py3-tts-wrapper 0.9.16

TTS-Wrapper makes it easier to use text-to-speech APIs by providing a unified and easy-to-use interface.

py3-TTS-Wrapper

Contributions are welcome! Check our contribution guide.

TTS-Wrapper simplifies using text-to-speech APIs by providing a unified interface across multiple services, allowing easy integration and manipulation of TTS capabilities.

Supported Services

• AWS Polly
• Google TTS
• Microsoft Azure TTS
• IBM Watson
• ElevenLabs
• Wit.Ai

Experimental (Not fully featured or in a state of WIP)

• PicoTTS
• UWP (WinRT) Speech system (win 10+)
• Sherpa-Onnx (focusing on MMS models for now)
• gTTS (GoogleTranslation TTS.)
• eSpeak/SAPI (Microsoft Speech API)/NSSS

Features

• Text to Speech: Convert text into spoken audio.
• SSML Support: Use Speech Synthesis Markup Language to enhance speech synthesis.
• Voice and Language Selection: Customize the voice and language for speech synthesis.
• Streaming and Direct Play: Stream audio or play it directly.
• Pause, Resume, and Stop Controls: Manage audio playback dynamically.
• File Output: Save spoken audio to files in various formats.
• Unified Voice handling Get Voices across all TTS engines with alike keys
• Volume, Pitch, and Rate Controls Control volume, pitch and rate with unified methods

Feature set overview

Engine	OS	Online/Offline	SSML	Rate/Volume/Pitch	onWord events
Polly	Linux/MacOS/Windows	Online	Yes	Yes	Yes
Google	Linux/MacOS/Windows	Online	Yes	Yes	Yes
Azure	Linux/MacOS/Windows	Online	Yes	Yes	Yes
Watson	Linux/MacOS/Windows	Online	Yes	No	Yes
ElevenLabs	Linux/MacOS/Windows	Online	No	Yes	Yes
Wit.AI	Linux/MacOS/Windows	Online	Yes	No	No
Sherpa-Onnx	Linux/MacOS/Windows	Offline	No	No	No
gTTS	Linux/MacOS/Windows	Online	No	No	No
UWP	Windows	Offline	No	Yes	No
SAPI	Windows	Offline	Yes	Yes	Yes
NSS	MacOS	Offline	Yes	Yes	Yes
eSpeak	Linux/MacOS/Windows	Offline	No	Yes	No

Methods for each engine

Method	Description	Available Engines
speak()	Plays synthesized speech directly.	All engines
synth_to_file()	Synthesizes speech and saves it to a file.	All engines
speak_streamed()	Streams synthesized speech.	All engines
set_property()	Sets properties like rate, volume, pitch.	All engines
get_voices()	Retrieves available voices.	All engines
connect()	Connects callback functions for events.	Polly, Microsoft, Google, Watson.
pause_audio()	Pauses ongoing speech playback.	All engines
resume_audio()	Resumes paused speech playback.	All engines
stop_audio()	Stops ongoing speech playback.	All engines
check_credentials()	True or False if Credentials are ok	All engines

Notes:

• For SSML where it says 'no' you can send the engine SSML we will just strip it
• For onWord Events. For Engines where it is a no we have a very bad fallback mechanism which will emit word timings based on estimation. You cant rely on this for accurate use cases.

Install

System Dependencies

This project requires the following system dependencies on Linux:

sudo apt-get insall portaudio19-dev

or MacOS, using Homebrew

brew install portaudio

For PicoTTS on Debian systems:

sudo apt-get install libttspico-utils

Using pip

pip install py3-tts-wrapper[google,microsoft,sapi,sherpaonnx,googletrans]

or via git

pip install git+https://github.com/willwade/tts-wrapper#egg=tts-wrapper[google,microsoft,sapi,mms,sherpaonnx]

or (the newer way we should all use)

pip install tts-wrapper[google,microsoft,sapi,sherpaonnx,googletrans]@git+https://github.com/willwade/tts-wrapper

NB: On MacOS(/zsh) you may need to do use quotes

pip install py3-tts-wrapper"[google, watson, polly, elevenlabs, microsoft, mms, sherpaonnx]"

