Best Text to Speech Python APIs Building a stellar app that requires text to speech? We listed the best text to speech Python APIs to get you started quickly.

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October 1, 2024 10 min read
Best Text to Speech Python APIs

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Text to speech (TTS) technology has become a critical feature for modern apps & live streams, especially when you want to create accessibility features, interactive voice experiences, or audio content. Let’s review the Best Text to Speech Python APIs.

For Python developers, integrating a TTS API into your project can be a powerful way to convert text into high-quality, real-time audio. In this post, I’ll guide you through the best text-to-speech APIs available, including code snippets to get you started with each.

Why Python Text to Speech is a Great Programming Language?

Python is one of the best programming languages for implementing text-to-speech (TTS) features for several reasons:

  1. Simplicity and Ease of Use: Python’s clean syntax and readable code make it accessible to developers of all levels, which is particularly useful when integrating complex technologies like TTS.
  2. Rich Ecosystem of Libraries: Python has a large ecosystem of libraries that support TTS directly, such as pyttsx3, gTTS, and more advanced options like Coqui TTS. These libraries often come with simple APIs, making them easy to implement without extensive boilerplate code.
  3. Extensive API Support: Many cloud-based TTS providers, like PlayHT, Google Cloud, Amazon Polly, and ElevenLabs, offer Python SDKs or REST APIs with Python wrappers, which means you can easily integrate these services into your Python projects.
  4. Cross-Platform: Python works across different operating systems like Windows, macOS, and Linux, which is ideal when building apps that need to run across various platforms while maintaining consistency in TTS functionality.
  5. Machine Learning Integration: Python is the leading language for AI and machine learning, and TTS systems today increasingly leverage deep learning for voice synthesis. Libraries like TensorFlow and PyTorch, which are predominantly used with Python, allow developers to integrate TTS models and even fine-tune them if necessary.
  6. Automation and Scripting: Python’s versatility in automation and scripting makes it an excellent choice for building workflows that incorporate speech synthesis, such as generating audio files for large datasets, creating voiceovers for video content, or developing interactive bots.

Other Popular Programming Languages for TTS APIs

While Python is great, other programming languages are also widely used for TTS integrations:

  1. JavaScript
  2. Java
  3. C# (C-Sharp)
  4. Go (Golang)
  5. Swift
  6. C++

Python 3 is packed with goodies

  1. Performance Enhancements: Features like Just-In-Time (JIT) compilation and a free-threaded build mode in Python 3.13 boost the performance of real-time TTS applications by speeding up execution and allowing better concurrency.
  2. Advanced Typing and Syntax: Python 3’s improvements in typing and an upgraded interactive interpreter make TTS code more robust and easier to debug, especially when working with complex speech synthesis models.
  3. Machine Learning Integration: Python 3 works seamlessly with machine learning libraries like TensorFlow and PyTorch, which are often used to develop and deploy advanced speech synthesis models.
  4. Cross-Platform Support: Python 3 runs smoothly across Windows, macOS, Linux, and even has enhanced support for mobile platforms like iOS and Android.
  5. Extensive API Support: Most leading TTS APIs, such as PlayHT, Google Cloud TTS, and Amazon Polly, provide great support for Python 3, making integration easy and efficient.

These features make Python 3 an excellent choice for building scalable, high-performance TTS applications.

Alright, enough with our homage to Python.

Here’s the list of the best text to speech providers with Python APIs

1. PlayHT: Best TTS for Low Latency and High-Quality Voices

PlayHT stands out for its ultra-low latency and exceptional voice quality. If you’re building apps that demand real-time speech synthesis—like live streams, voice bots, or interactive experiences—PlayHT’s API is your go-to. It combines machine learning algorithms with top-tier speech synthesis to generate some of the most realistic voices available.

Features:

  1. Ultra-low latency: Perfect for real-time applications.
  2. High-quality, natural-sounding voices.
  3. Supports various languages and accents.
  4. API documentation is easy to follow and well-documented on GitHub.
  5. Get access to text to speech, voice cloning, & multi lingual voices.
  6. Also, enjoy native accents in English and other languages.

