RealTimeTTS 0.1.5

*Stream text into audio with an easy-to-use, highly configurable library delivering voice output with minimal latency.

RealtimeTTS

Easy to use, low-latency text-to-speech library for realtime applications

About the Project

RealtimeTTS converts text streams into immediate auditory output.

It's ideal for:

• Voice Assistants
• Applications requiring instant audio feedback

Features

• Realtime Streaming: Synthesis and playback of speech as text is being generated or input
• Sentence Segmentation: Advanced sentence boundary detection, ensuring immediate reaction time by isolating fast-synthesizable fragments.
• Modular Engine Design: System TTS, Azure and Elevenlabs supported with the possibility to add custom Text-to-Speech engines

Hint: Check out RealtimeSTT, the input counterpart of this library, for speech-to-text capabilities. Together, they form a powerful realtime audio wrapper around large language models.

Tech Stack

The library is built upon a robust and cutting-edge tech stack:

• Text-to-Speech Engines

  • CoquiEngine: High quality local neural TTS.
  • AzureEngine: Microsoft's leading TTS technology. 250000 chars free per month.
  • ElevenlabsEngine: Offer the best sounding voices available.
  • SystemEngine: Native engine for quick setup.

• Sentence Boundary Detection

  • NLTK Sentence Tokenizer: Uses the Natural Language Toolkit's sentence tokenizer for precise and efficient sentence segmentation.

By using "industry standard" components RealtimeTTS offers a reliable, high-end technological foundation for developing advanced voice solutions.

Installation

pip install RealtimeTTS

Engine Requirements

Different engines supported by RealtimeTTS have unique requirements. Ensure you fulfill these requirements based on the engine you choose.

SystemEngine

The SystemEngine works out of the box using your system's built-in TTS capabilities. No additional setup is needed.

AzureEngine

To use the AzureEngine, you will need:

• Microsoft Azure Text-to-Speech API key (provided via AzureEngine constructor parameter "speech_key" or in the environment variable AZURE_SPEECH_KEY)
• Microsoft Azure service region.

Make sure you have these credentials available and correctly configured when initializing the AzureEngine.

ElevenlabsEngine

For the ElevenlabsEngine, you need:

• Elevenlabs API key (provided via ElevenlabsEngine constructor parameter "api_key" or in the environment variable ELEVENLABS_API_KEY)

• mpv installed on your system (essential for streaming mpeg audio, Elevenlabs only delivers mpeg).

  🔹 Installing mpv:

  • macOS:

    brew install mpv
    

  • Linux and Windows: Visit mpv.io for installation instructions.

CoquiEngine

Downloads a neural TTS model first. Will only be fast enought for Realtime using GPU synthesis. Needs around 4-5 GB VRAM. Delivers high quality, local, neural TTS with voice-cloning.

• To use voice cloning create a 44100 Hz mono 32bit float WAV file containing a short (~10 sec) sample of the voice to clone, then submit the filename as cloning_reference_wav to the CoquiEngine constructor.

Quick Start

Here's a basic usage example:

from RealtimeTTS import TextToAudioStream, SystemEngine, AzureEngine, ElevenlabsEngine

engine = SystemEngine() # replace with your TTS engine
stream = TextToAudioStream(engine)
stream.feed("Hello world! How are you today?")
stream.play_async()

Feed Text

You can feed individual strings:

stream.feed("Hello, this is a sentence.")

Or you can feed generators and character iterators for real-time streaming:

def write(prompt: str):
    for chunk in openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[{"role": "user", "content" : prompt}],
        stream=True
    ):
        if (text_chunk := chunk["choices"][0]["delta"].get("content")) is not None:
            yield text_chunk

text_stream = write("A three-sentence relaxing speech.")

stream.feed(text_stream)

char_iterator = iter("Streaming this character by character.")
stream.feed(char_iterator)

Playback

Asynchronously:

stream.play_async()
while stream.is_playing():
    time.sleep(0.1)

Synchronously:

stream.play()

Testing the Library

The test subdirectory contains a set of scripts to help you evaluate and understand the capabilities of the RealtimeTTS library.

• simple_test.py

  • Description: A "hello world" styled demonstration of the library's simplest usage.

• complex_test.py

  • Description: A comprehensive demonstration showcasing most of the features provided by the library.

• coqui_test.py

  • Description: Test of local coqui TTS engine.

• translator.py

  • Dependencies: Run pip install openai realtimestt.
  • Description: Real-time translations into six different languages.

• openai_voice_interface.py

  • Dependencies: Run pip install openai realtimestt.
  • Description: Wake word activated and voice based user interface to the OpenAI API.

• advanced_talk.py

  • Dependencies: Run pip install openai keyboard realtimestt.
  • Description: Choose TTS engine and voice before starting AI conversation.