Basic Usage

from tts_wrapper import PollyClient
pollyClient = PollyClient(credentials=('aws_key_id', 'aws_secret_access_key'))

from tts_wrapper import PollyTTS

tts = PollyTTS(pollyClient)
ssml_text = tts.ssml.add('Hello, <break time="500ms"/> world!')
tts.speak(ssml_text)

You can use SSML or plain text

from tts_wrapper import PollyClient
pollyClient = PollyClient(credentials=('aws_key_id', 'aws_secret_access_key'))
from tts_wrapper import PollyTTS

tts = PollyTTS(pollyClient)
tts.speak('Hello world')

For a full demo see the examples folder. You'll need to fill out the credentials.json (or credentials-private.json). Use them from cd'ing into the examples folder. Tips on gaining keys are below.

Authorization

Each service uses different methods for authentication:

Polly

from tts_wrapper import PollyTTS, PollyClient
client = PollyClient(credentials=('aws_region','aws_key_id', 'aws_secret_access_key'))

tts = PollyTTS(client)

Google

from tts_wrapper import GoogleTTS, GoogleClient
client = GoogleClient(credentials=('path/to/creds.json'))

tts = GoogleTTS(client)

or pass the auth file as dict - so in memory

from tts_wrapper import GoogleTTS, GoogleClient

with open(os.getenv("GOOGLE_CREDS_PATH"), "r") as file:
    credentials_dict = json.load(file)

client = GoogleClient(credentials=os.getenv('GOOGLE_CREDS_PATH'))
client = GoogleClient(credentials=credentials_dict)]

Microsoft

from tts_wrapper import MicrosoftTTS, MicrosoftClient
client = MicrosoftClient(credentials=('subscription_key','subscription_region'))

tts = MicrosoftTTS(client)

Watson

from tts_wrapper import WatsonTTS, WatsonClient
client = WatsonClient(credentials=('api_key', 'region', 'instance_id'))

tts = WatsonTTS(client)

Note If you have issues with SSL certification try

from tts_wrapper import WatsonTTS, WatsonClient
client = WatsonClient(credentials=('api_key', 'region', 'instance_id'),disableSSLVerification=True)

tts = WatsonTTS(client)

ElevenLabs

from tts_wrapper import ElevenLabsTTS, ElevenLabsClient
client = ElevenLabsClient(credentials=('api_key'))
tts = ElevenLabsTTS(client)

• Note: ElevenLabs does not support SSML.

Wit.Ai

from tts_wrapper import WitAiTTS, WitAiClient
client = WitAiClient(credentials=('token'))
tts = WitAiTTS(client)

UWP

from tts_wrapper import UWPTTS, UWPClient
client = UWPClient()
tts = UWPTTS(client)

SAPI/eSpeak/NSSS

from tts_wrapper import SAPIClient, SAPITTS
client = SAPIClient('espeak') # eSpeak
client = SAPIClient('sapi') #SAPI
client = SAPIClient('nsss') #NSSS MacOS
# Initialize the TTS engine
tts = SAPITTS(client)

Just note: We cant do word timings in this.

GoogleTrans

Uses the gTTS library.

from tts_wrapper import GoogleTransClient, GoogleTransTTS
voice_id = "en-co.uk"  # Example voice ID for UK English
client = GoogleTransClient(voice_id)
# Initialize the TTS engine
tts = GoogleTransTTS(client)

Sherpa-ONNX

You can provide blank model path and tokens path - and we will use a default location.. AS NOTED - WE HAVE DESIGNED THIS RIGHT NOW FOR MMS MODELS! We will add others like piper etc to this - Infact I'll drop regular piper support for sherpa-onnx. Its less of a headache..

from tts_wrapper import SherpaOnnxClient, SherpaOnnxTTS
client = SherpaOnnxClient(model_path=None, tokens_path=None)
tts = SherpaOnnxTTS(client)

Set a voice like

# Find voices/langs availables
voices = tts.get_voices()
print("Available voices:", voices)

# Set the voice using ISO code
iso_code = "eng"  # Example ISO code for the voice - also ID in voice details
tts.set_voice(iso_code)

and then use speak, speak_streamed etc..

You then can perform the following methods.

Advanced Usage

SSML

Even if you don't use SSML features that much its wise to use the same syntax - so pass SSML not text to all engines

ssml_text = tts.ssml.add('Hello world!')

