[Python Treasure Box] Rhythm Weaving: A technical symphony of Python speech synthesis library

A wonderful journey into the future of sound: the intersection of deep learning and cloud voice services

Preface

In today's digital era, speech synthesis technology is increasingly used in various fields, from assistive technology to entertainment media, showing great potential. This article will take you deep into the world of speech synthesis, from simple and easy-to-use libraries pyttsx3to deep learning models Wavenet, and gradually explore the subtleties of this field.

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Article directory

A wonderful journey into the future of sound: the intersection of deep learning and cloud voice services

1. puddlex3

1.1 Overview

1.1.1 Introduction

pyttsx3
is a Python library for text-to-speech conversion. It is based on Microsoft
SAPI5
TTS engine and supports multiple languages and speech engines.

1.1.2 Features

Simple and easy to use, suitable for beginners
Supports multiple languages and speech engines
Speech speed and volume can be adjusted

1.2 Use

1.2.1 Installation and configuration

Install using the following command
pyttsx3:

pip install pyttsx3

1.2.2 Basic syntax

import pyttsx3

# 初始化
engine = pyttsx3.init()

# 设置语速
engine.setProperty('rate', 150)

# 设置音量（0.0到1.0）
engine.setProperty('volume', 0.9)

# 将文本转换为语音
text = "Hello, welcome to the world of text-to-speech."
engine.say(text)

# 等待语音输出完成
engine.runAndWait()

1.2.3 Example demonstration

Here's a simple example that converts text to speech and plays it back:

import pyttsx3


def text_to_speech(text):
    engine = pyttsx3.init()
    engine.say(text)
    engine.runAndWait()


text_to_speech("This is an example of pyttsx3 text-to-speech.")

This example will convert the given text into speech and play it back.

1.3 Advanced usage

1.3.1 Change the speech engine

pyttsx3Allows users to select different speech engines to meet specific needs. By default, it uses the Microsoft SAPI5 engine, but you can also choose other available engines. Here is an example:

import pyttsx3

# 获取可用的语音引擎列表
engines = pyttsx3.init().runandwait()
print("Available engines:", engines)

# 选择其中一个引擎
selected_engine = "sapi5"  # 替换为你想要使用的引擎
engine = pyttsx3.init(driverName=selected_engine)

# 继续使用该引擎进行文本到语音转换
text = "You can choose different TTS engines with pyttsx3."
engine.say(text)
engine.runAndWait()

1.3.2 Set voice attributes

In addition to adjusting speaking speed and volume, pyttsx3it also allows setting other voice attributes such as pitch and intonation. Here is an example:

import pyttsx3

engine = pyttsx3.init()

# 设置音调 (范围是0.0到2.0)
engine.setProperty('pitch', 1.5)

# 设置语调 (范围是0.0到1.0)
engine.setProperty('voice', 0.8)

text = "You can customize pitch and voice in pyttsx3."
engine.say(text)
engine.runAndWait()

1.3.3 Save voice output

Sometimes, you may want to convert text to speech and save it as an audio file. pyttsx3Supports saving output as audio file as shown below:

import pyttsx3

engine = pyttsx3.init()

text = "This speech output will be saved as an audio file."
engine.save_to_file(text, 'output.mp3')
engine.runAndWait()

The above code converts text to speech and saves it as an audio file named 'output.mp3'.

1.4 Summary

In this section, we dive into pyttsx3advanced usage of the library, including selecting a different speech engine, setting more speech properties, and saving speech output as an audio file. These advanced usages can help users better customize and control the text-to-speech conversion process. In actual applications, appropriate configurations are selected according to specific needs to improve user experience.

2. gTTS (Google Text-to-Speech)

2.1 Overview

2.1.1 Introduction

gTTS
is a Python library for Google
Text-to-Speech that allows users to convert text to speech, supporting multiple languages and speech options.

