def evaluate(model: tf.keras.Model,
data_path: str,
max_len: int,
vocab_size: int,
max_train_data_size: int,
batch_size: int,
buffer_size: int,
tokenized_type: str = "phoneme"):
"""
评估模块
:param model: 模型
:param data_path: 文本数据路径
:param max_len: 文本序列最大长度
:param vocab_size: 词汇大小
:param tokenized_type: 分词类型，默认按音素分词，模式：phoneme(音素)/word(单词)/char(字符)
:param buffer_size: Dataset加载缓存大小
:param batch_size: Dataset加载批大小
:param max_train_data_size: 最大训练数据量
:return: 无返回值
"""
dataset, _, steps_per_epoch, _ = \
_dataset.load_data(train_data_path=data_path, max_len=max_len, vocab_size=vocab_size,
batch_size=batch_size, buffer_size=buffer_size, tokenized_type=tokenized_type,
max_train_data_size=max_train_data_size)
j = 0
score_sum = 0
for (batch, (mel, stop_token,
sentence)) in enumerate(dataset.take(steps_per_epoch)):
for i in range(sentence.shape[0]):
new_input_ids = sentence[i]
new_input_ids = tf.expand_dims(new_input_ids, axis=0)
mel_outputs, mel_outputs_postnet, gate_outputs, alignments = model.inference(
new_input_ids)
mel2 = mel[i]
mel2 = tf.expand_dims(mel2, axis=0)
mel2 = tf.transpose(mel2, [0, 2, 1])
score = spec_distance(mel_outputs_postnet, mel2)
score_sum += score
j = j + 1
print('第{}个样本的欧式距离为：{}'.format(j, score))
print("样本平均欧式距离为：", score_sum / j)
def generate(model: tf.keras.Model,
max_db: int,
ref_db: int,
sr: int,
max_len: int,
wave_save_dir: str,
n_fft: int,
n_mels: int,
pre_emphasis: float,
n_iter: int,
hop_length: int,
cmu_dict_path: str,
win_length: int,
dict_path: str = "",
tokenized_type: str = "phoneme"):
"""
生成语音的方法
:param model: 模型
:param max_len: 句子序列最大长度
:param wave_save_dir: 合成的音频保存目录
:param n_fft: FFT窗口大小
:param n_mels: 产生的梅尔带数
:param hop_length: 帧移
:param n_iter: 指针
:param win_length: 每一帧音频都由window()加窗，窗长win_length，然后用零填充以匹配N_FFT
:param max_db: 峰值分贝值
:param ref_db: 参考分贝值
:param sr: 采样率
:param pre_emphasis: 预加重
:param dict_path: 字典路径
:param cmu_dict_path: 音素字典路径
:param tokenized_type: 分词类型
:return: 无返回值
"""
if not os.path.exists(wave_save_dir):
os.makedirs(wave_save_dir)
i = 0
# 抓取文本数据
while True:
i = i + 1
b = str(i)
print()
seq = input("请输入您要合成的话，输入ESC结束：")
if seq == 'ESC':
break
sequences_list = []
sequences_list.append(
text_to_phonemes(text=seq, cmu_dict_path=cmu_dict_path))
if tokenized_type == "phoneme":
input_ids = text_to_sequence_phoneme(texts=sequences_list,
max_len=max_len)
else:
with open(dict_path, 'r', encoding="utf-8") as dict_file:
json_string = dict_file.read().strip().strip("\n")
tokenizer = tf.keras.preprocessing.text.tokenizer_from_json(
json_string)
input_ids = tokenizer.texts_to_sequences(sequences_list)
input_ids = tf.keras.preprocessing.sequence.pad_sequences(
input_ids, max_len=max_len, padding="post")
input_ids = tf.convert_to_tensor(input_ids)
# 预测
mel_outputs, mel_outputs_postnet, gate_outputs, alignments = model.inference(
input_ids)
# 生成预测声音
wav = melspectrogram2wav(mel_outputs_postnet[0].numpy(), max_db,
ref_db, sr, n_fft, n_mels, pre_emphasis,
n_iter, hop_length, win_length)
name = wave_save_dir + '\\generated' + b + '.wav'
wave.write(name, rate=sr, data=wav)
playsound(name)
print("已合成，路径：{}".format(name))
print("合成结束")