Sample Code:

import requests

API_KEY = 'your_playht_api_key'

URL = "https://playht-api.com/api/convert"

def text_to_speech(text, voice="en_us_male"):

    payload = {

        'text': text,

        'voice': voice,

        'format': 'wav'

    }

    headers = {

        'Authorization': f'Bearer {API_KEY}',

        'Content-Type': 'application/json'

    }

    response = requests.post(URL, json=payload, headers=headers)

    with open('output.wav', 'wb') as audio_file:

        audio_file.write(response.content)

text_to_speech("Hello, world! This is a test of PlayHT API.")

The code above converts text to speech and saves it as a wav file. With PlayHT, developers can focus on building the features that matter while relying on the API’s speed and voice quality.

2. Google Cloud Text-to-Speech

Google Cloud offers a robust TTS API powered by deep learning models. It supports multiple languages and dialects, making it a solid choice for global applications. One unique feature is the ability to customize voice pitch and speed, which adds versatility to the audio output.

Features:

  1. Customizable voices (pitch, speed, etc.).
  2. Supports 220+ voices in 40+ languages.
  3. Integration with Google Cloud ecosystem.
  4. Free tier available for limited use cases.

Sample Code:

from google.cloud import texttospeech

def google_tts(text):

    client = texttospeech.TextToSpeechClient()

    synthesis_input = texttospeech.SynthesisInput(text=text)

    voice = texttospeech.VoiceSelectionParams(

        language_code="en-US", name="en-US-Wavenet-D")

    audio_config = texttospeech.AudioConfig(

        audio_encoding=texttospeech.AudioEncoding.MP3)

    response = client.synthesize_speech(

        input=synthesis_input, voice=voice, audio_config=audio_config)

    with open("output.mp3", "wb") as out:

        out.write(response.audio_content)

        print("Audio content written to file 'output.mp3'")

google_tts("Hello world! This is a test of Google Cloud Text-to-Speech.")

For a more comprehensive tutorial on integrating Google Cloud Text-to-Speech into your Python apps, check out their docs.

3. Amazon Polly

Amazon Polly is another top contender in the TTS space. It’s part of the Amazon Web Services (AWS) suite and provides scalable, real-time speech synthesis. With Polly, you can create life-like speech in multiple languages, and it also offers support for speech marks, which can be helpful for building animations.

Features:

  1. Real-time speech synthesis.
  2. Supports speech marks for precise control over output.
  3. Multi-language support.

Sample Code:

import boto3

def polly_tts(text):

    client = boto3.client('polly')

    response = client.synthesize_speech(

        Text=text,

        OutputFormat='mp3',

        VoiceId='Joanna')

    with open("speech.mp3", "wb") as file:

        file.write(response['AudioStream'].read())

polly_tts("Hello, this is a test of Amazon Polly.")

Amazon Polly’s documentation makes it easy to get started.

4. pyttsx3: Offline TTS

pyttsx3 is a Python library for text-to-speech conversion that works offline. It uses various TTS engines depending on the platform: NSSpeechSynthesizer on Mac OS, sapi5 on Windows, and espeak on Linux. It’s ideal for applications where you need offline TTS or want to avoid relying on external APIs.

Features:

  1. Works offline.
  2. Cross-platform support (Windows, Mac, Linux).
  3. Customizable voice properties (rate, volume, etc.).

Sample Code:

import pyttsx3

def offline_tts(text):

    engine = pyttsx3.init()

    engine.say(text)

    engine.runAndWait()

offline_tts("Hello, this is a test of pyttsx3.")

Because it doesn’t rely on the internet, pyttsx3 is a great choice for TTS in offline environments.

5. gTTS (Google Text-to-Speech)

The gTTS Python library is a lightweight wrapper around the Google Text-to-Speech API. It’s simple to use, and great for quick applications, but be aware that it does require an internet connection to function.

Features:

  1. Easy to use.
  2. Supports multiple languages.
  3. Generates mp3 audio files.

Sample Code:

from gtts import gTTS

def google_tts(text):

    tts = gTTS(text=text, lang='en')

    tts.save("output.mp3")

google_tts("Hello, world! This is a test of gTTS.")

Check out the official gTTS documentation for more info.

6. Coqui TTS: Open-Source Option

Coqui TTS is a powerful open-source TTS framework that provides deep learning-powered speech synthesis models. It’s customizable and gives developers control over the training and fine-tuning of models for their own use cases.

