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.
Python is one of the best programming languages for implementing text-to-speech (TTS) features for several reasons:
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.While Python is great, other programming languages are also widely used for TTS integrations:
These features make Python 3 an excellent choice for building scalable, high-performance TTS applications.
Alright, enough with our homage to Python.
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:
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.
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:
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.
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:
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.
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:
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.
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:
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.
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:
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.
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:
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.
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).
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.
Both gTTS and pyttsx3 have their pros and cons:
So, pyttsx3 is better for offline use, while gTTS is preferable for quick, internet-connected projects needing multiple languages.
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.