2.1.2 Functional features

Using the Google
Text-to-Speech engine
Support multiple languages
Can save speech as audio file

2.2 Use

2.2.1 Installation and configuration

Install using the following command
gTTS:

pip install gtts

2.2.2 Text-to-speech function

from gtts import gTTS
import os


def text_to_speech(text, language='en'):
    tts = gTTS(text=text, lang=language, slow=False)
    tts.save("output.mp3")
    os.system("start output.mp3")


text_to_speech("This is an example of gTTS text-to-speech.", language='en')

This example converts the given text to speech and saves the result as
output.mp3
an audio file named which is then automatically played.

2.2.3 Supported languages and options

gTTS
Multiple languages and options are supported, see the official documentation for details.

2.3 Advanced usage

2.3.1 Adjust voice speed

Similar to pyttsx3, gTTSusers are also allowed to adjust the speed of speech. Below is an example:

from gtts import gTTS
import os

def text_to_speech_with_speed(text, speed=1.5, language='en'):
    tts = gTTS(text=text, lang=language, slow=False)
    # 设置语音速度
    tts.speed = speed
    tts.save("output_speed.mp3")
    os.system("start output_speed.mp3")

text_to_speech_with_speed("Adjusting speech speed with gTTS.", speed=2.0, language='en')

2.3.2 Merge multiple text fragments

Sometimes, you may need to combine multiple text fragments into a single audio file. gTTSMethods are provided concatto implement this functionality:

from  gtts import gTTS
import os

def concatenate_texts_and_save(texts, output_file='concatenated.mp3', language='en'):
    concatenated_text = ' '.join(texts)
    tts = gTTS(text=concatenated_text, lang=language, slow=False)
    tts.save(output_file)
    os.system(f"start {
      
      output_file}")

texts_to_concat = ["This is the first part.", "And this is the second part."]
concatenate_texts_and_save(texts_to_concat)

2.4 Summary

In this section, we detail the gTTSuse of the library, including basic text-to-speech functionality, adjusting speech speed, and advanced usage of merging multiple text fragments. Through these functions, users can more flexibly utilize gTTStext-to-speech conversion and customize it according to actual needs. In practical applications, choose the appropriate language, speed and other options to provide a better voice experience.

3. Festival

3.1 Overview

3.1.1 Introduction

Festival
is a universal text-to-speech synthesis system that supports multiple languages and voices.

3.1.2 Technical background

Using the Festival speech synthesis engine
Provides custom synthesized voices and tones

3.2 Use

3.2.1 Installation and configuration

Install
Festival:

sudo apt-get install festival

Start
Festival
the interactive interface:

festival

3.2.2 Text-to-speech synthesis

Use
Festival
the command line for text-to-speech synthesis:

echo
"Hello, welcome to the world of Festival text-to-speech." | festival - -tts

3.2.3 Introduction to advanced functions

Festival
Supports more advanced features, such as customizing sounds, speech speed, etc. You can get more information by viewing the official documentation .

3.3 Advanced usage

3.3.1 Using Festival API

In addition to the command line, you can also use it in Python through Festival's API. Here's a simple example:

import subprocess

def text_to_speech_with_festival(text):
    process = subprocess.Popen(['festival', '--tts'], stdin=subprocess.PIPE)
    process.communicate(input=text.encode())

text_to_speech_with_festival("Festival provides a powerful text-to-speech synthesis.")

3.3.2 Switch voice model

Festival supports multiple voice models, and you can switch between different voices as needed. Here is an example:

festival

Then execute in the Festival interactive interface:

(voice_rab_diphone)

This will switch to a rab_diphonedifferent speech model called.

3.4 Summary

This section introduces Festivalthe basic concepts and usage of text-to-speech synthesis systems. FestivalThrough the command line and Python API, you can use it for speech synthesis in different scenarios . At the same time, you learned about some advanced features, such as customizing sounds and switching voice models, to better meet personalized needs. In practice, the most suitable speech synthesis tool can be selected according to the specific situation.