• minimalistic_talkbot.py

  • Dependencies: Run pip install openai realtimestt.
  • Description: A basic talkbot in 20 lines of code.

• simple_llm_test.py

  • Dependencies: Run pip install openai.
  • Description: Simple demonstration how to integrate the library with large language models (LLMs).

• test_callbacks.py

  • Dependencies: Run pip install openai.
  • Description: Showcases the callbacks and lets you check the latency times in a real-world application environment.

Pause, Resume & Stop

Pause the audio stream:

stream.pause()

Resume a paused stream:

stream.resume()

Stop the stream immediately:

stream.stop()

Requirements Explained

• Python 3.6+

• requests (>=2.31.0): to send HTTP requests for API calls and voice list retrieval

• PyAudio (>=0.2.13): to create an output audio stream

• stream2sentence (>=0.1.1): to split the incoming text stream into sentences

• pyttsx3 (>=2.90): System text-to-speech conversion engine

• azure-cognitiveservices-speech (>=1.31.0): Azure text-to-speech conversion engine

• elevenlabs (>=0.2.24): Elevenlabs text-to-speech conversion engine

Configuration

Initialization Parameters for TextToAudioStream

When you initialize the TextToAudioStream class, you have various options to customize its behavior. Here are the available parameters:

engine (BaseEngine)

• Type: BaseEngine
• Required: Yes
• Description: The underlying engine responsible for text-to-audio synthesis. You must provide an instance of BaseEngine or its subclass to enable audio synthesis.

on_text_stream_start (callable)

• Type: Callable function
• Required: No
• Description: This optional callback function is triggered when the text stream begins. Use it for any setup or logging you may need.

on_text_stream_stop (callable)

• Type: Callable function
• Required: No
• Description: This optional callback function is activated when the text stream ends. You can use this for cleanup tasks or logging.

on_audio_stream_start (callable)

• Type: Callable function
• Required: No
• Description: This optional callback function is invoked when the audio stream starts. Useful for UI updates or event logging.

on_audio_stream_stop (callable)

• Type: Callable function
• Required: No
• Description: This optional callback function is called when the audio stream stops. Ideal for resource cleanup or post-processing tasks.

on_character (callable)

• Type: Callable function
• Required: No
• Description: This optional callback function is called when a single character is processed.

level (int)

• Type: Integer
• Required: No
• Default: logging.WARNING
• Description: Sets the logging level for the internal logger. This can be any integer constant from Python's built-in logging module.

Example Usage:

engine = YourEngine()  # Substitute with your engine
stream = TextToAudioStream(
    engine=engine,
    on_text_stream_start=my_text_start_func,
    on_text_stream_stop=my_text_stop_func,
    on_audio_stream_start=my_audio_start_func,
    on_audio_stream_stop=my_audio_stop_func,
    level=logging.INFO
)

Methods

play and play_async

These methods are responsible for executing the text-to-audio synthesis and playing the audio stream. The difference is that play is a blocking function, while play_async runs in a separate thread, allowing other operations to proceed.

fast_sentence_fragment (bool)

• Default: False
• Description: When set to True, the method will prioritize speed, generating and playing sentence fragments faster. This is useful for applications where latency matters.

buffer_threshold_seconds (float)

• Default: 2.0

• Description: Specifies the time in seconds for the buffering threshold, which impacts the smoothness and continuity of audio playback.

  • How it Works: Before synthesizing a new sentence, the system checks if there is more audio material left in the buffer than the time specified by buffer_threshold_seconds. If so, it retrieves another sentence from the text generator, assuming that it can fetch and synthesize this new sentence within the time window provided by the remaining audio in the buffer. This process allows the text-to-speech engine to have more context for better synthesis, enhancing the user experience.

  A higher value ensures that there's more pre-buffered audio, reducing the likelihood of silence or gaps during playback. If you experience breaks or pauses, consider increasing this value.

• Hint: If you experience silence or breaks between sentences, consider raising this value to ensure smoother playback.

minimum_sentence_length (int)

• Default: 3
• Description: Sets the minimum character length to consider a string as a sentence to be synthesized. This affects how text chunks are processed and played.

log_characters (bool)

• Default: False
• Description: Enable this to log the individual characters that are being processed for synthesis.

log_synthesized_text (bool)

• Default: False
• Description: When enabled, logs the text chunks as they are synthesized into audio. Helpful for auditing and debugging.

By understanding and setting these parameters and methods appropriately, you can tailor the TextToAudioStream to meet the specific needs of your application.

Contribution

Contributions are always welcome (e.g. PR to add a new engine).

License

MIT

Author

Kolja Beigel
Email: [e-mail]
GitHub