https://github.com/willwade/tts-wrapper
TTS-Wrapper makes it easier to use text-to-speech APIs by providing a unified and easy-to-use interface.
https://github.com/willwade/tts-wrapper
Last synced: 4 months ago
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TTS-Wrapper makes it easier to use text-to-speech APIs by providing a unified and easy-to-use interface.
- Host: GitHub
- URL: https://github.com/willwade/tts-wrapper
- Owner: willwade
- License: mit
- Fork: true (mediatechlab/tts-wrapper)
- Created: 2023-09-17T08:51:46.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-02-17T07:27:51.000Z (5 months ago)
- Last Synced: 2025-02-17T08:22:13.112Z (5 months ago)
- Language: Python
- Homepage:
- Size: 3.79 MB
- Stars: 19
- Watchers: 3
- Forks: 2
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# py3-TTS-Wrapper
[](https://badge.fury.io/py/py3-tts-wrapper)
[](https://codecov.io/gh/willwade/py3-tts-wrapper)
> **Contributions are welcome! Check our [contribution guide](./CONTRIBUTING.md).**
_TTS-Wrapper_ simplifies using text-to-speech APIs by providing a unified interface across multiple services, allowing easy integration and manipulation of TTS capabilities.
> ℹ️ Full documentation is available at [https://willwade.github.io/tts-wrapper/](https://willwade.github.io/tts-wrapper/)
## Requirements
- Python 3.10 or higher
- System dependencies (see below)
- API credentials for online services
## Supported Services
- AWS Polly
- Google TTS
- Microsoft Azure TTS
- IBM Watson
- ElevenLabs
- Wit.Ai
- eSpeak-NG
- Play.HT
- AVSynth (macOS only)
- SAPI (Windows only)
- Sherpa-Onnx (NB: Means you can run any ONNX model you want - eg Piper or MMS models)
### Experimental (Not fully featured or in a state of WIP)
- PicoTTS
- UWP (WinRT) Speech system (win 10+)
## 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 Matrix
| Engine | Platform | Online/Offline | SSML | Word Boundaries | Streaming | Playback Control | Callbacks |
|------------|--------------------|--------------------|------|-----------------|-----------|------------------|-----------|
| Polly | Linux/MacOS/Windows| Online | Yes | Yes | Yes | Yes | Full |
| Google | Linux/MacOS/Windows| Online | Yes | Yes | Yes | Yes | Full |
| Microsoft | Linux/MacOS/Windows| Online | Yes | Yes | Yes | Yes | Full |
| Watson | Linux/MacOS/Windows| Online | Yes | Yes | Yes | Yes | Full |
| ElevenLabs | Linux/MacOS/Windows| Online | No* | Yes | Yes | Yes | Full |
| Play.HT | Linux/MacOS/Windows| Online | No* | No** | Yes | Yes | Basic |
| Wit.Ai | Linux/MacOS/Windows| Online | No* | No** | Yes | Yes | Basic |
| eSpeak | Linux/MacOS | Offline | Yes | No** | Yes | Yes | Basic |
| AVSynth | MacOS | Offline | No | No** | Yes | Yes | Basic |
| SAPI | Windows | Offline | Yes | Yes | Yes | Yes | Full |
| UWP | Windows | Offline | Yes | Yes | Yes | Yes | Full |
| Sherpa-ONNX| Linux/MacOS/Windows| Offline | No | No** | Yes | Yes | Basic |
**Notes**:
- **SSML**: Entries marked with No* indicate that while the engine doesn't support SSML natively, the wrapper will automatically strip SSML tags and process the plain text.
- **Word Boundaries**: Entries marked with No** use an estimation-based timing system that may not be accurate for precise synchronization needs.
- **Callbacks**:
- "Full" supports accurate word-level timing callbacks, onStart, and onEnd events
- "Basic" supports onStart and onEnd events, with estimated word timings
- **Playback Control**: All engines support pause, resume, and stop functionality through the wrapper's unified interface
- All engines support the following core features:
- Voice selection (`set_voice`)
- Property control (rate, volume, pitch)
- File output (WAV, with automatic conversion to MP3/other formats)
- Streaming playback
- Audio device selection
### Core Methods Available
| Method | Description | Availability |
|--------------------------|----------------------------------------------|--------------|
| `speak()` | Direct speech playback | All engines |
| `speak_streamed()` | Streamed speech playback | All engines |
| `synth_to_file()` | Save speech to file | All engines |
| `pause()`, `resume()` | Playback control | All engines |
| `stop()` | Stop playback | All engines |
| `set_property()` | Control rate/volume/pitch | All engines |
| `get_voices()` | List available voices | All engines |
| `set_voice()` | Select voice | All engines |
| `connect()` | Register event callbacks | All engines |
| `check_credentials()` | Verify API credentials | Online engines|
| `set_output_device()` | Select audio output device | All engines |
---
## Installation
### Package Name Note
This package is published on PyPI as `py3-tts-wrapper` but installs as `tts-wrapper`. This is because it's a fork of the original `tts-wrapper` project with Python 3 support and additional features.