4. Tacotron

4.1 Overview

4.1.1 Introduction

Tacotron
is an end-to-end text-to-speech synthesis system designed to generate natural, smooth speech.

4.1.2 Technical principles

Based on deep neural network
Generating spectrograms of speech using attention mechanisms

4.2 Use

4.2.1 Installation and configuration

Tacotron
The installation is more complicated and needs to rely on multiple deep learning frameworks, such as TensorFlow, etc. Detailed installation steps can be found on the Tacotron GitHub page .

4.2.2 Text-to-speech conversion

# 使用Tacotron进行文本到语音转换的示例代码
# （请注意，这里的代码仅为演示，实际使用可能需要更多设置和依赖）

import tensorflow as tf
from  tacotron.synthesizer import Synthesizer

# 初始化Tacotron合成器
synthesizer = Synthesizer()

# 将文本转换为语音
text = "Hello, welcome to the world of Tacotron text-to-speech."
audio = synthesizer.synthesize(text)

# 播放生成的语音
# （此处可能需要额外的音频库和设置，例如pygame、pydub等）

4.2.3 Advanced parameter settings

Tacotron
It has many advanced parameters, such as training models, adjusting sound styles, etc. You can learn more by consulting its official documentation .

4.3 Advanced usage

4.3.1 Style migration

TacotronAllows users to achieve speech style migration by adjusting model parameters, making the generated speech more consistent with a specific style or emotion. Here's a simple example:

# Tacotron风格迁移示例代码
# （请注意，这里的代码仅为演示，实际使用可能需要更多设置和依赖）

import tensorflow as tf
from tacotron.synthesizer import Synthesizer

# 初始化Tacotron合成器
synthesizer = Synthesizer()

# 将文本转换为语音并同时进行风格迁移
text = "Hello, welcome to the world of Tacotron text-to-speech with style transfer."
audio = synthesizer.synthesize(text, style='happy')  # 通过style参数指定风格

# 播放生成的语音
# （此处可能需要额外的音频库和设置，例如pygame、pydub等）

4.3.2 Multi-language support

TacotronIt is designed to support multiple languages and can generate voices in different languages by specifying language parameters. Here is an example:

# Tacotron多语言支持示例代码
# （请注意，这里的代码仅为演示，实际使用可能需要更多设置和依赖）

import tensorflow as tf
from  tacotron.synthesizer import Synthesizer

# 初始化Tacotron合成器
synthesizer = Synthesizer()

# 将文本转换为不同语言的语音
text_english = "Hello, welcome to the world of Tacotron text-to-speech in English."
audio_english = synthesizer.synthesize(text_english, language='en')

text_french = "Bonjour, bienvenue dans le monde de la synthèse vocale Tacotron en français."
audio_french = synthesizer.synthesize(text_french, language='fr')

# 播放生成的语音
# （此处可能需要额外的音频库和设置，例如pygame、pydub等）

4.4 Summary

This section details Tacotronthe concepts, technical principles, and basic usage of the end-to-end text-to-speech synthesis system. Learned the intricacies of installation and configuration, as well as how to use basic text-to-speech functionality. At the same time, some advanced usages are demonstrated, such as style migration and multi-language support, which provide more personalization and customization options. In actual use, it is very important to choose the appropriate speech synthesis tool based on needs and application scenarios.

5. Wavenet

5.1 Overview

5.1.1 Introduction

Wavenet
It is a deep neural network speech synthesis model developed by DeepMind and is designed to generate high-quality natural speech.

5.1.2 Principles and Innovation

Based on deep convolutional neural network
Using WaveNet to generate speech waveforms with high fidelity

5.2 Use

5.2.1 Installation and configuration

Wavenet
The installation can be relatively complicated because it relies on deep learning libraries such as TensorFlow. Detailed installation steps can be found on DeepMind’s WaveNet GitHub page
.

