环境安装
系统：Ubuntu >= 16.04
源码下载
- 克隆
PaddleSpeech仓库
bash
# github下载
git clone https://github.com/PaddlePaddle/PaddleSpeech.git
# 也可以从gitee下载
git clone https://gitee.com/paddlepaddle/PaddleSpeech.git
# 进入PaddleSpeech目录
cd PaddleSpeech安装 Conda
bash
使用apt安装 build-essential
sudo apt install build-essential
# 下载 miniconda
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -P tools/
# 安装 miniconda
bash tools/Miniconda3-latest-Linux-x86_64.sh -b
# conda 初始化
$HOME/miniconda3/bin/conda init
# 激活 conda
bash
# 创建 Conda 虚拟环境
conda create -y -p tools/venv python=3.8
# 激活 Conda 虚拟环境:
conda activate tools/venv
# 安装 Conda 包
conda install -y -c conda-forge sox libsndfile swig bzip2 libflac bc安装 PaddlePaddle
bash
#CPU版本安装
python3 -m pip install paddlepaddle- -i https://mirror.baidu.com/pypi/simple
#GPU版本安装，注意：2.4.1 只是一个示例，请按照对paddlepaddle的最小依赖进行选择。
python3 -m pip install paddlepaddle-gpu==2.4.1 -i https://mirror.baidu.com/pypi/simple用开发者模式安装 PaddleSpeech
bash
pip install pytest-runner -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install -e .[develop] -i https://pypi.tuna.tsinghua.edu.cn/simple下载预训练模型
bash
#下载预训练模型：声学模型、声码器
!mkdir download
#中文男声学模型
!wget -P download https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_male_zh_ckpt_1.4.0.zip
!unzip -d download download/fastspeech2_male_zh_ckpt_1.4.0.zip
#声码器
!wget -P download https://paddlespeech.bj.bcebos.com/Parakeet/released_models/hifigan/hifigan_male_ckpt_1.4.0.zip
!unzip -d download download/hifigan_male_ckpt_1.4.0.zip语音合成
脚本命名为：FastSpeech2-hifigan.py
python
import argparse
import os
from pathlib import Path
import IPython.display as dp
import matplotlib.pyplot as plt
import numpy as np
import paddle
import soundfile as sf
import yaml
from paddlespeech.t2s.frontend.zh_frontend import Frontend
from paddlespeech.t2s.models.fastspeech2 import FastSpeech2
from paddlespeech.t2s.models.fastspeech2 import FastSpeech2Inference
from paddlespeech.t2s.models.hifigan import HiFiGANGenerator
from paddlespeech.t2s.models.hifigan import HiFiGANInference
from paddlespeech.t2s.modules.normalizer import ZScore
from yacs.config import CfgNode
# 配置预训练模型
fastspeech2_config = "download/fastspeech2_male_zh_ckpt_1.4.0/default.yaml"
fastspeech2_checkpoint = "download/fastspeech2_male_zh_ckpt_1.4.0/snapshot_iter_76000.pdz"
fastspeech2_stat = "download/fastspeech2_male_zh_ckpt_1.4.0/speech_stats.npy"
hifigan_config = "download/hifigan_male_ckpt_1.4.0/default.yaml"
hifigan_checkpoint = "download/hifigan_male_ckpt_1.4.0/snapshot_iter_630000.pdz"
hifigan_stat = "download/hifigan_male_ckpt_1.4.0/feats_stats.npy"
phones_dict = "download/fastspeech2_male_zh_ckpt_1.4.0/phone_id_map.txt"
# 读取 conf 配置文件并结构化
with open(fastspeech2_config) as f:
fastspeech2_config = CfgNode(yaml.safe_load(f))
with open(hifigan_config) as f:
hifigan_config = CfgNode(yaml.safe_load(f))
print("========Config========")
print(fastspeech2_config)
print("---------------------")
print(hifigan_config)
# 构造文本前端对象
# 传入 phones_dict 会把相应的 phones 转换成 phone_ids
frontend = Frontend(phone_vocab_path=phones_dict)
print("Frontend done!")