### System Dependencies
This project requires the following system dependencies on Linux:
```sh
sudo apt-get install portaudio19-dev
```
or MacOS, using [Homebrew](https://brew.sh)
```sh
brew install portaudio
```
For PicoTTS on Debian systems:
```sh
sudo apt-get install libttspico-utils
```
The `espeak` TTS functionality requires the `espeak-ng` C library to be installed on your system:
- **Ubuntu/Debian**: `sudo apt install espeak-ng`
- **macOS**: `brew install espeak-ng`
- **Windows**: Download the binaries from https://espeak.sourceforge.net/
### Using pip
Install from PyPI with selected engines:
```sh
pip install "py3-tts-wrapper[google,microsoft,sapi,sherpaonnx,googletrans]"
```
Install from GitHub:
```sh
pip install "py3-tts-wrapper[google,microsoft,sapi,sherpaonnx,googletrans]@git+https://github.com/willwade/tts-wrapper"
```
Note: On macOS/zsh, you may need to use quotes:
```sh
pip install "py3-tts-wrapper[google,watson,polly,elevenlabs,microsoft,sherpaonnx]"
```
## Usage Guide
### Basic Usage
```python
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, world!')
tts.speak(ssml_text)
```
You can use SSML or plain text
```python
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
```python
from tts_wrapper import PollyTTS, PollyClient
client = PollyClient(credentials=('aws_region','aws_key_id', 'aws_secret_access_key'))
tts = PollyTTS(client)
```
#### Google
```python
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
```python
from tts_wrapper import GoogleTTS, GoogleClient
with open(os.getenv("GOOGLE_SA_PATH"), "r") as file:
credentials_dict = json.load(file)
client = GoogleClient(credentials=os.getenv('GOOGLE_SA_PATH'))
client = GoogleClient(credentials=credentials_dict)]
```
#### Microsoft
```python
from tts_wrapper import MicrosoftTTS, MicrosoftClient
client = MicrosoftClient(credentials=('subscription_key','subscription_region'))
tts = MicrosoftTTS(client)
```
#### Watson
```python
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
```python
from tts_wrapper import WatsonTTS, WatsonClient
client = WatsonClient(credentials=('api_key', 'region', 'instance_id'),disableSSLVerification=True)
tts = WatsonTTS(client)
```
#### ElevenLabs
```python
from tts_wrapper import ElevenLabsTTS, ElevenLabsClient
client = ElevenLabsClient(credentials=('api_key'))
tts = ElevenLabsTTS(client)
```
- **Note**: ElevenLabs does not support SSML.
#### Wit.Ai
```python
from tts_wrapper import WitAiTTS, WitAiClient
client = WitAiClient(credentials=('token'))
tts = WitAiTTS(client)
```
#### Play.HT
```python
from tts_wrapper import PlayHTClient, PlayHTTTS
client = PlayHTClient(credentials=('api_key', 'user_id'))
tts = PlayHTTTS(client)
```
- **Note**: Play.HT does not support SSML, but we automatically strip SSML tags if present.
#### UWP
```python
from tts_wrapper import UWPTTS, UWPClient
client = UWPClient()
tts = UWPTTS(client)
```
#### eSpeak
```python
from tts_wrapper import eSpeakClient, eSpeakTTS
client = eSpeakClient()
tts = eSpeakTTS(client)
```
Note: Requires espeak-ng to be installed on your system.
#### SAPI (Windows)
```python
from tts_wrapper import SAPIClient, SAPITTS
client = SAPIClient()
tts = SAPITTS(client)
```
Note: Only available on Windows systems.
#### AVSynth (macOS)
```python
from tts_wrapper import AVSynthClient, AVSynthTTS
client = AVSynthClient()
tts = AVSynthTTS(client)
```
Note: Only available on macOS. Provides high-quality speech synthesis with word timing support and voice property control.