Plain Text

If you want to keep things simple each engine will convert plain text to SSML if its not.

tts.speak('Hello World!')

Speak

This will use the default audio output of your device to play the audio immediately

tts.speak(ssml_text)

Check Credentials

This will check if the credentials are valid. Its only on the client object. Eg

    client = MicrosoftClient(
        credentials=(os.getenv("MICROSOFT_TOKEN"), os.getenv("MICROSOFT_REGION"))
    )
    if client.check_credentials():
        print("Credentials are valid.")
    else:
        print("Credentials are invalid."

NB: Each engine has a different way of checking credentials. If they dont have a supported the parent class will check get_voices. If you want to save calls just do a get_voices call.

Streaming and Playback Control

pause_audio(), resume_audio(), stop_audio()

These methods manage audio playback by pausing, resuming, or stopping it. NB: Only to be used for speak_streamed

tts.speak_streamed(ssml_text)

tts.pause_audio()
tts.resume_audio()
tts.stop_audio()

here's an example of this in use

ssml_text = tts.ssml.add('Hello world!')

tts.speak_streamed(ssml_text)
input("Press enter to pause...")
tts.pause_audio()
input("Press enter to resume...")
tts.resume_audio()
input("Press enter to stop...")
tts.stop_audio()

File Output

tts.synth_to_file(ssml_text, 'output.mp3', format='mp3')

there is also "synth" method which is legacy. Note we support saving as mp3, wav or flac.

tts.synth('<speak>Hello, world!</speak>', 'hello.mp3', format='mp3)

Note you can also stream - and save. Just note it saves at the end of streaming entirely..

ssml_text = tts.ssml.add('Hello world!')

tts.speak_streamed(ssml_text,filepath,'wav')

Fetch Available Voices

voices = tts.get_voices()
print(voices)

NB: All voices will have a id, dict of language_codes, name and gender. Just note not all voice engines provide gender

Voice Selection

tts.set_voice(voice_id,lang_code=en-US)

e.g.

tts.set_voice('en-US-JessaNeural','en-US')

Use the id - not a name

SSML

ssml_text = tts.ssml.add('Hello, <break time="500ms"/> world!')
tts.speak(ssml_text)

Volume, Rate and Pitch Control

Set volume:

tts.set_property("volume", "90")
text_read = f"The current volume is 90"
text_with_prosody = tts.construct_prosody_tag(text_read)
ssml_text = tts.ssml.add(text_with_prosody)

• Volume is set on a scale of 0 (silent) to 100 (maximum).
• The default volume is 100 if not explicitly specified.

Set rate:

tts.set_property("rate", "slow")
text_read = f"The current rate is SLOW"
text_with_prosody = tts.construct_prosody_tag(text_read)
ssml_text = tts.ssml.add(text_with_prosody)

Speech Rate:

• Rate is controlled using predefined options:
  • x-slow: Very slow speaking speed.
  • slow: Slow speaking speed.
  • medium (default): Normal speaking speed.
  • fast: Fast speaking speed.
  • x-fast: Very fast speaking speed.
• If not specified, the speaking rate defaults to medium.

Set pitch:

tts.set_property("pitch", "high")
text_read = f"The current pitch is SLOW"
text_with_prosody = tts.construct_prosody_tag(text_read)
ssml_text = tts.ssml.add(text_with_prosody)

Pitch Control:

• Pitch is adjusted using predefined options that affect the vocal tone:
  • x-low: Very deep pitch.
  • low: Low pitch.
  • medium (default): Normal pitch.
  • high: High pitch.
  • x-high: Very high pitch.
• If not explicitly set, the pitch defaults to medium.

