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YarnGPT Python SDK

Tests Code Quality PyPI version Python Support License: MIT

Unofficial community-built Python SDK for YarnGPT - A powerful Text-to-Speech API featuring authentic Nigerian accents.

Note: This is a community-maintained SDK and is not officially endorsed by YarnGPT. For official support, please visit yarngpt.ai.

Features

  • 🎤 16 Unique Nigerian Voices - Choose from a diverse range of authentic accents
  • 🔊 Multiple Audio Formats - Support for MP3, WAV, OPUS, and FLAC
  • 🚀 Simple & Intuitive API - Get started in minutes
  • 🔒 Type-Safe - Full type hints and enums for better IDE support
  • ⚡ Async Support - Efficient for high-volume applications
  • 📝 Comprehensive Documentation - Clear examples and API reference

Installation

Basic Installation

pip install yarngpt-sdk

With CLI Support

# Install with command-line interface
pip install yarngpt-sdk[cli]

Development Installation

# Install with development dependencies
pip install yarngpt-sdk[dev]

# Install everything (CLI + dev tools)
pip install yarngpt-sdk[cli,dev]

Quick Start

Python API

from yarngpt import YarnGPT, Voice, AudioFormat

# Initialize the client with your API key
client = YarnGPT(api_key="your_api_key_here")

# Generate speech
audio = client.text_to_speech(
    text="Hello! Welcome to Nigeria.",
    voice=Voice.IDERA,
    response_format=AudioFormat.MP3
)

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

Command-Line Interface

# Set your API key
export YARNGPT_API_KEY="your_api_key_here"

# Convert text to speech
yarngpt convert "Hello, welcome to Nigeria!"

# Specify voice and format
yarngpt convert "Good morning!" --voice emma --format wav -o greeting.wav

# Batch convert from a file
yarngpt batch texts.txt --output-dir audio_files --voice jude

# List all available voices
yarngpt voices

# Show API information
yarngpt info

Authentication

Get your API key from your YarnGPT Account Page.

Python API

from yarngpt import YarnGPT

# Method 1: Pass directly
client = YarnGPT(api_key="your_api_key")

# Method 2: Use environment variable
import os
client = YarnGPT(api_key=os.getenv("YARNGPT_API_KEY"))

# Method 3: Auto-load from .env file
# Create .env file with: YARNGPT_API_KEY=your_api_key
client = YarnGPT()  # Automatically loads from environment

CLI

# Method 1: Set environment variable
export YARNGPT_API_KEY="your_api_key"  # Linux/Mac
set YARNGPT_API_KEY=your_api_key       # Windows CMD
$env:YARNGPT_API_KEY="your_api_key"    # Windows PowerShell

# Method 2: Pass via command line
yarngpt convert "Hello" --api-key your_api_key

# Method 3: Use .env file
# Create .env file in your project directory
echo "YARNGPT_API_KEY=your_api_key" > .env

Available Voices

The SDK provides 16 authentic Nigerian voices, each with unique characteristics:

Voice Name Description Enum
Idera Melodic, gentle Voice.IDERA
Emma Authoritative, deep Voice.EMMA
Zainab Soothing, gentle Voice.ZAINAB
Osagie Smooth, calm Voice.OSAGIE
Wura Young, sweet Voice.WURA
Jude Warm, confident Voice.JUDE
Chinenye Engaging, warm Voice.CHINENYE
Tayo Upbeat, energetic Voice.TAYO
Regina Mature, warm Voice.REGINA
Femi Rich, reassuring Voice.FEMI
Adaora Warm, engaging Voice.ADAORA
Umar Calm, smooth Voice.UMAR
Mary Energetic, youthful Voice.MARY
Nonso Bold, resonant Voice.NONSO
Remi Melodious, warm Voice.REMI
Adam Deep, clear Voice.ADAM

Usage Examples

Basic Text-to-Speech

from yarngpt import YarnGPT, Voice

client = YarnGPT(api_key="your_api_key")

