def SR_recognize(self, wavs, pre_type):
hanzi = ''
am_url = tf_serving_url.format('am')
lm_url = tf_serving_url.format('lm')
if ues_tf_serving:
x, _, _ = utils.get_wav_Feature(wavsignal=wavs)
try:
receipt = requests.post(am_url,
data='{"instances":%s}' %
x.tolist()).json()['predictions'][0]
receipt = np.array([receipt], dtype=np.float32)
except BaseException as e:
return _, '声学模型调用异常'
_, pinyin = utils.decode_ctc(receipt, utils.pny_vocab)
pinyin = [[
utils.pny_vocab.index(p)
for p in ' '.join(pinyin).strip('\n').split(' ')
]]
if pre_type == 'H':
#curl -d '{"instances": [[420,58]]}' -X POST http://localhost:8501/v1/models/lm:predict
try:
hanzi = requests.post(lm_url,
data='{"instances": %s}' %
pinyin).json()['predictions'][0]
except BaseException as e:
return _, '语言模型调用异常'
hanzi = ''.join(utils.han_vocab[idx] for idx in hanzi)
else:
if pre_type == 'H':
pinyin, hanzi = yysb.predict(wavs)
else:
pinyin = yysb.predict(wavs, only_pinyin=True)
return pinyin, hanzi
def test_batch():
data_args.data_type = 'test'
data_args.shuffle = False
data_args.batch_size = 1
test_data = get_data(data_args)
am_batch = test_data.get_am_batch()
word_num = 0
word_error_num = 0
for i in range(10):
print('\n the ', i, 'th example.')
# 载入训练好的模型，并进行识别
inputs, _ = next(am_batch)
x = inputs['the_inputs']
y = test_data.pny_lst[i]
result = am.model.predict(x, steps=1)
# 将数字结果转化为文本结果
_, text = decode_ctc(result, train_data.am_vocab)
text = ' '.join(text)
print('文本结果：', text)
print('原文结果：', ' '.join(y))
with sess.as_default():
text = text.strip('\n').split(' ')
x = np.array([train_data.pny_vocab.index(pny) for pny in text])
x = x.reshape(1, -1)
preds = sess.run(lm.preds, {lm.x: x})
label = test_data.han_lst[i]
got = ''.join(train_data.han_vocab[idx] for idx in preds[0])
print('原文汉字：', label)
print('识别结果：', got)
word_error_num += min(len(label), GetEditDistance(label, got))
word_num += len(label)
print('词错误率：', word_error_num / word_num)
sess.close()
def speech(self, filename):
try:
inputs, _ = self.data_processer.am_data_process(filename)
audio_con = inputs['the_inputs']
result = self.am.model.predict(audio_con, steps=1)
_, text = decode_ctc(result, self.vocab.ampny_vocab)
text = ' '.join(text)
with self.sess.as_default():
text = text.strip('\n').split(' ')
x = np.array([self.vocab.pny_vocab.index(pny) for pny in text if pny != ''])
if len(x) == 0:
x = np.array([120, 79, 1, 53]) # 为了不出现警告，临时添加的没用，加几都行
x = x.reshape(1, -1)
preds = self.sess.run(self.lm.preds, {self.lm.x: x})
ultimate_result = ''.join(self.vocab.han_vocab[idx] for idx in preds[0])
return ultimate_result
except KeyboardInterrupt:
return
data_args.batch_size = 1
test_data = get_data(data_args) # get_data是一個class
# 4. 进行测试-------------------------------------------
am_batch = test_data.get_am_batch()
word_num = 0
word_error_num = 0
for i in range(10):
print('\n the ', i, 'th example.')