Use the tts.ssml.clear_ssml() method to clear all entries from the ssml list

set_property()

This method allows setting properties like rate, volume, and pitch.

tts.set_property("rate", "fast")
tts.set_property("volume", "80")
tts.set_property("pitch", "high")

get_property()

This method retrieves the value of properties such as volume, rate, or pitch.

current_volume = tts.get_property("volume")
print(f"Current volume: {current_volume}")

Using callbacks on word-level boundaries

Note only Polly, Microsoft, Google, ElevenLabs, UWP, SAPI and Watson can do this correctly. We can't do this in anything else but we do do a estimated tonings for all other engines (ie elevenlabs, witAi and Piper)

def my_callback(word: str, start_time: float, end_time: float):
    duration = end_time - start_time
    print(f"Word: {word}, Duration: {duration:.3f}s")

def on_start():
    print('Speech started')

def on_end():
    print('Speech ended')

try:
    text = "Hello, This is a word timing test"
    ssml_text = tts.ssml.add(text)
    tts.connect('onStart', on_start)
    tts.connect('onEnd', on_end)
    tts.start_playback_with_callbacks(ssml_text, callback=my_callback)
except Exception as e:
    print(f"Error: {e}")

and it will output

Speech started
Word: Hello, Duration: 0.612s
Word: , Duration: 0.212s
Word: This, Duration: 0.364s
Word: is, Duration: 0.310s
Word: a, Duration: 0.304s
Word: word, Duration: 0.412s
Word: timing, Duration: 0.396s
Word: test, Duration: 0.424s
Speech ended

connect()

This method allows registering callback functions for events like onStart or onEnd.

def on_start():
    print("Speech started")

tts.connect('onStart', on_start)

Supported File Formats

By default, all engines output audio in the WAV format, but can be configured to output MP3 or other formats where supported.

tts.synth('<speak>Hello, world!</speak>', 'hello.mp3', format='mp3)

The synth_to_bytestream method is designed to synthesize text into an in-memory bytestream in the specified audio format (wav, mp3, flac, etc.). It is particularly useful when you want to handle the audio data in-memory for tasks like saving it to a file, streaming the audio, or passing it to another system for processing.

Method Signature:

def synth_to_bytestream(self, text: Any, format: Optional[str] = "wav") -> BytesIO:
    """
    Synthesizes text to an in-memory bytestream in the specified audio format.

    :param text: The text to synthesize.
    :param format: The audio format (e.g., 'wav', 'mp3', 'flac'). Default: 'wav'.
    :return: A BytesIO object containing the audio data.
    """

Parameters:

• text: The text to be synthesized into audio.
• format: The audio format in which the synthesized audio should be returned. Default is wav. Supported formats include wav, mp3, and flac.

Returns:

• BytesIO: A BytesIO object containing the audio data in the requested format. This can be used directly to save to a file or for playback in real-time.

---

Example Use Cases

1. Saving Audio to a File

You can use the synth_to_bytestream method to synthesize audio in any supported format and save it directly to a file.

# Synthesize text into a bytestream in MP3 format
bytestream = tts.synth_to_bytestream("Hello, this is a test", format="mp3")

# Save the audio bytestream to a file
with open("output.mp3", "wb") as f:
    f.write(bytestream.read())

print("Audio saved to output.mp3")

Explanation:

• The method synthesizes the given text into audio in MP3 format.
• The BytesIO object is then written to a file using the .read() method of the BytesIO class.

2. Real-Time Playback Using sounddevice

If you want to play the synthesized audio live without saving it to a file, you can use the sounddevice library to directly play the audio from the BytesIO bytestream.

import sounddevice as sd
import numpy as np

# Synthesize text into a bytestream in WAV format
bytestream = tts.synth_to_bytestream("Hello, this is a live playback test", format="wav")

# Convert the bytestream back to raw PCM audio data for playback
audio_data = np.frombuffer(bytestream.read(), dtype=np.int16)

# Play the audio using sounddevice
sd.play(audio_data, samplerate=tts.audio_rate)
sd.wait()

print("Live playback completed")

Explanation:

• The method synthesizes the text into a wav bytestream.
• The bytestream is converted to raw PCM data using np.frombuffer(), which is then fed into the sounddevice library for live playback.
• sd.play() plays the audio in real-time, and sd.wait() ensures that the program waits until playback finishes.

Developer's Guide

Setting up the Development Environment

Using Pipenv

1. Clone the repository:

   git clone https://github.com/willwade/tts-wrapper.git
   cd tts-wrapper
   

2. Install the package and system dependencies:

   pip install .
   

   To install optional dependencies, use:

   pip install .[google, watson, polly, elevenlabs, microsoft]
   

This will install Python dependencies and system dependencies required for this project. Note that system dependencies will only be installed automatically on Linux.