# Simple conversion
audio = client.text_to_speech("Hello, how are you doing today?")

# With specific voice
audio = client.text_to_speech(
    text="Welcome to our platform!",
    voice=Voice.EMMA
)

Save Directly to File

from yarngpt import YarnGPT, Voice, AudioFormat

client = YarnGPT(api_key="your_api_key")

# Save to file in one step
output_path = client.text_to_speech_file(
    text="This will be saved directly to a file",
    output_path="speech.mp3",
    voice=Voice.JUDE,
    response_format=AudioFormat.MP3
)

print(f"Audio saved to: {output_path}")

Using Different Audio Formats

from yarngpt import YarnGPT, AudioFormat

client = YarnGPT(api_key="your_api_key")

# Generate WAV format
audio_wav = client.text_to_speech(
    text="High quality audio",
    response_format=AudioFormat.WAV
)

# Generate OPUS format (good for streaming)
audio_opus = client.text_to_speech(
    text="Compressed audio",
    response_format=AudioFormat.OPUS
)

Context Manager (Recommended)

from yarngpt import YarnGPT, Voice

# Automatically handles cleanup
with YarnGPT(api_key="your_api_key") as client:
    audio = client.text_to_speech(
        text="Using context manager for proper resource management",
        voice=Voice.ADAORA
    )
    
    with open("output.mp3", "wb") as f:
        f.write(audio)

Error Handling

from yarngpt import YarnGPT, Voice
from yarngpt.exceptions import (
    AuthenticationError,
    ValidationError,
    APIError,
    QuotaExceededError,
    PaymentRequiredError,
    YarnGPTError
)

client = YarnGPT()

try:
    audio = client.text_to_speech(
        text="Your text here",
        voice=Voice.IDERA
    )
except QuotaExceededError as e:
    print(f"Quota exceeded: {e}")
    print("Free tier: 80 TTS requests/day")
except AuthenticationError:
    print("Invalid API key!")
except ValidationError as e:
    print(f"Invalid parameters: {e}")
except PaymentRequiredError as e:
    print(f"Payment required: {e}")
except APIError as e:
    print(f"API request failed: {e}")
except YarnGPTError as e:
    print(f"YarnGPT error: {e}")

Daily Usage Limits

YarnGPT enforces the following daily limits:

Resource Free Tier Limit
TTS Requests 80/day
Media Processing Jobs 8/day
URL Extractions 100/day
Chunked Audio Generations 120/day

When you exceed these limits, the SDK will raise a QuotaExceededError.

Working with Long Text

from yarngpt import YarnGPT

client = YarnGPT(api_key="your_api_key")

# Text must be under 2000 characters
long_text = "Your long text here..."

if len(long_text) > 2000:
    # Split into chunks
    chunks = [long_text[i:i+2000] for i in range(0, len(long_text), 2000)]
    
    for i, chunk in enumerate(chunks):
        audio = client.text_to_speech(chunk)
        with open(f"output_part_{i}.mp3", "wb") as f:
            f.write(audio)
else:
    audio = client.text_to_speech(long_text)

Async Support

The SDK provides full async/await support through the AsyncYarnGPT client for high-performance, concurrent operations. Perfect for web applications, batch processing, and high-volume TTS generation.

Basic Async Usage

import asyncio
from yarngpt import AsyncYarnGPT, Voice, AudioFormat

async def generate_speech():
    async with AsyncYarnGPT(api_key="your_api_key") as client:
        audio = await client.text_to_speech(
            text="Hello from async client!",
            voice=Voice.IDERA,
            response_format=AudioFormat.MP3,
        )
        
        # Save to file
        with open("output.mp3", "wb") as f:
            f.write(audio)
        
        print(f"Generated {len(audio):,} bytes")

