基于Python PaddleSpeech怎么实现语音文字处理，针对这个问题，这篇文章详细介绍了相对应的分析和解答，希望可以帮助更多想解决这个问题的小伙伴找到更简单易行的方法。
环境安装
首先我们看一下项目结构以及安装文档。
需要Python3.7以上、C++环境、requirements安装等等，下面按照我的顺序说一下。
conda安装Python3.9虚拟环境
使用conda安装python3.9环境，命令如下。
conda create -n py39 python=3.9安装Visual Studio 2019
安装地址: Microsoft C++ 生成工具 - Visual Studio
注意安装的时候需要勾选C++桌面开发。
安装requirements.txt
使用命令安装requiremets.txt，命令如下：
pip install -r requirements.txt -i https://pypi.douban.com/simple这里要注意一下，paddlespeech_ctcdecoders安装失败的话无所谓，可以略掉。
安装paddlepaddle和paddlespeech
命令如下：
pip install paddlepaddle -i https://mirror.baidu.com/pypi/simplepip install paddlespeech -i https://pypi.tuna.tsinghua.edu.cn/simplenltk_data下载
按照项目安装文档内的说明。
我的本地目录地址如下
项目验证
我下面分别验证一下tts、asr以及标点恢复功能。
tts语音合成
使用命令如下：
paddlespeech tts --input "南京现在很冷，下次再去夫子庙吧。" --output C:\Users\xxx\Desktop\115.wav执行过程
(dh_partner) D:\spyder\PaddleSpeech>paddlespeech tts --input "南京现在很冷，下次再去夫子庙吧。" --output C:\Users\xxx\Desktop\115.wavphones_dict: None[2022-01-05 17:23:43,642] [ INFO] [log.py] [L57] - File C:\Users\huyi\.paddlespeech\models\fastspeech3_csmsc-zh\fastspeech3_nosil_baker_ckpt_0.4.zip md5 checking...[2022-01-05 17:23:44,742] [ INFO] [log.py] [L57] - Use pretrained model stored in: C:\Users\huyi\.paddlespeech\models\fastspeech3_csmsc-zh\fastspeech3_nosil_baker_ckpt_0.4self.phones_dict: C:\Users\huyi\.paddlespeech\models\fastspeech3_csmsc-zh\fastspeech3_nosil_baker_ckpt_0.4\phone_id_map.txt[2022-01-05 17:23:44,743] [ INFO] [log.py] [L57] - C:\Users\huyi\.paddlespeech\models\fastspeech3_csmsc-zh\fastspeech3_nosil_baker_ckpt_0.4[2022-01-05 17:23:44,744] [ INFO] [log.py] [L57] - C:\Users\huyi\.paddlespeech\models\fastspeech3_csmsc-zh\fastspeech3_nosil_baker_ckpt_0.4\default.yaml[2022-01-05 17:23:44,744] [ INFO] [log.py] [L57] - C:\Users\huyi\.paddlespeech\models\fastspeech3_csmsc-zh\fastspeech3_nosil_baker_ckpt_0.4\snapshot_iter_76000.pdzself.phones_dict: C:\Users\huyi\.paddlespeech\models\fastspeech3_csmsc-zh\fastspeech3_nosil_baker_ckpt_0.4\phone_id_map.txt[2022-01-05 17:23:44,745] [ INFO] [log.py] [L57] - File C:\Users\huyi\.paddlespeech\models\pwgan_csmsc-zh\pwg_baker_ckpt_0.4.zip md5 checking...[2022-01-05 17:23:44,782] [ INFO] [log.py] [L57] - Use pretrained model stored in: C:\Users\huyi\.paddlespeech\models\pwgan_csmsc-zh\pwg_baker_ckpt_0.4[2022-01-05 17:23:44,783] [ INFO] [log.py] [L57] - C:\Users\huyi\.paddlespeech\models\pwgan_csmsc-zh\pwg_baker_ckpt_0.4[2022-01-05 17:23:44,783] [ INFO] [log.py] [L57] - C:\Users\huyi\.paddlespeech\models\pwgan_csmsc-zh\pwg_baker_ckpt_0.4\pwg_default.yaml[2022-01-05 17:23:44,785] [ INFO] [log.py] [L57] - C:\Users\huyi\.paddlespeech\models\pwgan_csmsc-zh\pwg_baker_ckpt_0.4\pwg_snapshot_iter_400000.pdzvocab_size: 268frontend done!encoder_type is transformerdecoder_type is transformerC:\Users\huyi\.conda\envs\dh_partner\lib\site-packages\paddle\framework\io.py:415: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3, and in 3.10 it will stop working if isinstance(obj, collections.Iterable) and not isinstance(obj, (acoustic model done!voc done!Building prefix dict from the default dictionary ...[2022-01-05 17:23:51] [DEBUG] [__init__.py:113] Building prefix dict from the default dictionary ...Loading model from cache C:\Users\huyi\AppData\Local\Temp\jieba.cache[2022-01-05 17:23:51] [DEBUG] [__init__.py:132] Loading model from cache C:\Users\huyi\AppData\Local\Temp\jieba.cacheLoading model cost 0.659 seconds.[2022-01-05 17:23:52] [DEBUG] [__init__.py:164] Loading model cost 0.659 seconds.Prefix dict has been built successfully.[2022-01-05 17:23:52] [DEBUG] [__init__.py:166] Prefix dict has been built successfully.C:\Users\huyi\.conda\envs\dh_partner\lib\site-packages\paddle\fluid\dygraph\math_op_patch.py:251: UserWarning: The dtype of left and right variables are not the same, left dtype is paddle.int64, but right dtype is paddle.int32, the right dtype will convert to paddle.int64 warnings.warn([2022-01-05 17:23:58,811] [ INFO] [log.py] [L57] - Wave file has been generated: C:\Users\xxx\Desktop\115.wav生成的音频如下
asr语音识别
我就使用了tts生成的音频进行asr识别，看看效果，命令如下:
paddlespeech asr --lang zh --input C:\Users\xxx\Desktop\115.wav执行结果如下
可以看到最后打印的内容是没有标点的文字输出，还是比较准的。
标点恢复
就用这句话试试标点恢复的情况，命令如下：
paddlespeech text --task punc --input 南京现在很冷下次再去夫子庙吧执行结果
关于基于Python PaddleSpeech怎么实现语音文字处理问题的解答就分享到这里了，希望以上内容可以对大家有一定的帮助，如果你还有很多疑惑没有解开，可以关注编程网行业资讯频道了解更多相关知识。