# 载入训练好的模型，并进行识别
inputs, _ = next(am_batch)
x = inputs['the_inputs']
y = test_data.pny_lst[i]
result = am.model.predict(x, steps=1)
# 将数字结果转化为文本结果
_, text = decode_ctc(result, train_data.am_vocab)
text = ' '.join(text)
print('文本結果：', text)
print('原文結果：', ' '.join(y))
with sess.as_default():
text = text.strip('\n').split(' ')
x = np.array([train_data.pny_vocab.index(pny) for pny in text])
x = x.reshape(1, -1)
preds = sess.run(lm.preds, {lm.x: x})
label = test_data.han_lst[i]
got = ''.join(train_data.han_vocab[idx] for idx in preds[0])
print('原文漢字：', label)
print('识别結果：', got)
word_error_num += min(len(label), GetEditDistance(label, got))
word_num += len(label)
print('詞错誤率：', word_error_num / word_num)
am_args = am_hparams()
am_args.vocab_size = len(am_vocab)
am = Am(am_args)
print('loading acoustic model...')
am.ctc_model.load_weights(model_save_path)
data_args.data_type = 'test'
data_args.shuffle = False
data_args.batch_size = 1
test_data = get_data(data_args)
# 4. 进行测试-------------------------------------------
am_batch = test_data.get_am_batch()
word_num = 0
word_error_num = 0
for i in range(1):
print('\n the ', i, 'th example.')
# 载入训练好的模型，并进行识别
inputs, _ = next(am_batch)
x = inputs['the_inputs']
y = test_data.pny_lst[i]
result = am.model.predict(x, steps=1)
# 将数字结果转化为文本结果
_, text = decode_ctc(result, am_vocab)
text = ' '.join(text)
print('文本结果：', text)
print('原文结果：', ' '.join(y))
word_error_num += min(len(y), GetEditDistance(y, text.split(' ')))
word_num += len(y)
print('词错误率：', word_error_num / word_num)
def predict(thefile):
if not thefile.endswith('wav'):
print("[Error*] The file is not in .wav format!")
testfile = wave.open('./tmp/' + thefile, mode='rb')
print("The input has {} channel(s)".format(testfile.getnchannels()))
am_batch = ''
framerate = testfile.getframerate()
framenum = testfile.getnframes()
length = framenum / framerate
print("The length of {} is {} seconds.".format(thefile, length))
max_len = 10
if length > max_len:
piece_len = (max_len // 3) * 2
portion = piece_len * framerate
n_pieces = length // piece_len + 1
n_pieces = int(n_pieces)
print(
"The file exceeds the max length of {} seconds and needs to be split into {} pieces"
.format(max_len, n_pieces))
for i in range(n_pieces):
apiece = testfile.readframes(framerate * max_len)
testfile.setpos(testfile.tell() - portion / 2)
tmp = wave.open('./tmp/tmp{:04}.wav'.format(i), mode='wb')
tmp.setnchannels(1)
tmp.setframerate(16000)
tmp.setsampwidth(2)
tmp.writeframes(apiece)
tmp.close()
am_batch = test_data.get_dep_batch(os.listdir('./tmp/'))
for i in range(n_pieces):
inputs, _ = next(am_batch)
x = inputs['the_inputs']
#print(x.shape)
#y = test_data.pny_lst[i]
result = am.model.predict(x, steps=1)
# 将数字结果转化为文本结果
_, text = decode_ctc(result, train_data.am_vocab)
text = ' '.join(text)
print('文本结果：', text)
#print('原文结果：', ' '.join(y))
with sess.as_default():
text = text.strip('\n').split(' ')
x = np.array([train_data.pny_vocab.index(pny) for pny in text])
x = x.reshape(1, -1)
preds = sess.run(lm.preds, {lm.x: x})
#label = test_data.han_lst[i]
got = ''.join(train_data.han_vocab[idx] for idx in preds[0])
#print('原文汉字：', label)
print('识别结果：', got)
#word_error_num += min(len(label), GetEditDistance(label, got))
else:
thelist = [thefile]
am_batch = test_data.get_dep_batch(thelist)
inputs, _ = next(am_batch)
x = inputs['the_inputs']
#print(x.shape)
#y = test_data.pny_lst[i]
result = am.model.predict(x, steps=1)
# 将数字结果转化为文本结果
_, text = decode_ctc(result, train_data.am_vocab)
text = ' '.join(text)
print('文本结果：', text)
#print('原文结果：', ' '.join(y))
with sess.as_default():
text = text.strip('\n').split(' ')
x = np.array([train_data.pny_vocab.index(pny) for pny in text])
x = x.reshape(1, -1)
preds = sess.run(lm.preds, {lm.x: x})
#label = test_data.han_lst[i]
got = ''.join(train_data.han_vocab[idx] for idx in preds[0])
#print('原文汉字：', label)
print('识别结果：', got)
def predict(thefile, conns, lock):
if not thefile.endswith('wav'):
print("[Error*] The file is not in .wav format!")