# Run the async function
asyncio.run(generate_speech())

Async File Operations

import asyncio
from yarngpt import AsyncYarnGPT, Voice

async def save_to_file():
    async with AsyncYarnGPT() as client:
        path = await client.text_to_speech_file(
            text="Async file save example",
            output_path="async_output.mp3",
            voice=Voice.EMMA,
        )
        print(f"Saved to {path}")

asyncio.run(save_to_file())

Concurrent Batch Processing

The async client shines when processing multiple texts concurrently:

import asyncio
from yarngpt import AsyncYarnGPT, Voice

async def batch_concurrent():
    texts = [
        "First message",
        "Second message",
        "Third message",
        "Fourth message",
    ]
    
    async with AsyncYarnGPT() as client:
        # Process all texts concurrently (much faster!)
        audios = await client.batch_text_to_speech(
            texts=texts,
            voice=Voice.JUDE,
            concurrent=True,  # Enable concurrent processing
        )
        
        print(f"Generated {len(audios)} audio files concurrently!")
        
        # Save all files
        for i, audio in enumerate(audios):
            with open(f"batch_{i}.mp3", "wb") as f:
                f.write(audio)

asyncio.run(batch_concurrent())

Concurrent vs Sequential Comparison

import asyncio
import time
from yarngpt import AsyncYarnGPT

async def compare_performance():
    texts = ["Text 1", "Text 2", "Text 3", "Text 4", "Text 5"]
    
    async with AsyncYarnGPT() as client:
        # Sequential processing
        start = time.time()
        audios_seq = await client.batch_text_to_speech(
            texts=texts,
            concurrent=False,
        )
        seq_time = time.time() - start
        
        # Concurrent processing
        start = time.time()
        audios_con = await client.batch_text_to_speech(
            texts=texts,
            concurrent=True,
        )
        con_time = time.time() - start
        
        print(f"Sequential: {seq_time:.2f}s")
        print(f"Concurrent: {con_time:.2f}s")
        print(f"Speed up: {seq_time/con_time:.2f}x faster!")

asyncio.run(compare_performance())

Async Batch to Files

import asyncio
from pathlib import Path
from yarngpt import AsyncYarnGPT, Voice, AudioFormat

async def batch_to_files():
    texts = [
        "Welcome to our service",
        "Thank you for calling",
        "Please hold",
    ]
    
    async with AsyncYarnGPT() as client:
        paths = await client.batch_text_to_speech_files(
            texts=texts,
            output_dir="audio_output",
            filename_prefix="message",
            voice=Voice.REGINA,
            response_format=AudioFormat.MP3,
            concurrent=True,
        )
        
        print("Generated files:")
        for path in paths:
            print(f"  • {path}")

asyncio.run(batch_to_files())

Custom Retry Configuration (Async)

import asyncio
from yarngpt import AsyncYarnGPT, RetryConfig

async def with_custom_retry():
    retry_config = RetryConfig(
        max_retries=5,
        backoff_factor=2.0,
        max_backoff=60.0,
        jitter=True,
    )
    
    async with AsyncYarnGPT(retry_config=retry_config) as client:
        audio = await client.text_to_speech(
            text="Text with custom retry logic"
        )
        print(f"Generated with retry config: {len(audio):,} bytes")

asyncio.run(with_custom_retry())

Async with Multiple Voices (Concurrent)

import asyncio
from yarngpt import AsyncYarnGPT, Voice

async def test_multiple_voices():
    async with AsyncYarnGPT() as client:
        # Process different voices concurrently
        tasks = [
            client.text_to_speech("Testing Idera", voice=Voice.IDERA),
            client.text_to_speech("Testing Emma", voice=Voice.EMMA),
            client.text_to_speech("Testing Jude", voice=Voice.JUDE),
            client.text_to_speech("Testing Zainab", voice=Voice.ZAINAB),
        ]
        
        audios = await asyncio.gather(*tasks)
        
        voices = [Voice.IDERA, Voice.EMMA, Voice.JUDE, Voice.ZAINAB]
        for voice, audio in zip(voices, audios):
            print(f"{voice.value}: {len(audio):,} bytes")

asyncio.run(test_multiple_voices())