5.2.2 Audio generation

# 使用Wavenet进行音频生成的示例代码
# （请注意，这里的代码仅为演示，实际使用可能需要更多设置和依赖）

import tensorflow as tf
from wavenet_vocoder import vocoder

# 初始化Wavenet声码器
vocoder_instance = vocoder.WaveNetVocoder()

# 生成语音波形
text = "Hello, welcome to the world of Wavenet text-to-speech."
waveform = vocoder_instance.generate_waveform(text)

# 播放生成的语音
# （此处可能需要额外的音频库和设置，例如pygame、pydub等）

5.2.3 Advanced application scenarios

Wavenet
Supports many advanced application scenarios, such as customizing sounds, adjusting audio quality, etc. Detailed information can be found in WaveNet's official documentation .

5.3 Advanced usage

5.3.1 Customizing sound characteristics

WavenetAllows users to customize the sound characteristics of the generated speech, including pitch, speaking speed, etc., by adjusting model parameters. Here's a simple example:

# Wavenet定制声音特性示例代码
# （请注意，这里的代码仅为演示，实际使用可能需要更多设置和依赖）

import tensorflow as tf
from wavenet_vocoder import vocoder

# 初始化Wavenet声码器
vocoder_instance = vocoder.WaveNetVocoder()

# 生成定制声音特性的语音波形
text = "Hello, welcome to the world of customized Wavenet text-to-speech."
waveform = vocoder_instance.generate_waveform(text, pitch=0.5, speed=1.2)

# 播放生成的语音
# （此处可能需要额外的音频库和设置，例如pygame、pydub等）

5.3.2 High-quality audio generation

By adjusting Wavenetsome parameters, higher-quality audio generation can be achieved. Here is an example:

# Wavenet高音质音频生成示例代码
# （请注意，这里的代码仅为演示，实际使用可能需要更多设置和依赖）

import tensorflow as tf
from wavenet_vocoder import vocoder

# 初始化Wavenet声码器
vocoder_instance = vocoder.WaveNetVocoder()

# 生成高音质的语音波形
text = "Hello, welcome to the world of high-quality Wavenet text-to-speech."
waveform = vocoder_instance.generate_waveform(text, quality=3)  # 调整quality参数

# 播放生成的语音
# （此处可能需要额外的音频库和设置，例如pygame、pydub等）

5.4 Summary

This section introduces in detail Wavenetthe concepts, principles and basic usage methods of the deep neural network speech synthesis model. Learned the intricacies of installation and configuration, as well as how to use basic audio generation functionality. At the same time, some advanced uses are demonstrated, such as customizing sound characteristics and generating high-quality audio, which provide more personalization and customization options. In actual use, it is very important to choose the appropriate speech synthesis tool based on needs and application scenarios.

6. Baidu AIP (Baidu speech synthesis)

6.1 Overview

6.1.1 Introduction

Baidu speech synthesis (Baidu
AIP) is a speech synthesis service provided by Baidu that allows developers to convert text into speech through API calls.

6.1.2 API function overview

Provide simple and easy-to-use API interface
Supports multiple languages and timbre selections
Speech synthesis results can be saved as audio files

6.2 Use

6.2.1 Registration and configuration

Register an account on Baidu AI open platform and create an application to obtain API Key and Secret Key.
Install Baidu AIP Python SDK:

pip install baidu-aip

6.2.2 Call the interface to implement speech synthesis

from aip import AipSpeech


def text_to_speech_baidu(text, app_id, api_key, secret_key, lang='zh', speed=5, pit=5, vol=5, per=0):
    client = AipSpeech(app_id, api_key, secret_key)

    result = client.synthesis(text, 'zh' if lang == 'zh' else 'en', 1, {
    
    
        'spd': speed, 'pit': pit,
        'vol': vol, 'per': per
    })

    if not isinstance(result, dict):
        with open('output_baidu.mp3', 'wb') as f:
            f.write(result)
        # 播放生成的语音
        # （此处可能需要额外的音频库和设置，例如pygame、pydub等）


# 在此填入您在百度AI开放平台创建应用时获得的App ID、API Key和Secret Key
app_id = 'your_app_id'
api_key = 'your_api_key'
secret_key = 'your_secret_key'

text_to_speech_baidu("百度语音合成示例", app_id, api_key, secret_key, lang='zh')

6.2.3 Advanced features and customization options

Baidu speech synthesis API supports adjusting speech speed, pitch, volume and other parameters. For specific parameters and value ranges, please refer to Baidu speech synthesis documentation .

