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scripts/creat_human-study.py

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+import argparse
+import math
+import pickle
+import pprint
+import random
+from pathlib import Path
+from datetime import datetime
+
+import librosa
+import numpy as np
+import time
+
+import torch
+from scipy.signal import savgol_filter
+import joblib as jl
+
+import utils
+from pymo.preprocessing import *
+from pymo.viz_tools import *
+from pymo.writers import *
+from utils.data_utils import SubtitleWrapper, normalize_string
+from utils.train_utils import set_logger
+
+from data_loader.data_preprocessor import DataPreprocessor
+
+from inference_Autoencoder import smoothing_function
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+
+
+def mk_hs_slots(tags_list, name, transcript_path, dict_, result):
+    print(name)
+    transcript = SubtitleWrapper(transcript_path).get()
+
+    word_list = []
+    for wi in range(len(transcript)):
+        word_s = float(transcript[wi]['start_time'][:-1])
+        word_e = float(transcript[wi]['end_time'][:-1])
+        word = transcript[wi]['word']
+
+        word = normalize_string(word)
+        if len(word) > 0:
+            word_list.append([word, word_s, word_e])
+
+    q = 0
+    for word in word_list:
+        if word[0] == 'like':
+            q = q + 1
+
+    for tag in tags_list:
+        for word in word_list:
+            if word[0] == str.lower(tag):
+                dict_[tag] += 1
+                result.append((name, word))
+
+
+
+
+    # print("Wait")
+    return dict_, result
+
+if __name__ == '__main__':
+
+    '''
+        ../output/text2embedding/text2embedding_checkpoint_100.bin
+        /local-scratch/pjomeyaz/GENEA_DATASET/trinityspeechgesture.scss.tcd.ie/data/GENEA_Challenge_2020_data_release/Test_data/Transcripts/TestSeq001.json
+        ../output/DAE/train_DAE_H41/rep_learning_DAE_H41_checkpoint_020.bin
+        ../output/autoencoder/VAE+sim/Dautoencoder_fxw_zinput_VAE_checkpoint_020.bin
+
+
+       ../output/text2embedding/text2embedding_300d/text2embedding_300d_checkpoint_160.bin
+/local-scratch/pjomeyaz/GENEA_DATASET/trinityspeechgesture.scss.tcd.ie/data/GENEA_Challenge_2020_data_release/Test_data/Transcripts/TestSeq001.json
+../output/DAE/train_DAE_H41/rep_learning_DAE_H41_checkpoint_020.bin
+../output/autoencoder/VAE+sim/Decoder_trained/autoencoder_decoder_trained_checkpoint_020.bin
+
+
+              ../output/text2embedding_ABST/text2embedding_ABST_checkpoint_100.bin
+/local-scratch/pjomeyaz/GENEA_DATASET/trinityspeechgesture.scss.tcd.ie/data/GENEA_Challenge_2020_data_release/Test_data/Transcripts/TestSeq001.json
+../output/DAE/train_DAE_H41/rep_learning_DAE_H41_checkpoint_020.bin
+../output/autoencoder/autoencoder_both_traned_sametime/autoencoder_both_traned_sametime_checkpoint_100.bin
+
+       '''
+    random.seed(datetime.now())
+    parser = argparse.ArgumentParser()
+    parser.add_argument("ckpt_path", type=Path)
+    parser.add_argument("transcript_path", type=Path)
+    parser.add_argument("rep_learning_checkpoint", type=Path)
+    parser.add_argument("autoencoder_checkpoint", type=Path)
+
+    args = parser.parse_args()
+
+    pre_transcript_path = "/local-scratch/pjomeyaz/GENEA_DATASET/trinityspeechgesture.scss.tcd.ie/data/GENEA_Challenge_2020_data_release/Test_data/Transcripts/"
+
+
+    # Read softbank tags
+    file = open("../output/hs_testcases/softbank_tags.txt", 'r')
+    tags_list = []
+    for line in file:
+        seprated = line.split(',')
+        tags_list.append(str.lower(seprated[0]))
+
+    dict_ = dict()
+    result = []
+    for tag in tags_list:
+        dict_[tag] = 0
+    for i in reversed(range(1, 11)):
+        transcript_path = pre_transcript_path + "TestSeq" + str(i).zfill(3) + ".json"
+        transcript_path = Path(transcript_path)
+
+        dict_, result  = mk_hs_slots(tags_list, "TestSeq" + str(i).zfill(3), transcript_path, dict_, result)
+    total_categiry = 0
+    total = 0
+    for key in dict_:
+        if dict_[key]>0:
+            total_categiry+=1
+            total += dict_[key]
+            print(key, ",", dict_[key])
+
+    file = open("../output/hs_testcases/word2slot.txt", 'w')
+    for item in result:
+        str1 = item[0] + "," + item[1][0] + "," + str(item[1][1]) + "," + str(item[1][2])
+        file.write(str1 + '\n')
+    file.flush()
+    file.close()
+    print("done!", total, "of", dict_.__len__())