Async Error Handling

The async client includes robust error handling with automatic retries for transient failures:

import asyncio
from yarngpt import AsyncYarnGPT, Voice
from yarngpt.exceptions import (
    ValidationError,
    QuotaExceededError,
    AuthenticationError,
    APIError,
)

async def handle_errors():
    async with AsyncYarnGPT() as client:
        try:
            # This will succeed
            audio = await client.text_to_speech(
                text="Valid text",
                voice=Voice.IDERA,
            )
            print(f"✅ Success: {len(audio):,} bytes")
            
        except ValidationError as e:
            print(f"❌ Validation Error: {e}")
            # Handle invalid input (empty text, text too long, etc.)
            
        except QuotaExceededError as e:
            print(f"❌ Quota Exceeded: {e}")
            # Handle daily limit reached (80 requests/day for free tier)
            
        except AuthenticationError as e:
            print(f"❌ Auth Error: {e}")
            # Handle invalid API key
            
        except APIError as e:
            print(f"❌ API Error: {e}")
            # Handle other API errors (500, 502, 503, etc.)
            # These are automatically retried with exponential backoff

asyncio.run(handle_errors())

Graceful Error Handling in Batch Operations

When processing multiple items concurrently, some may fail while others succeed:

import asyncio
from yarngpt import AsyncYarnGPT, Voice
from yarngpt.exceptions import YarnGPTError

async def batch_with_error_handling():
    texts = [
        "Valid text 1",
        "",  # This will fail (empty)
        "Valid text 2",
        "a" * 3000,  # This will fail (too long)
        "Valid text 3",
    ]
    
    async with AsyncYarnGPT() as client:
        results = []
        
        for i, text in enumerate(texts):
            try:
                audio = await client.text_to_speech(text, voice=Voice.EMMA)
                results.append((i, audio, None))
                print(f"✅ Text {i}: {len(audio):,} bytes")
            except YarnGPTError as e:
                results.append((i, None, str(e)))
                print(f"❌ Text {i}: {e}")
        
        # Process successful results
        successful = [(i, audio) for i, audio, error in results if error is None]
        failed = [(i, error) for i, audio, error in results if error is not None]
        
        print(f"\n📊 Results: {len(successful)} succeeded, {len(failed)} failed")

asyncio.run(batch_with_error_handling())

async def safe_batch_processing():
    texts = ["Text 1", "Text 2", "Text 3"]
    
    async with AsyncYarnGPT() as client:
        tasks = [
            client.text_to_speech(text=text, voice=Voice.IDERA)
            for text in texts
        ]
        
        # Use return_exceptions=True to handle partial failures
        results = await asyncio.gather(*tasks, return_exceptions=True)
        
        for i, result in enumerate(results):
            if isinstance(result, Exception):
                print(f"Text {i+1} failed: {type(result).__name__}")
            else:
                print(f"Text {i+1} succeeded: {len(result):,} bytes")

asyncio.run(safe_batch_processing())

Tip: Use asyncio.gather(*tasks, return_exceptions=True) for graceful handling of partial failures in batch operations.

Integration with Web Frameworks

FastAPI Example

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from yarngpt import AsyncYarnGPT, Voice

app = FastAPI()
client = AsyncYarnGPT()

class TTSRequest(BaseModel):
    text: str
    voice: str = "idera"

@app.post("/tts")
async def generate_tts(request: TTSRequest):
    try:
        async with client:
            audio = await client.text_to_speech(
                text=request.text,
                voice=Voice[request.voice.upper()],
            )
            return {"size": len(audio), "status": "success"}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

Django Async View Example

from django.http import JsonResponse
from yarngpt import AsyncYarnGPT, Voice

async def tts_view(request):
    text = request.GET.get('text', '')
    voice = request.GET.get('voice', 'idera')
    
    async with AsyncYarnGPT() as client:
        audio = await client.text_to_speech(
            text=text,
            voice=Voice[voice.upper()],
        )
        
        return JsonResponse({
            'size': len(audio),
            'status': 'success'
        })

