自制有声书阅读器：用PaddleSpeech打开读书新方式

吕声辉，飞桨开发者技术专家（PPDE），某网络科技公司研发工程师。主要研究方向为图像识别，自然语言处理等。 • AI Studio主页
https://aistudio.baidu.com/aistudio/personalcenter/thirdview/227158

项目背景

随着互联网的发展，普通用户对于书籍展示形式的需求已由纯文字变成了图文、语音、视频等多种形式，因此将文本书籍转换为有声读物具有很大的市场需求。本文以飞桨语音模型库PaddleSpeech提供的语音合成技术为核心，通过音色克隆、语速设置、音量调整等附加功能，展示有声书籍的技术可行方案。
在这里插入图片描述
最终呈现效果如
player.bilibili.com/player.html?bvid=BV1x84y1V7SR

网页体验访问地址
https://book.weixin12306.com/

环境准备

PaddleSpeech 是基于飞桨的语音方向开源模型库，用于语音和音频中的各种关键任务的开发，包含大量基于深度学习的前沿和有影响力的模型。首先进行PaddleSpeech安装环境的配置，配置如下：

# 注意如果之前运行过这步 下次就不用再运行了，这个目录重启项目也不会清空的
# 下载解压说话人编码器
!wget -P data https://bj.bcebos.com/paddlespeech/Parakeet/released_models/ge2e/ge2e_ckpt_0.3.zip
!unzip -o -d work data/ge2e_ckpt_0.3.zip
# 下载解压声码器
!wget -P data https://paddlespeech.bj.bcebos.com/Parakeet/released_models/pwgan/pwg_aishell3_ckpt_0.5.zip
!unzip -o -d work data/pwg_aishell3_ckpt_0.5.zip
# 下载解压声学模型
!wget -P data https://paddlespeech.bj.bcebos.com/Parakeet/released_models/fastspeech2/fastspeech2_nosil_aishell3_vc1_ckpt_0.5.zip
!unzip -o -d work data/fastspeech2_nosil_aishell3_vc1_ckpt_0.5.zip
#  下载解压nltk包
!wget -P data https://paddlespeech.bj.bcebos.com/Parakeet/tools/nltk_data.tar.gz
!tar zxvf data/nltk_data.tar.gz

# 安装PaddleSpeech
!pip install pytest-runner
!pip install paddlespeech

# 将nltk_data 拷贝到 /home/aistudio 目录
!cp -r /home/aistudio/work/nltk_data /home/aistudio

# 安装moviepy 
!pip install moviepy==1.0.3

数据处理

每本书的内容均以json格式存放在txt文本中，路径为
/work/books/inputs/bookname.txt。为方便演示，这里以三国演义为例。

{
   “name”: “三国演义”, 
   “lists”: [{
      “title”: “第一回 宴桃园豪杰三结义 斩黄巾英雄首立功”
      “content”: “滚滚长江东逝水，浪花淘尽英雄。是非成败转头空。青山依
}, {
      “title”: “第二回 张翼德怒鞭督邮 何国舅谋诛宦竖”,
      “content”: “且说董卓字仲颖，陇西临洮人也，官拜河东太守，自来骄傲
   }]
}

音频合成

段落句子分割

以换行符"\n"分割为段落，以"。"分割为句子。

# 段落和句子分割
def lists(self, lists):
    results = []
    for i in range(len(lists)):
        item = lists[i]
        title = item['title']
        content = item['content']
        sections = []
        sentences = []
        contents = content.split('\n')
        for citem in contents:
            if len(citem) > 1:
                sections.append(citem)

        sentenceIndex = 0
        for sitems in sections:
            sitems_ = []
            for tmp  in sitems.split('。'):
                if len(tmp) > 1:
                    sitems_.append(tmp)

            for j in range(len(sitems_)):
                sentence = {
                    'id':sentenceIndex,
                    'sentence': sitems_[j],
                    'end': 0 if j < len(sitems_) - 1 else 1
                }
                sentences.append(sentence)
                sentenceIndex += 1
        result = {
            'id':i,
            'title':title,
            'sentences':sentences
        }
        results.append(result)
    return results