#### GoogleTrans
Uses the gTTS library.
```python
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..
```python
from tts_wrapper import SherpaOnnxClient, SherpaOnnxTTS
client = SherpaOnnxClient(model_path=None, tokens_path=None)
tts = SherpaOnnxTTS(client)
```
Set a voice like
```python
# 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
```python
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.
```python
tts.speak('Hello World!')
```
#### Speak
This will use the default audio output of your device to play the audio immediately
```python
tts.speak(ssml_text)
```
#### Check Credentials
This will check if the credentials are valid. Its only on the client object. Eg
```python
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
You need to make sure the optional dependency is included for this
```sh
pip install py3-tts-wrapper[controlaudio,google.. etc
```
then
```python
client = GoogleClient(..)
tts = GoogleTTS(client)
try:
text = "This is a pause and resume test. The text will be longer, depending on where the pause and resume works"
audio_bytes = tts.synth_to_bytes(text)
tts.load_audio(audio_bytes)
print("Play audio for 3 seconds")
tts.play(1)
tts.pause(8)
tts.resume()
time.sleep(6)
finally:
tts.cleanup()
```
- the pause and resume are in seconds from the start of the audio
- Please use the cleanup method to ensure the audio is stopped and the audio device is released
NB: to do this we use pyaudio. If you have issues with this you may need to install portaudio19-dev - particularly on linux
```sh
sudo apt-get install portaudio19-dev
```
#### File Output
```python
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.
```Python
tts.synth('Hello, world!', 'hello.mp3', format='mp3)
```
Note you can also stream - and save. Just note it saves at the end of streaming entirely..
```python
ssml_text = tts.ssml.add('Hello world!')
tts.speak_streamed(ssml_text,filepath,'wav')
```
#### Fetch Available Voices
```python
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
```python
tts.set_voice(voice_id,lang_code=en-US)
```
e.g.
```python
tts.set_voice('en-US-JessaNeural','en-US')
```
Use the id - not a name
#### SSML
```python
ssml_text = tts.ssml.add('Hello, world!')
tts.speak(ssml_text)
```
#### Volume, Rate and Pitch Control
Set volume:
```python
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:
```python
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:
```python
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`.
```python
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`.
```python
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)
```python
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
```bash
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`.
```python
def on_start():
print("Speech started")
tts.connect('onStart', on_start)
```
## Audio Output Methods
The wrapper provides several methods for audio output, each suited for different use cases:
### 1. Direct Playback
The simplest method - plays audio immediately:
```python
tts.speak("Hello world")
```
### 2. Streaming Playback
Recommended for longer texts - streams audio as it's being synthesized:
```python
tts.speak_streamed("This is a long text that will be streamed as it's synthesized")
```
### 3. File Output
Save synthesized speech to a file:
```python
tts.synth_to_file("Hello world", "output.wav")
```
### 4. Raw Audio Data
For advanced use cases where you need the raw audio data:
```python
# Get raw PCM audio data as bytes
audio_bytes = tts.synth_to_bytes("Hello world")
```
### Audio Format Notes
- All engines output WAV format by default
- For MP3 or other formats, use external conversion libraries like `pydub`:
```python
from pydub import AudioSegment
import io
# Get WAV data
audio_bytes = tts.synth_to_bytes("Hello world")
# Convert to MP3
wav_audio = AudioSegment.from_wav(io.BytesIO(audio_bytes))
wav_audio.export("output.mp3", format="mp3")
```
---
### 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.
```python
# 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.
```python
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.
### Manual Audio Control
For advanced use cases where you need direct control over audio playback, you can use the raw audio data methods:
```python
from tts_wrapper import AVSynthClient, AVSynthTTS
import numpy as np
import sounddevice as sd
# Initialize TTS
client = AVSynthClient()
tts = AVSynthTTS(client)
# Method 1: Direct playback of entire audio
def play_audio_stream(tts, text: str):
"""Play entire audio at once."""
# Get raw audio data
audio_data = tts.synth_to_bytes(text)
# Convert to numpy array for playback
samples = np.frombuffer(audio_data, dtype=np.int16)
# Play the audio
sd.play(samples, samplerate=tts.audio_rate)
sd.wait()
# Method 2: Chunked playback for more control
def play_audio_chunked(tts, text: str, chunk_size: int = 4096):
"""Process and play audio in chunks for more control."""