Features:

  1. Open-source and highly customizable.
  2. Leverages deep learning for high-quality voices.
  3. Can be used offline.

Sample Code:

from TTS.api import TTS

tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC", progress_bar=True)

tts.tts_to_file(text="Hello, this is a test of Coqui TTS.", file_path="output.wav")

You can find the Coqui TTS project on GitHub and explore various pre-trained models.

7. ElevenLabs TTS: AI-Powered Realistic Voices

ElevenLabs offers state-of-the-art AI voice synthesis that creates extremely realistic, human-like voices. It’s ideal for developers looking to incorporate highly expressive speech into their apps, voiceovers, or real-time speech generation. Their Python API provides easy integration and delivers an impressive variety of voices with emotional depth and nuanced articulation.

Features:

  1. Extremely realistic voices with emotional expression.
  2. Multiple languages and accents.
  3. Highly customizable: Modify speech rate, pitch, and style to suit your app’s needs.
  4. Real-time API: Ideal for instant speech generation.

Sample Code:

import requests

API_KEY = 'your_elevenlabs_api_key'

URL = "https://api.elevenlabs.io/v1/text-to-speech"

def elevenlabs_tts(text, voice_id="21m00Tcm4TlvDq8ikWAM"):

    headers = {

        'xi-api-key': API_KEY,

        'Content-Type': 'application/json'

    }

    data = {

        "text": text,

        "voice_id": voice_id,

        "voice_settings": {

            "stability": 0.75,

            "similarity_boost": 0.75

        }

    }    

    response = requests.post(URL, json=data, headers=headers)

    with open("output.mp3", "wb") as audio_file:

        audio_file.write(response.content)

elevenlabs_tts("Hello, this is a test of ElevenLabs TTS API.")

ElevenLabs is perfect for applications that require more expressive and dynamic voices—like storytelling, character dialogues in games, or interactive assistants. You can find the full API documentation on their official site.

Choosing the right text-to-speech API depends on your project’s needs. If low-latency and high-quality voices are your priority, PlayHT is an exceptional choice. For more customizable, cloud-based solutions, Google Cloud and Amazon Polly are top contenders. For offline use, pyttsx3 and Coqui TTS provide great flexibility.

Whether you’re building a chatbot, voice assistant, or simply converting text to audio files, these TTS solutions offer robust Python libraries to help you get started quickly.

What is the best text-to-speech for Python?

The best text-to-speech API for Python depends on your project’s needs. If you are looking for ultra-low latency and high-quality voices for real-time applications, then PlayHT is an excellent choice. It provides an intuitive Python API, perfect for apps requiring real-time speech synthesis like live streams or voice bots.

For offline or simple projects, pyttsx3 is a great Python library because it doesn’t require an internet connection and works cross-platform (Windows, macOS, and Linux).

What is the best speech-to-text module in Python?

For speech-to-text, one of the best Python modules is SpeechRecognition. It supports multiple engines like Google Web Speech API, CMU Sphinx (offline), and others. The library is versatile and can handle various use cases, from real-time transcription to more complex speech recognition tasks. It’s often used because of its simplicity and wide range of backend services for speech recognition.

Which is better, gTTS or pyttsx3?

Both gTTS and pyttsx3 have their pros and cons:

gTTS (Google Text-to-Speech):

  • Pros: Simple to use, lightweight, and supports multiple languages. Ideal for projects where high-quality, cloud-based TTS is needed.
  • Cons: Requires an internet connection and only outputs MP3 files.

pyttsx3:

  • Pros: Works offline, and is cross-platform (Windows, macOS, Linux). It allows you to control speech properties like rate and volume.
  • Cons: Voices can sound robotic compared to cloud-based solutions like Google or Amazon.

So, pyttsx3 is better for offline use, while gTTS is preferable for quick, internet-connected projects needing multiple languages.

Which text-to-speech API is realistic?

The most realistic text-to-speech API currently is PlayHT, known for its ultra-low latency and natural-sounding voices. It is great for applications requiring lifelike audio, including voice-overs, virtual assistants, and more. Other realistic APIs include Amazon Polly (with its Neural TTS) and Google Cloud TTS (especially its WaveNet voices). Both use advanced deep learning models for highly realistic speech synthesis.

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