6.3 Advanced usage

6.3.1 Support SSML

Baidu speech synthesis API supports SSML (Speech Synthesis Markup Language). By using SSML, users can more flexibly control the effect of speech synthesis. Here's a simple example:

from aip import AipSpeech

def text_to_speech_baidu_ssml(text, app_id, api_key, secret_key):
    client = AipSpeech(app_id, api_key, secret_key)

    ssml_text = f"<speak>{
      
      text}</speak>"

    result = client.synthesis(ssml_text, 'zh', 1, {
    
    
        'cuid': 'example_user',
        'per': 0
    })

    if not isinstance(result, dict):
        with open('output_baidu_ssml.mp3', 'wb') as f:
            f.write(result)
        # 播放生成的语音
        # （此处可能需要额外的音频库和设置，例如pygame、pydub等）

# 在此填入您在百度AI开放平台创建应用时获得的App ID、API Key和Secret Key
app_id = 'your_app_id'
api_key = 'your_api_key'
secret_key = 'your_secret_key'

text_to_speech_baidu_ssml("百度语音合成示例，<prosody rate='fast'>语速加快</prosody>，<prosody volume='loud'>音量提高</prosody>", app_id, api_key, secret_key)

6.3.2 Synthesize multiple text fragments

Baidu speech synthesis API allows multiple text segments to be synthesized and spliced into an audio file, which allows for more flexible control of speech output. Here's a simple example:

from   aip import AipSpeech

def concatenate_texts_and_save_baidu(texts, output_file, app_id, api_key, secret_key):
    client = AipSpeech(app_id, api_key, secret_key)

    ssml_texts = [f"<speak>{
      
      text}</speak>" for text in texts]
    ssml_text = ''.join(ssml_texts)

    result = client.synthesis(ssml_text, 'zh', 1, {
    
    
        'cuid': 'example_user',
        'per': 0
    })

    if not isinstance(result, dict):
        with open(output_file, 'wb') as f:
            f.write(result)
        # 播放生成的语音
        # （此处可能需要额外的音频库和设置，例如pygame、pydub等）

# 在此填入您在百度AI开放平台创建应用时获得的App ID、API Key和Secret Key
app_id = 'your_app_id'
api_key = 'your_api_key'
secret_key = 'your_secret_key'

texts_to_concat = ["百度语音合成示例第一段", "百度语音合成示例第二段"]
concatenate_texts_and_save_baidu(texts_to_concat, 'output_baidu_concat.mp3', app_id, api_key, secret_key)

6.4 Summary

In this section, we introduce in detail the basic concepts of Baidu speech synthesis (Baidu AIP), how to use the API, and some advanced functions and customization options. Through the Baidu speech synthesis API, developers can quickly convert text to speech and achieve more flexible speech output effects by adjusting parameters and using SSML. In practical applications, appropriate speech synthesis tools can be selected according to specific needs.

7. Microsoft Azure Speech

7.1 Overview

7.1.1 Introduction

Microsoft
Azure
Speech is a speech service provided by Microsoft that includes speech synthesis capabilities to convert text into natural speech.

7.1.2 Main functions

Supports multiple languages and voices
Provide high-quality speech synthesis services
Synthesized speech can be saved as an audio file

7.2 Use

7.2.1 Registration and configuration

Register and create a voice service resource in the Azure portal .
Get the key and endpoint of the voice service resource.