Using Poetry

1. Clone the repository:

   git clone https://github.com/willwade/tts-wrapper.git
   cd tts-wrapper
   

2. Install Python dependencies:

   poetry install
   

3. Install system dependencies (Linux only):

   poetry run postinstall
   

NOTE: to get a requirements.txt file for the project use poetry export --without-hashes --format=requirements.txt > requirements.txt --all-extras juat be warned that this will include all dependencies including dev ones.

Release a new build

git tag -a v0.1.0 -m "Release 0.1.0"
git push origin v0.1.0

Adding a New Engine to TTS Wrapper

This guide provides a step-by-step approach to adding a new engine to the existing Text-to-Speech (TTS) wrapper system.

Step 1: Create Engine Directory Structure

1. Create a new folder for your engine within the engines directory. Name this folder according to your engine, such as witai for Wit.ai.

   Directory structure:

   engines/witai/
   

2. Create necessary files within this new folder:

   • __init__.py - Makes the directory a Python package.
   • client.py - Handles all interactions with the TTS API.
   • engine.py - Contains the TTS class that integrates with your abstract TTS system.
   • ssml.py - Defines any SSML handling specific to this engine.

   Final directory setup:

   engines/
   └── witai/
       ├── __init__.py
       ├── client.py
       ├── engine.py
       └── ssml.py
   

Step 2: Implement Client Functionality in client.py

Implement authentication and necessary setup for API connection. This file should manage tasks such as sending synthesis requests and fetching available voices.

class TTSClient:
    def __init__(self, api_key):
        self.api_key = api_key
        # Setup other necessary API connection details here

    def synth(self, text, options):
        # Code to send a synthesis request to the TTS API
        pass

    def get_voices(self):
        # Code to retrieve available voices from the TTS API
        pass

Step 3: Define the TTS Engine in engine.py

This class should inherit from the abstract TTS class and implement required methods such as get_voices and synth_to_bytes.

from .client import TTSClient
from your_tts_module.abstract_tts import AbstractTTS

class WitTTS(AbstractTTS):
    def __init__(self, api_key):
        super().__init__()
        self.client = TTSClient(api_key)

    def get_voices(self):
        return self.client.get_voices()

    def synth_to_bytes(self, text, format='wav'):
        return self.client.synth(text, {'format': format})

Step 4: Implement SSML Handling in ssml.py

If the engine has specific SSML requirements or supports certain SSML tags differently, implement this logic here.

from your_tts_module.abstract_ssml import BaseSSMLRoot, SSMLNode

class EngineSSML(BaseSSMLRoot):
    def add_break(self, time='500ms'):
        self.root.add(SSMLNode('break', attrs={'time': time}))

Step 5: Update __init__.py

Make sure the __init__.py file properly imports and exposes the TTS class and any other public classes or functions from your engine.

from .engine import WitTTS
from .ssml import EngineSSML

Tips

Getting keys

Watson

This is not straightforward

Polly

Microsoft

• You first need an Azure subscription - Create one for free.
• Create a Speech resource in the Azure portal.
• Your Speech resource key and region. After your Speech resource is deployed, select Go to resource to view and manage keys. For more information about Azure AI services resources, see Get the keys for your resource

Google

Create a Service Account:

1. Go to the Google Cloud Console: Visit the Google Cloud Console.
2. Create a New Project: If you don't already have a project, create a new one in the developer console.
3. Enable APIs: Enable the APIs that your service account will be using. For example, if you're using Google Drive API, enable that API for your project.
4. Create a Service Account:

• In the Google Cloud Console, navigate to "IAM & Admin" > "Service accounts."
• Click on "Create Service Account."
• Enter a name for the service account and an optional description.
• Choose the role for the service account. This determines the permissions it will have.
• Click "Continue" to proceed.

5. Create and Download Credentials:

• On the next screen, you can grant the service account a role in your project. You can also skip this step and grant roles later.
• Click "Create Key" to create and download the JSON key file. This file contains the credentials for your service account.
• Keep this JSON file secure and do not expose it publicly.

Wit.Ai

1. https://wit.ai/apps
2. Look for Bearer token. Its in the Curl example

ElevenLabs

1. Login at https://elevenlabs.io/app/speech-synthesis
2. Go to your profile and click on "Profile + API Key"
3. Click on Popup and copy "API Key"

License

This project is licensed under the MIT License.