Performance Tips for Async

  1. Use Concurrent Processing: Always set concurrent=True for batch operations
  2. Context Manager: Use async with to ensure proper cleanup
  3. Connection Pooling: AsyncYarnGPT reuses HTTP connections efficiently
  4. Gather Tasks: Use asyncio.gather() for maximum concurrency
  5. Error Handling: Wrap async calls in try-except for robust error handling
import asyncio
from yarngpt import AsyncYarnGPT, YarnGPTError

async def robust_async_tts():
    async with AsyncYarnGPT() as client:
        try:
            audio = await client.text_to_speech("Your text here")
            return audio
        except YarnGPTError as e:
            print(f"Error: {e}")
            return None

# Run with proper error handling
result = asyncio.run(robust_async_tts())

Command-Line Interface (CLI)

The YarnGPT SDK includes a powerful CLI tool for easy text-to-speech conversion from the terminal.

Installation

pip install yarngpt-sdk[cli]

CLI Commands

yarngpt convert

Convert text to speech and save to a file.

# Basic usage
yarngpt convert "Hello, welcome to Nigeria!"

# Custom output file
yarngpt convert "Good morning" -o greeting.mp3

# Specify voice
yarngpt convert "Test message" --voice emma

# Specify format
yarngpt convert "High quality" --format wav -o output.wav

# All options
yarngpt convert "Complete example" \
  --output audio.mp3 \
  --voice jude \
  --format mp3 \
  --api-key your_key

Options:

  • -o, --output PATH - Output file path (default: output.mp3)
  • -v, --voice TEXT - Voice name (default: idera)
  • -f, --format TEXT - Audio format: mp3, wav, opus, flac (default: mp3)
  • -k, --api-key TEXT - YarnGPT API key (or use YARNGPT_API_KEY env var)

yarngpt batch

Batch convert multiple texts from a file.

# Create a text file with one text per line
cat > texts.txt << EOF
Hello, welcome!
How are you today?
Thank you very much!
EOF

# Batch convert
yarngpt batch texts.txt

# Custom output directory and prefix
yarngpt batch texts.txt -o audio_files --prefix speech

# With specific voice and format
yarngpt batch texts.txt --voice emma --format wav

Options:

  • -o, --output-dir PATH - Output directory (default: output)
  • -v, --voice TEXT - Voice for all files (default: idera)
  • -f, --format TEXT - Audio format for all files (default: mp3)
  • -p, --prefix TEXT - Filename prefix (default: audio)
  • -k, --api-key TEXT - YarnGPT API key

Input Formats:

Text file (one per line):

First text to convert
Second text to convert
Third text to convert

JSON file (array of strings):

[
  "First text to convert",
  "Second text to convert",
  "Third text to convert"
]

yarngpt voices

List all available voices with descriptions.

yarngpt voices

Output:

┌────────────┬────────────┬──────────────────────────┐
│ Name       │ Value      │ Description              │
├────────────┼────────────┼──────────────────────────┤
│ IDERA      │ Idera      │ Melodic and gentle voice │
│ EMMA       │ Emma       │ Authoritative and deep   │
│ ...        │ ...        │ ...                      │
└────────────┴────────────┴──────────────────────────┘

yarngpt formats

List all supported audio formats.

yarngpt formats

yarngpt info

Show API information, limits, and helpful links.

yarngpt info

Output shows:

  • Free tier limits
  • Available features
  • Useful links
  • Getting started guide

yarngpt version

Show the SDK version.

yarngpt version

CLI Examples

Convert with Nigerian accent:

yarngpt convert "Welcome to Lagos!" --voice tayo -o lagos.mp3

Create multiple greetings:

cat > greetings.txt << EOF
Good morning, have a great day!
Good afternoon, hope you're doing well!
Good evening, time to relax!
EOF

yarngpt batch greetings.txt -o greetings --prefix greeting --voice regina
# Creates: greetings/greeting_0.mp3, greetings/greeting_1.mp3, ...