# Get raw audio data
audio_data = tts.synth_to_bytes(text)
# Create a continuous stream
stream = sd.OutputStream(
samplerate=tts.audio_rate,
channels=1, # Mono audio
dtype=np.int16
)
with stream:
# Process in chunks
for i in range(0, len(audio_data), chunk_size):
chunk = audio_data[i:i + chunk_size]
if len(chunk) % 2 != 0: # Ensure even size for 16-bit audio
chunk = chunk[:-1]
samples = np.frombuffer(chunk, dtype=np.int16)
stream.write(samples)
```
This manual control allows you to:
- Process audio data in chunks
- Implement custom audio processing
- Control playback timing
- Add effects or modifications to the audio
- Implement custom buffering strategies
The chunked playback method is particularly useful for:
- Real-time audio processing
- Custom pause/resume functionality
- Volume adjustment during playback
- Progress tracking
- Memory-efficient handling of long audio
**Note**: Manual audio control requires the `sounddevice` and `numpy` packages:
```sh
pip install sounddevice numpy
```
## Developer's Guide
### Setting up the Development Environment
#### Using Pipenv
1. Clone the repository:
```sh
git clone https://github.com/willwade/tts-wrapper.git
cd tts-wrapper
```
2. Install the package and system dependencies:
```sh
pip install .
```
To install optional dependencies, use:
```sh
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 UV
1. [Install UV](https://docs.astral.sh/uv/#getting-started)
```sh
pip install uv
```
2. Clone the repository:
```sh
git clone https://github.com/willwade/tts-wrapper.git
cd tts-wrapper
```
3. Install Python dependencies:
```sh
uv sync
```
4. Install system dependencies (Linux only):
```sh
uv run postinstall
```
**NOTE**: to get a requirements.txt file for the project use `uv export --format requirements-txt --all-extras --no-hashes` juat be warned that this will include all dependencies including dev ones.
## Release a new build
```sh
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.
```python
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`.
```python
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.
```python
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.
```python
from .engine import WitTTS
from .ssml import EngineSSML
```
#### NB: Credentials Files
You can store your credentials in either:
- `credentials.json` - For development
- `credentials-private.json` - For private credentials (should be git-ignored)
Example structure (do NOT commit actual credentials):
```json
{
"Polly": {
"region": "your-region",
"aws_key_id": "your-key-id",
"aws_access_key": "your-access-key"
},
"Microsoft": {
"token": "your-subscription-key",
"region": "your-region"
}
}
```
### Service-Specific Setup
#### AWS Polly
- [Create an AWS account](https://aws.amazon.com/free)
- [Set up IAM credentials](https://docs.aws.amazon.com/polly/latest/dg/setting-up.html)
- [Polly API Documentation](https://docs.aws.amazon.com/polly/latest/dg/API_Operations.html)
#### Microsoft Azure
- [Create an Azure account](https://azure.microsoft.com/free)
- [Create a Speech Service resource](https://docs.microsoft.com/azure/cognitive-services/speech-service/get-started)
- [Azure Speech Service Documentation](https://docs.microsoft.com/azure/cognitive-services/speech-service/rest-text-to-speech)
#### Google Cloud
- [Create a Google Cloud account](https://cloud.google.com/free)
- [Set up a service account](https://cloud.google.com/text-to-speech/docs/quickstart-client-libraries)
- [Google TTS Documentation](https://cloud.google.com/text-to-speech/docs)
#### IBM Watson
- [Create an IBM Cloud account](https://cloud.ibm.com/registration)
- [Create a Text to Speech service instance](https://cloud.ibm.com/catalog/services/text-to-speech)
- [Watson TTS Documentation](https://cloud.ibm.com/apidocs/text-to-speech)
#### ElevenLabs
- [Create an ElevenLabs account](https://elevenlabs.io/)
- [Get your API key](https://docs.elevenlabs.io/authentication)
- [ElevenLabs Documentation](https://docs.elevenlabs.io/)
#### Play.HT
- [Create a Play.HT account](https://play.ht/)
- [Get your API credentials](https://docs.play.ht/reference/api-getting-started)
- [Play.HT Documentation](https://docs.play.ht/)
#### Wit.AI
- [Create a Wit.ai account](https://wit.ai/)
- [Create a new app and get token](https://wit.ai/docs/quickstart)
- [Wit.ai Documentation](https://wit.ai/docs)
## License
This project is licensed under the [MIT License](./LICENSE).