特殊字符处理

在国学书籍中，有可能出现很多生僻字或者特殊符号，这里需要做针对性的替换。

# 特殊处理示例，工程化最好用字典自动判断替换
def dealText(self, text):
    text = text.replace('-','')
    text = text.replace(' ', '')
    text = text.replace('’','')
    text = text.replace('﨑','崎')
    text = text.replace("[",' ')
    text = text.replace("]",' ')
    text = text.replace('　',' ')
    text = text.replace("，]","")
    text = text.replace("１","1")
    text = text.replace("２",'2')
    text = text.replace("６","6")
    text = text.replace("〔","")
    text = text.replace("─","")
    text = text.replace("┬","")
    text = text.replace("┼","")
    text = text.replace("┴","")
    text = text.replace("〖"," ")
    text = text.replace("〗"," ")
    text = text.replace("礻殳","祋")
    return text

音频合成

根据分割的ID，保存到对应位置。

# 音频合成
def audio(self, contents):
    self.tts = TTSExecutor()
    for i in range(len(contents['lists'])):
        item = contents['lists'][i]
        basePath = self.bookPathOutput+'/'+self.bookname+'/'+str(i)
        if os.path.exists(basePath) is False:
            os.makedirs(r''+basePath)

        # 生成每回标题音频
        self.text2audio(item['title'], basePath+'/title.wav')

        # 生成每句内容音频
        for j in range(len(item['sentences'])):
            sitem = item['sentences'][j]
            self.text2audio(sitem['sentence'], basePath+'/'+str(sitem['id'])+'.wav')

def text2audio(self, text, path):
    text = self.dealText(text)
    self.voice_cloning(text, path)
#self.tts(text=text, output=path)

音色克隆

可以事先将不同音色音频放置在 /work/sounds 目录下。此处音色克隆部分的功能主要参考自PaddleSpeech语音克隆项目。

项目链接
https://aistudio.baidu.com/aistudio/projectdetail/4265795?channelType=0&channel=0

def clone_pre(self):

        # Init body.
        with open(self.am_config) as f:
            am_config = CfgNode(yaml.safe_load(f))

        self.am_config_ = am_config
        with open(self.voc_config) as f:
            voc_config = CfgNode(yaml.safe_load(f))

        # speaker encoder
        p = SpeakerVerificationPreprocessor(
            sampling_rate=16000,
            audio_norm_target_dBFS=-30,
            vad_window_length=30,
            vad_moving_average_width=8,
            vad_max_silence_length=6,
            mel_window_length=25,
            mel_window_step=10,
            n_mels=40,
            partial_n_frames=160,
            min_pad_coverage=0.75,
            partial_overlap_ratio=0.5)
        print("Audio Processor Done!")
        self.p = p

        speaker_encoder = LSTMSpeakerEncoder(
            n_mels=40, num_layers=3, hidden_size=256, output_size=256)
        speaker_encoder.set_state_dict(paddle.load(self.ge2e_params_path))
        speaker_encoder.eval()
        self.speaker_encoder = speaker_encoder
        print("GE2E Done!")

        with open(self.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)

        # acoustic model
        odim = am_config.n_mels
        # model: {model_name}_{dataset}
        am_name = self.am[:self.am.rindex('_')]
        am_dataset = self.am[self.am.rindex('_') + 1:]

        am_class = dynamic_import(am_name, self.model_alias)
        am_inference_class = dynamic_import(
            am_name + '_inference', self.model_alias)

      if am_name == 'fastspeech2':
            am = am_class(
                idim=vocab_size, odim=odim, spk_num=None, **am_config["model"])
        elif am_name == 'tacotron2':
            am = am_class(idim=vocab_size, odim=odim, **am_config["model"])

        am.set_state_dict(paddle.load(self.am_ckpt)["main_params"])
        am.eval()
        am_mu, am_std = np.load(self.am_stat)
        am_mu = paddle.to_tensor(am_mu)
        am_std = paddle.to_tensor(am_std)
        am_normalizer = ZScore(am_mu, am_std)
        am_inference = am_inference_class(am_normalizer, am)
        am_inference.eval()
        self.am_inference = am_inference
        print("acoustic model done!")


        # vocoder
        # model: {model_name}_{dataset}
        voc_name = self.voc[:self.voc.rindex('_')]
        voc_class = dynamic_import(voc_name, self.model_alias)
        voc_inference_class = dynamic_import(
            voc_name + '_inference', self.model_alias)
        voc = voc_class(**voc_config["generator_params"])
        voc.set_state_dict(paddle.load(self.voc_ckpt)["generator_params"])
        voc.remove_weight_norm()
        voc.eval()
        voc_mu, voc_std = np.load(self.voc_stat)
        voc_mu = paddle.to_tensor(voc_mu)
        voc_std = paddle.to_tensor(voc_std)
        voc_normalizer = ZScore(voc_mu, voc_std)
        voc_inference = voc_inference_class(voc_normalizer, voc)
        voc_inference.eval()
        self.voc_inference = voc_inference
        print("voc done!")

        self.frontend = Frontend(phone_vocab_path=self.phones_dict)
        print("frontend done!")

