Text to Speech

Convert text to natural-sounding speech with Fish Audio

Overview

Transform any text into natural, expressive speech using Fish Audio’s advanced TTS models. Choose from pre-made voices or use your own cloned voices.

Discover the world’s best cloned voices models on our Discovery page.

Quick Start

Web Interface

The easiest way to generate speech:

1. Visit Playground

   Go to fish.audio and log in

2. Enter Your Text

   Type or paste the text you want to convert

3. Choose a Voice

   Select from available voices or use your own

4. Generate

   Click “Generate” and download your audio

Using the SDK

Python

1. Install the SDK

   pip install fish-audio-sdk
   

2. Basic Usage

   Generate speech with just a few lines of code:

   from fishaudio import FishAudio
   from fishaudio.utils import save
   
   # Initialize client
   client = FishAudio(api_key="your_api_key_here")
   
   # Generate speech
   audio = client.tts.convert(
       text="Hello, world!",
       reference_id="your_voice_model_id"
   )
   save(audio, "output.mp3")
   
   print("✓ Audio saved to output.mp3")
   

Voice Options

Using Pre-made Voices

Browse and select voices from the playground:

Python

# Use a voice from the playground
audio = client.tts.convert(
    text="Welcome to Fish Audio!",
    reference_id="7f92f8afb8ec43bf81429cc1c9199cb1"
)

Using Your Cloned Voice

Use voices you’ve created:

Python

# Use your own cloned voice
audio = client.tts.convert(
    text="This is my custom voice speaking",
    reference_id="your_model_id"
)

Using Reference Audio

Provide reference audio directly:

Python

from fishaudio.types import ReferenceAudio

# Use reference audio on-the-fly
with open("voice_sample.wav", "rb") as f:
    audio = client.tts.convert(
        text="Hello from reference audio",
        references=[
            ReferenceAudio(
                audio=f.read(),
                text="Sample text from the audio"
            )
        ]
    )

Model Selection

Choose the right model for your needs:

Model	Best For	Quality	Speed
s1	Latest features	Excellent	Fast
speech-1.6	Stable production	Very Good	Fast
speech-1.5	Legacy support	Good	Fastest

Specify a model in your request:

Python

# Using the latest model (default)
audio = client.tts.convert(text="Hello world")

Advanced Options

Audio Formats

Choose your output format:

Python

audio = client.tts.convert(
    text="Your text here",
    format="mp3",  # Options: "mp3", "wav", "pcm", "opus"
    mp3_bitrate=128  # For MP3: 64, 128, or 192
)

Chunk Length

Control text processing chunks:

Python

audio = client.tts.convert(
    text="Long text content...",
    chunk_length=200  # 100-300 characters per chunk
)

Latency Mode

Optimize for speed or quality:

Python

audio = client.tts.convert(
    text="Quick response needed",
    latency="balanced"  # "normal" or "balanced"
)

Balanced mode reduces latency to ~300ms but may slightly decrease stability.

Direct API Usage

For direct API calls without the SDK:

Python

import httpx
import ormsgpack

# Prepare request
request_data = {
    "text": "Hello, world!",
    "reference_id": "your_model_id",
    "format": "mp3"
}

# Make API call
with httpx.Client() as client:
    response = client.post(
        "https://api.fish.audio/v1/tts",
        content=ormsgpack.packb(request_data),
        headers={
            "authorization": "Bearer YOUR_API_KEY",
            "content-type": "application/msgpack",
            "model": "s1"
        }
    )
    
    # Save audio
    with open("output.mp3", "wb") as f:
        f.write(response.content)

Streaming Audio

Stream audio for real-time applications:

Python

# Stream audio chunks
audio_stream = client.tts.stream(
    text="Streaming this text in real-time",
    reference_id="model_id"
)

with open("stream_output.mp3", "wb") as f:
    for chunk in audio_stream:
        f.write(chunk)
        # Process chunk immediately for real-time playback

Adding Emotions

Make your speech more expressive:

Python

# Add emotion markers to your text
emotional_text = """
(excited) I just won the lottery!
(sad) But then I lost the ticket.
(laughing) Just kidding, I found it!
"""

audio = client.tts.convert(
    text=emotional_text,
    reference_id="model_id"
)

Available emotions:

• Basic: (happy), (sad), (angry), (excited), (calm)
• Tones: (shouting), (whispering), (soft tone)
• Effects: (laughing), (sighing), (crying)

For more precise control over pronunciation and additional paralanguage features like pauses and breathing, see Fine-grained Control.

Best Practices

Text Preparation

Do:

• Use proper punctuation for natural pauses
• Add emotion markers for expression
• Break long texts into paragraphs
• Use consistent formatting

Don’t:

• Use ALL CAPS (unless shouting)
• Mix multiple languages randomly
• Include special characters unnecessarily
• Forget punctuation

Performance Tips

1. Batch Processing: Process multiple texts efficiently
2. Cache Models: Store frequently used model IDs
3. Optimize Chunk Size: Use 200 characters for best balance
4. Handle Errors: Implement retry logic for network issues

Quality Optimization

For best results:

• Use high-quality reference audio for cloning
• Choose appropriate emotion markers
• Test different latency modes
• Monitor API rate limits

Troubleshooting

Common Issues

No audio output:

• Check API key validity
• Verify model ID exists
• Ensure proper audio format

Poor quality:

• Use better reference audio
• Try normal latency mode
• Check text formatting

Slow generation:

• Use balanced latency mode
• Reduce chunk length
• Check network connection

Code Examples

Batch Processing

Python

from fishaudio.utils import save

texts = [
    "First announcement",
    "Second announcement",
    "Third announcement"
]

for i, text in enumerate(texts):
    audio = client.tts.convert(
        text=text,
        reference_id="model_id"
    )
    save(audio, f"output_{i}.mp3")

Error Handling

Python

import time
from fishaudio.exceptions import FishAudioError

def generate_with_retry(text, max_retries=3):
    for attempt in range(max_retries):
        try:
            audio = client.tts.convert(
                text=text,
                reference_id="model_id"
            )
            return audio
        except FishAudioError as e:
            if attempt < max_retries - 1:
                time.sleep(2 ** attempt)  # Exponential backoff
            else:
                raise e

API Reference

Request Parameters

Parameter	Type	Description	Default
text	string	Text to convert	Required
reference_id	string	Model/voice ID	None
format	string	Audio format	”mp3”
chunk_length	integer	Characters per chunk	200
normalize	boolean	Normalize text	true
latency	string	Speed vs quality	”normal”

Response

Returns audio data in the specified format as binary stream.

Get Support

Need help with text-to-speech?

• API Reference
• Discord Community: Join our Discord
• Email Support: [e-mail]