# 调用文本前端
# input = "我每天中午12:00起床"
# input = "我出生于2005/11/08，那天的最低气温达到-10°C"
input = "先生您好，欢迎使用百度飞桨框架进行深度学习！"
input_ids = frontend.get_input_ids(input, merge_sentences=True, print_info=True)
phone_ids = input_ids["phone_ids"][0]
print("phone_ids:%s"%phone_ids)
# 初始化声学模型
with open(phones_dict, "r") as f:
phn_id = [line.strip().split() for line in f.readlines()]
vocab_size = len(phn_id)
print("vocab_size:", vocab_size)
odim = fastspeech2_config.n_mels
model = FastSpeech2(
idim=vocab_size, odim=odim, **fastspeech2_config["model"])
# 加载预训练模型参数
model.set_state_dict(paddle.load(fastspeech2_checkpoint)["main_params"])
# 推理阶段不启用 batch norm 和 dropout
model.eval()
stat = np.load(fastspeech2_stat)
# 读取数据预处理阶段数据集的均值和标准差
mu, std = stat
mu, std = paddle.to_tensor(mu), paddle.to_tensor(std)
# 构造归一化的新模型
fastspeech2_normalizer = ZScore(mu, std)
fastspeech2_inference = FastSpeech2Inference(fastspeech2_normalizer, model)
fastspeech2_inference.eval()
print("FastSpeech2 done!")
# 调用声学模型
with paddle.no_grad():
mel = fastspeech2_inference(phone_ids)
print("shepe of mel (n_frames x n_mels):")
print(mel.shape)
# 绘制声学模型输出的 mel 频谱
#fig, ax = plt.subplots(figsize=(16, 6))
#im = ax.imshow(mel.T, aspect='auto',origin='lower')
#plt.title('Mel Spectrogram')
#plt.xlabel('Time')
#plt.ylabel('Frequency')
#plt.tight_layout()
# 初始化声码器
vocoder = HiFiGANGenerator(**hifigan_config["generator_params"])
# 模型加载预训练参数
vocoder.set_state_dict(paddle.load(hifigan_checkpoint)["generator_params"])
vocoder.remove_weight_norm()
# 推理阶段不启用 batch norm 和 dropout
vocoder.eval()
# 读取数据预处理阶段数据集的均值和标准差
stat = np.load(hifigan_stat)
mu, std = stat
mu, std = paddle.to_tensor(mu), paddle.to_tensor(std)
hifigan_normalizer = ZScore(mu, std)
# 构建归一化的模型
hifigan_inference = HiFiGANInference(hifigan_normalizer, vocoder)
hifigan_inference.eval()
print("HiFiGan done!")
# 调用声码器
with paddle.no_grad():
wav = hifigan_inference(mel)
print("shepe of wav (time x n_channels):%s"%wav.shape)
# 绘制声码器输出的波形图
wave_data = wav.numpy().T
time = np.arange(0, wave_data.shape[1]) * (1.0 / fastspeech2_config.fs)
fig, ax = plt.subplots(figsize=(16, 6))
plt.plot(time, wave_data[0])
plt.title('Waveform')
plt.xlabel('Time (seconds)')
plt.ylabel('Amplitude (normed)')
plt.tight_layout()
#保存音频
sf.write(
"output/output-male-hifigan.wav",
wav.numpy(),
samplerate=fastspeech2_config.fs)运行脚本
bash
#运行脚本前，确保有output目录，没有就手动创建一下
python3 FastSpeech2-hifigan.py
#运行成功后在output/output-male-hifigan.wav目录可以找到生成的音频文件
https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install_cn.md2. 飞桨PaddleSpeech语音技术课程 - 飞桨AI Studio星河社区-人工智能学习与实训社区 (baidu.com)
- 更多模型下载