写一个利用TensorFlow写的离线tts程序

好的，以下是一个利用TensorFlow写的离线tts程序的示例代码：
import tensorflow as tf
import numpy as np
import scipy.io.wavfile as wavfile
import os

# 设置参数
sampling_rate = 22050
n_fft = 1024
hop_length = 256
win_length = 1024
n_mels = 80
n_iter = 100
batch_size = 1
frame_length_ms = 50
frame_shift_ms = 12.5
preemphasis = 0.97
min_level_db = -100
ref_level_db = 20
power = 1.5
griffin_lim_iters = 60

# 加载语音文件
def load_wave(path):
with open(path, 'rb') as f:
rate, data = wavfile.read(f)
return rate, data.astype(np.float32)

# 对语音进行预处理
def preprocess_wav(wav):
# 预加重
wav = np.append(wav[0], wav[1:] - preemphasis * wav[:-1])
# 分帧
frame_length = int(sampling_rate * frame_length_ms / 1000.0)
frame_shift = int(sampling_rate * frame_shift_ms / 1000.0)
frames = np.stack([wav[i:i+frame_length] for i in range(0, len(wav)-frame_length+1, frame_shift)])
# 加窗
frames *= np.hamming(frame_length)
# 进行短时傅里叶变换
stfts = np.abs(np.fft.rfft(frames, n_fft))[:, :n_fft//2+1]
# 将线性频谱转换为梅尔频谱
mel_basis = tf.signal.linear_to_mel_weight_matrix(
num_mel_bins=n_mels,
num_spectrogram_bins=n_fft//2+1,
sample_rate=sampling_rate,
lower_edge_hertz=0,
upper_edge_hertz=None)
mels = np.dot(stfts, mel_basis)
mels = np.log(np.maximum(1e-10, mels))
# 归一化
mels = (mels - min_level_db) / (max_level_db - min_level_db)
return mels

# 恢复语音
def griffin_lim(spectrogram):
X_best = spectrogram
for i in range(griffin_lim_iters):
X_t = librosa.istft(X_best, hop_length, win_length)
est = librosa.stft(X_t, n_fft, hop_length, win_length=win_length)
phase = est / np.maximum(1e-8, np.abs(est))
X_best = spectrogram * phase
X_t = librosa.istft(X_best, hop_length, win_length)
y = np.real(X_t)
return y.astype('int16')

# 加载模型
model = tf.keras.models.load_model('tts_model.h5')

# 读取文本
text = 'Hello, world! This is a test.'

# 将文本转换为语音
mels = preprocess_wav(text)
mels = np.expand_dims(mels, axis=0)
mels = np.repeat(mels, batch_size, axis=0)
wav = model.predict(mels)[0]

# 恢复语音
wav = griffin_lim(wav)

# 保存为WAV文件
wavfile.write('output.wav', sampling_rate, wav)
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需要注意的是，这只是一个简单的示例代码，实际上构建一个高质量的离线TTS系统需要更多的技术和资源。同时，以上代码中的参数也可能需要根据不同的应用场景进行调整。