If you're tired of paying for proprietary text-to-speech APIs or dealing with robotic-sounding open source alternatives, I've got some good news. I recently discovered Kokoro TTS, and it's genuinely impressive for an open source model.

What Makes Kokoro TTS Special? ​

Kokoro is an open weight TTS model with 82 million parameters. While that might sound small compared to some of the massive models out there, don't let the size fool you. It delivers quality that's comparable to much larger proprietary models, without the hefty price tag or usage restrictions.

The best part? It's completely open source with Apache-licensed weights. You can run it locally, modify it, and deploy it anywhere from production environments to personal projects without worrying about API costs or rate limits.

Finding the Best Open Source TTS Models ​

Before we dive into the code, let me share how I discovered Kokoro. HuggingFace hosts community-driven leaderboards that rank various AI models based on user voting. While these aren't official benchmarks, they provide valuable insights into what the community finds useful.

When browsing the TTS leaderboard, you'll notice most top spots are occupied by proprietary models like ElevenLabs and others. Kokoro-82M stands out as the highest-ranking non-proprietary option, making it an excellent choice for developers who want quality without ongoing costs.

Quick Installation Guide ​

Getting started with Kokoro is straightforward. First, initialize your virtual environment:

uv init

Then install the dependency:

uv add kokoro

Basic Usage ​

Here's the complete code to generate your first speech with Kokoro.

from kokoro import KPipeline
from IPython.display import display, Audio
import soundfile as sf
import torch

# Initialize the pipeline with language code
pipeline = KPipeline(lang_code='a')

# Your text with pronunciation hints
text = '''
Hello, this is [Kokoro](/kˈOkəɹO/) text to speech in action!
'''

# Generate speech with selected voice
generator = pipeline(text, voice='af_heart')

# Process and save the audio
for i, (gs, ps, audio) in enumerate(generator):
    print(i, gs, ps)
    display(Audio(data=audio, rate=24000, autoplay=i==0))
    sf.write(f'{i}.wav', audio, 24000)

Key Features Explained ​

The Pipeline Architecture ​

The KPipeline is the core component that handles text processing and audio generation. When you initialize it, you specify a language code:

# 'a' for American English
pipeline = KPipeline(lang_code='a')

# Other language codes:
# 'b' - British English
# 'e' - Spanish
# 'f' - French
# 'j' - Japanese
# 'k' - Korean
# 'z' - Chinese

Pronunciation Control ​

One of Kokoro's standout features is its phoneme notation system. Notice how we can guide pronunciation using IPA-like notation:

text = '''
[Kokoro](/kˈOkəɹO/) is pronounced ko-ko-ro.
The word [amazing](/əˈmeɪzɪŋ/) sounds natural with phoneme hints.
'''

This is particularly useful for:

• Proper nouns
• Technical terms
• Words with multiple pronunciations
• Non-native words

Stream Processing ​

The generator approach allows for streaming audio generation, which is perfect for:

• Long texts that need to start playing before complete generation
• Real-time applications
• Memory-efficient processing

generator = pipeline(text, voice='af_heart')
for i, (gs, ps, audio) in enumerate(generator):
    # gs: generation step
    # ps: processing stage
    # audio: numpy array of audio samples

    # Process each chunk as it's generated
    process_audio_chunk(audio)

Voice Selection ​

Kokoro offers various voices for different languages and genders. The voice format is [gender][language]_[name]:

# American English voices
pipeline("Hello", voice='af_heart')  # American Female - Heart
pipeline("Hello", voice='af_bella')  # American Female - Bella
pipeline("Hello", voice='am_adam')   # American Male - Adam

# Other language examples
pipeline("Hola", voice='es_dora')    # Spanish - Dora
pipeline("Bonjour", voice='ff_siwis') # French - Elise

Practical Examples ​

Creating an Audiobook Reader ​

def create_audiobook(text_file, output_prefix):
    pipeline = KPipeline(lang_code='a')

    with open(text_file, 'r') as f:
        text = f.read()

    # Split into chapters or paragraphs for better processing
    paragraphs = text.split('\n\n')

    audio_segments = []
    for i, paragraph in enumerate(paragraphs):
        print(f"Processing paragraph {i+1}/{len(paragraphs)}")
        generator = pipeline(paragraph, voice='af_heart')

        for j, (gs, ps, audio) in enumerate(generator):
            audio_segments.append(audio)

    # Combine all audio
    full_audio = np.concatenate(audio_segments)
    sf.write(f'{output_prefix}_complete.wav', full_audio, 24000)

Multi-Language Support ​

# Create pipelines for different languages
from kokoro import KPipeline
from IPython.display import display, Audio
import soundfile as sf
import torch


pipelines = {
    "en": KPipeline(lang_code="a"),
    "es": KPipeline(lang_code="e"),
    "fr": KPipeline(lang_code="f"),
}

# Generate speech in multiple languages
texts = {
    "en": "Hello, welcome to our service",
    "es": "Hola, bienvenido a nuestro servicio",
    "fr": "Bonjour, bienvenue dans notre service",
}

voices = {"en": "af_heart", "es": "ef_dora", "fr": "ff_siwis"}

for lang, text in texts.items():
    generator = pipelines[lang](text, voice=f"{voices[lang]}")
    for i, (gs, ps, audio) in enumerate(generator):
        sf.write(f"welcome_{lang}.wav", audio, 24000)

Performance Tips ​

1. Batch Processing: For multiple short texts, combine them with pauses:

   python

   texts = ["First sentence.", "Second sentence.", "Third sentence."]
   combined = " ... ".join(texts)  # Add pauses between

2. Memory Management: The generator approach is memory-efficient, but for very long texts:

   python

   # Process in chunks
   chunk_size = 1000  # characters
   for i in range(0, len(long_text), chunk_size):
       chunk = long_text[i:i+chunk_size]
       generator = pipeline(chunk, voice='af_heart')
       # Process chunk...

3. Audio Format: The output is 24kHz mono audio. For different formats:

   python

   # Convert to different sample rate
   import librosa
   audio_resampled = librosa.resample(audio, orig_sr=24000, target_sr=44100)

Common Use Cases ​

Kokoro TTS excels at:

• Accessibility features: Screen readers and voice assistants
• Content creation: YouTube videos, podcasts, audiobooks
• Educational tools: Language learning apps, pronunciation guides
• Gaming: NPC dialogue, narrative experiences
• Business applications: IVR systems, voice notifications

Troubleshooting ​

Common Issues and Solutions ​

1. Pronunciation Issues: Use phoneme notation for problem words
2. Language Mismatch: Ensure language code matches your text
3. Audio Quality: Use appropriate voices for your content type
4. Performance: Consider GPU acceleration for batch processing

Conclusion ​

Kokoro TTS demonstrates that high-quality text-to-speech doesn't require proprietary solutions or massive models. With its efficient 82M parameter architecture, extensive language support, and Apache license, it's an excellent choice for developers seeking reliable, natural-sounding speech generation.

The streaming generation approach, combined with phoneme control and multiple voice options, makes it versatile enough for everything from simple notifications to complex narrative applications.

Try it in your next project. The combination of quality, flexibility, and zero cost makes Kokoro a compelling alternative to commercial TTS services.

Have you tried Kokoro TTS? What's your experience with open source TTS models? Let me know in the comments below or reach out on social media.