def count_chs():
z200 = Z200(config.z200_datapath)
thchs = Thchs30(config.thu_datapath)
prime = Primewords(config.prime_datapath)
stcmd = ST_CMDS(config.stcmd_datapath)
aishell = AiShell(config.aishell_datapath)
lst = [z200, thchs, prime, stcmd, aishell]
chs_set = set()
for i in lst:
_, y_set = i.load_from_path(choose_x=False, choose_y=True)
for j, yfs in enumerate(y_set):
print(f"\r{j},{yfs}", sep="\0", flush=True)
with open(yfs, encoding="utf-8") as f:
line = f.readline().strip()
chs_set.update(line)
save_path = os.path.abspath("./dataset_chs.txt")
with open(save_path, "w", encoding="utf-8") as w:
for i in chs_set:
w.write(f"{i}")
print(f"dict has been saved in {save_path}.")
def err_count():
'''
根据音频统计错误数据集
:return:
'''
z200 = Z200(config.z200_datapath)
thchs = Thchs30(config.thu_datapath)
prime = Primewords(config.prime_datapath)
stcmd = ST_CMDS(config.stcmd_datapath)
aishell = AiShell(config.aishell_datapath)
lst = [z200, thchs, prime, stcmd, aishell]
from acoustic.ABCDNN import DCBNN1D
from util.reader import PinyinMapper,VoiceDatasetList,VoiceLoader
from feature.mel_feature import MelFeature5
w, h = 1600, 200
max_label_len = 64
pymap = PinyinMapper(sil_mode=-1)
model_helper = DCBNN1D(pymap)
model_helper.compile(feature_shape=(w, h), label_max_string_length=max_label_len,
ms_output_size=pymap.max_index + 1)
model_helper.load(os.path.join(config.model_dir,"cur_best_DCBNN1D_epoch_722_step_722000.h5"))
dataset = VoiceDatasetList()
x_set, y_set = dataset.merge_load(lst)
vloader = VoiceLoader(x_set, y_set,
batch_size=16,
feature_pad_len=w,
n_mels=h,
max_label_len=max_label_len,
pymap=pymap,
melf=MelFeature5(),
divide_feature_len=8,
all_train=True,
)
viter = vloader.create_iter(one_batch = True,return_word=True)
with open("data_err.txt","w",encoding="utf-8") as w:
for i,batch in enumerate(viter):
[_, ys, _, label_len], words = batch
py_true_b = pymap.batch_vector2pylist(ys,return_word_list=True,return_list=True)
py_pred_b = model_helper.predict(batch)
for py_true,py_pred,llen,word in zip(py_true_b,py_pred_b,label_len,words):
llen = llen[0]
# w.write(f"{pyt}")
py_true = py_true[:llen]
py_true = " ".join(py_true)
py_pred = " ".join(py_pred)
print(f"\r——{i*16}.",end="\0",flush=True)
# print(word.strip(),py_pred)
w.write(f"{word.strip()}\t{py_true}\t{py_pred}\n")
import config
from examples import train_language_model as lexample
from util.reader import ST_CMDS, Thchs30, Primewords, AiShell, Z200, TextDataGenerator
from acoustic.ABCDNN import DCBNN1D, DCBNN1Dplus
from acoustic.MAXM import MPBCONM, MPCONM, MCONM
from acoustic.WAVE import WAVEM
stcmd = ST_CMDS(config.stcmd_datapath) # 据说还可以
thchs = Thchs30(
config.thu_datapath) # 同质性太高，不过好拟合，可以用来测试模型的效果，在这个数据上都没法得到比较好的结果的就没啥使用的必要了
prime = Primewords(config.prime_datapath)
aishell = AiShell(config.aishell_datapath) # 据说数据集很差，不用该数据训练
z200 = Z200(config.z200_datapath)
wiki = TextDataGenerator(config.wiki_datapath)
config.model_dir = "./model/"
'''用于强行使用CPU训练'''
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
'''语言模型——————————————————————————————————————————————————————————————————————————————————'''
'''基本没什么效果，卷着卷着就卷没了'''
# lexample.train_dcnn1d([thchs],load_model=None)
'''效果目前来看很不错，但是目前（2019年7月9日）下语料不足，貌似过拟合了，需要扩充语料后再尝试'''
# lexample.train_somiao([thchs,stcmd,prime,aishell,z200],load_model=None)
# lexample.train_sommalpha(wiki, load_model=None)
# lexample.train_sommalpha(wiki, load_model=config.join_model_path("./language/SOMMalpha_step_50500.h5"))
'''声学模型——————————————————————————————————————————————————————————————————————————————————'''
# aexample.train_mconm([thchs],config.join_model_path("./acoustic/MCONM_epoch_55_step_55000.h5"))
import config
from util.reader import Z200, Thchs30, Primewords, ST_CMDS, AiShell
if __name__ == "__main__":
Thchs30(config.thu_datapath).summary()
AiShell(config.aishell_datapath).summary()
Primewords(config.prime_datapath).summary()
ST_CMDS(config.stcmd_datapath).summary()
Z200(config.z200_datapath).summary()