7.2.2 Text-to-speech service

import os
from azure.cognitiveservices.speech import SpeechConfig, AudioConfig, SpeechSynthesizer


def text_to_speech_azure(text, subscription_key, region='eastus'):
    speech_config = SpeechConfig(subscription=subscription_key, region=region)
    audio_config = AudioConfig(use_default_speaker=True)

    synthesizer = SpeechSynthesizer(speech_config=speech_config, audio_config=audio_config)
    synthesizer.speak_text_async(text).get()


# 在此填入您在Azure门户创建语音服务资源时获得的订阅密钥和区域
subscription_key = 'your_subscription_key'
region = 'your_region'

text_to_speech_azure("Microsoft Azure Speech合成语音示例", subscription_key, region)

7.2.3 Advanced speech processing functions

Microsoft Azure Speech provides a wealth of advanced features, such as custom pronunciation, voice effects, etc. For more details, please refer to the official documentation .

7.3 Advanced usage

7.3.1 Using SSML

Microsoft Azure Speech supports the use of SSML (Speech Synthesis Markup Language) to customize speech output. Here's a simple example:

import os
from azure.cognitiveservices.speech import SpeechConfig, AudioConfig, SpeechSynthesizer


def text_to_speech_azure_ssml(text, subscription_key, region='eastus'):
    speech_config = SpeechConfig(subscription=subscription_key, region=region)
    audio_config = AudioConfig(use_default_speaker=True)

    synthesizer = SpeechSynthesizer(speech_config=speech_config, audio_config=audio_config)

    ssml_text = f"<speak version='1.0' xmlns='http://www.w3.org/2001/10/synthesis' xml:lang='en-US'>{
      
      text}</speak>"
    synthesizer.speak_ssml_async(ssml_text).get()


# 在此填入您在Azure门户创建语音服务资源时获得的订阅密钥和区域
subscription_key = 'your_subscription_key'
region = 'your_region'

text_to_speech_azure_ssml("Microsoft Azure Speech合成语音示例，<prosody rate='fast'>语速加快</prosody>，<prosody volume='loud'>音量提高</prosody>", subscription_key, region)

7.3.2 Synthesizing multiple audio clips

With Microsoft Azure Speech, multiple audio clips can be synthesized and saved as one audio file to achieve more flexible speech output. Here is an example:

import os
from  azure.cognitiveservices.speech import SpeechConfig, AudioConfig, SpeechSynthesizer


def concatenate_texts_and_save_azure(texts, output_file, subscription_key, region='eastus'):
    speech_config = SpeechConfig(subscription=subscription_key, region=region)
    audio_config = AudioConfig(filename=output_file)

    synthesizer = SpeechSynthesizer(speech_config=speech_config, audio_config=audio_config)

    for text in texts:
        synthesizer.speak_text_async(text).get()


# 在此填入您在Azure门户创建语音服务资源时获得的订阅密钥和区域
subscription_key = 'your_subscription_key'
region = 'your_region'

texts_to_concat = ["Microsoft Azure Speech合成语音示例第一段", "Microsoft Azure Speech合成语音示例第二段"]
concatenate_texts_and_save_azure(texts_to_concat, 'output_azure_concat.wav', subscription_key, region)

7.4 Summary

In this section, we introduce in detail the basic concepts, usage and some advanced functions of the Microsoft Azure Speech speech synthesis service. Through the Azure Speech service, developers can easily convert text to speech and customize speech output more flexibly according to needs. In practical applications, it is very important to choose the appropriate speech synthesis tool according to specific needs.

Summarize

By reading this article, readers will have an in-depth understanding of various speech synthesis tools. pyttsx3As a simple and easy-to-use solution, it is suitable for beginners; gTTSleverages Google's powerful speech engine, supports multiple languages; Festivalprovides more customization options; Tacotronand Wavenetrepresents the latest progress in deep learning. In addition, cloud services provided by Baidu and Microsoft also provide developers with convenient solutions.