        # 获取音色
        ref_audio_path = self.soundsInput+'/'+str(self.sound)+'.mp3'
        mel_sequences = self.p.extract_mel_partials(self.p.preprocess_wav(ref_audio_path))
        # print("mel_sequences: ", mel_sequences.shape)
        with paddle.no_grad():
            spk_emb = self.speaker_encoder.embed_utterance(paddle.to_tensor(mel_sequences))
        # print("spk_emb shape: ", spk_emb.shape)
        self.spk_emb = spk_emb

def voice_cloning(self, text, path):

    input_ids = self.frontend.get_input_ids(text, merge_sentences=True)
    phone_ids = input_ids["phone_ids"][0]

    with paddle.no_grad():
        wav = self.voc_inference(self.am_inference(phone_ids, spk_emb=self.spk_emb))

    sf.write(path, wav.numpy(), samplerate=self.am_config_.fs)

语速和音量调整

def post_del(self, path):
    old_au = AudioFileClip(path)
    new_au = old_au.fl_time(lambda t:  self.speed*t, apply_to=['mask', 'audio'])
    new_au = new_au.set_duration(old_au.duration/self.speed)
    new_au = (new_au.fx(afx.volumex, self.volumex))
    final_path = path.replace('outputs','final')
    print(path, final_path)
    new_au.write_audiofile(final_path)
    print('^^^^^^')

音色、语速和音量需要在 main.py 的头部中设置。

class Main(object):

    def __init__(self):
        self.bookPathInput = './books/inputs' # 书籍输入目录
        self.bookPathOutput = './books/outputs' # 常规输出目录
        self.bookPathFinal = './books/final' # 最终输出目录
        self.bookname = 'sanguoyanyi'
        self.tts = None
        self.soundsInput = './sounds' # 音色文件存放目录
        self.sound = '001' # 音色编号
        self.speed = 1.0 # 语速
        self.volumex = 1.1 # 音量

# 音频合成，一键命令
%cd /home/aistudio/work/
!python main.py

查看生成结果

最终切分好的数据在
/work/outputs/sanguoyanyi目录下，原始语速和音量音频在outputs目录下，指定语速和音量音频在final目录下。其中的outputs.txt为切分内容，而音频会按照每个章节以及每个章节的句子索引排序好。

以下为outputs.txt 内容：

{
   “name”: “三国演义”,
   “lists”: [{
      “id”: 0,
      “title”: “第一回 宴桃园豪杰三结义 斩黄巾英雄首立功”, 
      “sentence”: [{
         “id”: 0
         “sentence”: “滚滚长江东逝水，浪花淘尽英雄”,
         “end”: 0
   }, {
      “id”: 1,
      “sentence”: “是否成败转头空”,
      “end”: 0
   }, {
      “id”: 2,
      “sentence”: “青山依旧在，几度夕阳红”,
      “end”: 0
   }, {
      “id”: 3,
      “sentence”: “白发渔樵江渚上，惯看秋月春风”,
      “end”: 0
}, {
      “id”: 4,
      “sentence”: “一壶浊酒喜相逢”,
      “end”: 0
}, {

以下为第一回的每个句子wav格式音频。

在这里插入图片描述

客户端展示

输出第三部分生成好的内容和音频。这里用H5页面简单展示一下有声书阅读的效果，包括内容展示和逐句朗读高亮两种功能。

用PaddleSpeech实现有声书阅读

H5的具体代码已放在GitHub 上，大家可在下方链接中查看
https://github.com/lvsh2012/book2audio

手机或者PC也可直接体验
https://book.weixin12306.com/

总结

通过PaddleSpeech可以简单快速地实现语音合成功能，轻松实现书籍有声化。使用者在这里需要关注下，当以H5展示播放效果时，需要注意内容和音频的对应关系。除了语音合成功能外，PaddleSpeech还提供了包括语音识别、声纹提取、标点恢复等其他功能。相信大家基于PaddleSpeech可以在该领域挖掘出更多的可能性！