprocess
 from .utils.frontend import WavFrontend
-from funasr.utils.timestamp_tools import time_stamp_lfr6_pl
+from .utils.timestamp_utils import time_stamp_lfr6_onnx
 logging = get_logger()
 
 import torch

funasr/runtime/python/libtorch/torch_paraformer/utils/timestamp_utils.py

@@ -0,0 +1,58 @@
+import numpy as np
+
+
+def time_stamp_lfr6_onnx(us_cif_peak, char_list, begin_time=0.0):
+    if not len(char_list):
+        return []
+    START_END_THRESHOLD = 5
+    MAX_TOKEN_DURATION = 14
+    TIME_RATE = 10.0 * 6 / 1000 / 3  #  3 times upsampled
+    cif_peak = us_cif_peak.reshape(-1)
+    num_frames = cif_peak.shape[-1]
+    if char_list[-1] == '</s>':
+        char_list = char_list[:-1]
+    # char_list = [i for i in text]
+    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 = np.where(cif_peak>1.0-1e-4)[0] - 1.5  # np format
+    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
+    if fire_place[0] > START_END_THRESHOLD:
+        # char_list.insert(0, '<sil>')
+        timestamp_list.append([0.0, fire_place[0]*TIME_RATE])
+        new_char_list.append('<sil>')
+    # tokens timestamp
+    for i in range(len(fire_place)-1):
+        new_char_list.append(char_list[i])
+        if MAX_TOKEN_DURATION < 0 or fire_place[i+1] - fire_place[i] < MAX_TOKEN_DURATION:
+            timestamp_list.append([fire_place[i]*TIME_RATE, fire_place[i+1]*TIME_RATE])
+        else:
+            # cut the duration to token and sil of the 0-weight frames last long
+            _split = fire_place[i] + MAX_TOKEN_DURATION
+            timestamp_list.append([fire_place[i]*TIME_RATE, _split*TIME_RATE])
+            timestamp_list.append([_split*TIME_RATE, fire_place[i+1]*TIME_RATE])
+            new_char_list.append('<sil>')
+    # tail token and end silence
+    if num_frames - fire_place[-1] > START_END_THRESHOLD:
+        _end = (num_frames + fire_place[-1]) / 2
+        timestamp_list[-1][1] = _end*TIME_RATE
+        timestamp_list.append([_end*TIME_RATE, num_frames*TIME_RATE])
+        new_char_list.append("<sil>")
+    else:
+        timestamp_list[-1][1] = num_frames*TIME_RATE
+    if begin_time:  # add offset time in model with vad
+        for i in range(len(timestamp_list)):
+            timestamp_list[i][0] = timestamp_list[i][0] + begin_time / 1000.0
+            timestamp_list[i][1] = timestamp_list[i][1] + begin_time / 1000.0
+    assert len(new_char_list) == len(timestamp_list)
+    res_total = []
+    for char, timestamp in zip(new_char_list, timestamp_list):
+        res_total.append([char, timestamp[0], timestamp[1]])  # += "{} {} {};".format(char, timestamp[0], timestamp[1])
+    res = []
+    for char, timestamp in zip(new_char_list, timestamp_list):
+        if char != '<sil>':
+            res.append([int(timestamp[0] * 1000), int(timestamp[1] * 1000)])
+    return res, res_total

funasr/runtime/python/onnxruntime/requirements.txt

@@ -1,6 +0,0 @@
-librosa
-numpy
-onnxruntime
-scipy
-typeguard
-kaldi-native-fbank