OpenAI Text-to-Speech

Generate high-quality spoken audio from text using OpenAI's TTS API.

Authentication

The API key is available as environment variable:

OPENAI_API_KEY

Models

• gpt-4o-mini-tts

  - Newest, most reliable. Supports tone/style instructions.

• tts-1

  - Lower latency, lower quality

• tts-1-hd

  - Higher quality, higher latency

Voice Options

Built-in voices (English optimized):

• alloy

  ,

  ash

  ,

  ballad

  ,

  coral

  ,

  echo

  ,

  fable

• nova

  ,

  onyx

  ,

  sage

  ,

  shimmer

  ,

  verse

• marin

  ,

  cedar

  - Recommended for best quality

Note:

tts-1

and

tts-1-hd

only support: alloy, ash, coral, echo, fable, onyx, nova, sage, shimmer.

Python Example

from pathlib import Path
from openai import OpenAI

client = OpenAI()  # Uses OPENAI_API_KEY env var

# Basic usage
with client.audio.speech.with_streaming_response.create(
    model="gpt-4o-mini-tts",
    voice="coral",
    input="Hello, world!",
) as response:
    response.stream_to_file("output.mp3")

# With tone instructions (gpt-4o-mini-tts only)
with client.audio.speech.with_streaming_response.create(
    model="gpt-4o-mini-tts",
    voice="coral",
    input="Today is a wonderful day!",
    instructions="Speak in a cheerful and positive tone.",
) as response:
    response.stream_to_file("output.mp3")

Handling Long Text

For long documents, split into chunks and concatenate:

from openai import OpenAI
from pydub import AudioSegment
import tempfile
import re
import os

client = OpenAI()

def chunk_text(text, max_chars=4000):
    """Split text into chunks at sentence boundaries."""
    sentences = re.split(r'(?<=[.!?])\s+', text)
    chunks = []
    current_chunk = ""

    for sentence in sentences:
        if len(current_chunk) + len(sentence) < max_chars:
            current_chunk += sentence + " "
        else:
            if current_chunk:
                chunks.append(current_chunk.strip())
            current_chunk = sentence + " "

    if current_chunk:
        chunks.append(current_chunk.strip())

    return chunks

def text_to_audiobook(text, output_path):
    """Convert long text to audio file."""
    chunks = chunk_text(text)
    audio_segments = []

    for chunk in chunks:
        with tempfile.NamedTemporaryFile(suffix='.mp3', delete=False) as tmp:
            tmp_path = tmp.name

        with client.audio.speech.with_streaming_response.create(
            model="gpt-4o-mini-tts",
            voice="coral",
            input=chunk,
        ) as response:
            response.stream_to_file(tmp_path)

        segment = AudioSegment.from_mp3(tmp_path)
        audio_segments.append(segment)
        os.unlink(tmp_path)

    # Concatenate all segments
    combined = audio_segments[0]
    for segment in audio_segments[1:]:
        combined += segment

    combined.export(output_path, format="mp3")

Output Formats

• mp3

  - Default, general use

• opus

  - Low latency streaming

• aac

  - Digital compression (YouTube, iOS)

• flac

  - Lossless compression

• wav

  - Uncompressed, low latency

• pcm

  - Raw samples (24kHz, 16-bit)

with client.audio.speech.with_streaming_response.create(
    model="gpt-4o-mini-tts",
    voice="coral",
    input="Hello!",
    response_format="wav",  # Specify format
) as response:
    response.stream_to_file("output.wav")

Best Practices

• Use

  marin

  or

  cedar

  voices for best quality
• Split text at sentence boundaries for long content
• Use

  wav

  or

  pcm

  for lowest latency
• Add

  instructions

  parameter to control tone/style (gpt-4o-mini-tts only)