High-quality WAV output:

yarngpt convert "Professional audio message" \
  --voice adam \
  --format wav \
  -o professional.wav

Quick voice testing:

# List all voices
yarngpt voices

# Test different voices
yarngpt convert "Testing Idera voice" --voice idera -o test_idera.mp3
yarngpt convert "Testing Emma voice" --voice emma -o test_emma.mp3
yarngpt convert "Testing Jude voice" --voice jude -o test_jude.mp3

API Reference

YarnGPT

Main client class for interacting with the YarnGPT API.

Constructor

YarnGPT(api_key: str, base_url: Optional[str] = None, timeout: float = 30.0)

Parameters:

  • api_key (str): Your YarnGPT API key
  • base_url (str, optional): Custom API base URL
  • timeout (float, optional): Request timeout in seconds (default: 30)

Methods

text_to_speech()

Convert text to speech.

client.text_to_speech(
    text: str,
    voice: Optional[Union[Voice, str]] = None,
    response_format: Optional[Union[AudioFormat, str]] = None
) -> bytes

Parameters:

  • text (str): Text to convert (max 2000 characters)
  • voice (Voice | str, optional): Voice to use (default: Idera)
  • response_format (AudioFormat | str, optional): Audio format (default: mp3)

Returns: Audio data as bytes

text_to_speech_file()

Convert text to speech and save to file.

client.text_to_speech_file(
    text: str,
    output_path: Union[str, Path],
    voice: Optional[Union[Voice, str]] = None,
    response_format: Optional[Union[AudioFormat, str]] = None
) -> Path

Parameters:

  • text (str): Text to convert (max 2000 characters)
  • output_path (str | Path): Where to save the audio file
  • voice (Voice | str, optional): Voice to use (default: Idera)
  • response_format (AudioFormat | str, optional): Audio format (default: mp3)

Returns: Path to the saved file

Enums

Voice

Available voice options: IDERA, EMMA, ZAINAB, OSAGIE, WURA, JUDE, CHINENYE, TAYO, REGINA, FEMI, ADAORA, UMAR, MARY, NONSO, REMI, ADAM

AudioFormat

Supported audio formats: MP3, WAV, OPUS, FLAC

Exceptions

  • YarnGPTError - Base exception for all SDK errors
  • AuthenticationError - Invalid or missing API key
  • ValidationError - Invalid request parameters
  • QuotaExceededError - Daily quota exceeded (80 TTS requests/day on free tier)
  • PaymentRequiredError - Payment required to continue
  • APIError - General API request failure

Development

Setup Development Environment

# Clone the repository
git clone https://github.com/yarngpt/yarngpt-sdk.git
cd yarngpt-sdk

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -e ".[dev]"

Running Tests

pytest tests/ -v

Code Formatting

black yarngpt/
ruff check yarngpt/

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

See CONTRIBUTING.md for more details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Roadmap

We have exciting features planned for future releases! See our ROADMAP.md for details on:

  • 🔄 Async Support - AsyncYarnGPT for concurrent applications
  • 🔁 Retry Logic - Automatic retry with exponential backoff
  • 💾 Caching - Optional caching to reduce API calls
  • 🎵 Audio Utilities - Audio processing, validation, and concatenation
  • 📊 Usage Analytics - Track and export usage statistics
  • 🧪 Mock Client - Testing without API calls
  • And much more!

See the full roadmap for planned features and timelines.

Links

Support

For issues, questions, or contributions, please visit our GitHub Issues page.

Maintainer: Halleluyah Darasimi Oludele (@hallelx2)

Unofficial community SDK • Not affiliated with YarnGPT

Made with ❤️ for the Nigerian developer community

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v0.1.0 - Initial Release Latest
Nov 23, 2025

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