testfile = wave.open(thefile, mode='rb')
print("The input has {} channel(s)".format(testfile.getnchannels()))
am_batch = ''
framerate = testfile.getframerate()
framenum = testfile.getnframes()
length = framenum / framerate
print("The length of {} is {} seconds.".format(thefile, length))
max_len = 10
if length > max_len:
piece_len = max_len #(max_len // 3) * 2
portion = piece_len * framerate
n_pieces = length // piece_len + 1
n_pieces = int(n_pieces)
print(
"The file exceeds the max length of {} seconds and needs to be split into {} pieces"
.format(max_len, n_pieces))
filelist = []
for i in range(n_pieces):
apiece = testfile.readframes(framerate * max_len)
#testfile.setpos(testfile.tell()-portion)
filename = './tmp/tmp{:04}.wav'.format(i)
tmp = wave.open(filename, mode='wb')
tmp.setnchannels(1)
tmp.setframerate(16000)
tmp.setsampwidth(2)
tmp.writeframes(apiece)
tmp.close()
filelist.append(filename)
#am_batch = test_data.get_dep_batch(os.listdir('./tmp/'))
am_batch = test_data.get_dep_batch(filelist)
#am_batch = test_data.get_dep_batch(os.listdir('/home/comp/15485625/data/speech/sp2chs/data_aishell/wav/test/'))
for i in range(n_pieces):
inputs, _ = next(am_batch)
x = inputs['the_inputs']
#print(x.shape)
#y = test_data.pny_lst[i]
result = am.model.predict(x, steps=1)
# 将数字结果转化为文本结果
_, text = decode_ctc(result, train_data.am_vocab)
text = ' '.join(text)
print('%s: %s' % (filelist[i], text))
#print('原文结果：', ' '.join(y))
with sess.as_default():
text = text.strip('\n').split(' ')
x = np.array([train_data.pny_vocab.index(pny) for pny in text])
x = x.reshape(1, -1)
preds = sess.run(lm.preds, {lm.x: x})
#label = test_data.han_lst[i]
got = ''.join(train_data.han_vocab[idx] for idx in preds[0])
#print('原文汉字：', label)
#print('识别结果：', got)
# modified_RecvMessage = data.decode('utf-8')
#print(modified_RecvMessage)
lock.acquire()
for conn in conns:
try:
conn.send((got + ':').encode('utf-8'))
except Exception as e:
pass
lock.release()
print('%s: %s' % (filelist[i], got))
#word_error_num += min(len(label), GetEditDistance(label, got))
#word_num += len(label)
else:
filelist = [thefile]
am_batch = test_data.get_dep_batch(filelist)
inputs, _ = next(am_batch)
x = inputs['the_inputs']
#print(x.shape)
#y = test_data.pny_lst[i]
result = am.model.predict(x, steps=1)
# 将数字结果转化为文本结果
_, text = decode_ctc(result, train_data.am_vocab)
text = ' '.join(text)
print('%s: %s' % (filelist[0], text))
#print('原文结果：', ' '.join(y))
with sess.as_default():
text = text.strip('\n').split(' ')
x = np.array([train_data.pny_vocab.index(pny) for pny in text])
x = x.reshape(1, -1)
preds = sess.run(lm.preds, {lm.x: x})
#label = test_data.han_lst[i]
got = ''.join(train_data.han_vocab[idx] for idx in preds[0])
#print('原文汉字：', label)
#print('识别结果：', got)
# modified_RecvMessage = data.decode('utf-8')
#print(modified_RecvMessage)
lock.acquire()
for conn in conns:
try:
conn.send((got + ':').encode('utf-8'))
except Exception as e:
pass
lock.release()
print('%s: %s' % (filelist[0], got))