Openai Tts

OpenAI TTS

Text-to-speech conversion using OpenAI's TTS API for generating high-quality, natural-sounding audio from text.

Features

• 6 different voice options (male/female)
• Standard and HD quality models
• Automatic text chunking for long content (4096 char limit)
• Multiple output formats (mp3, opus, aac, flac)

Activation

This skill activates when the user:

• Requests audio/voice output: "read this to me", "convert to audio", "generate speech", "make this an audio file"
• Uses keywords: "tts", "openai tts", "text to speech", "voice", "audio", "podcast"
• Needs content spoken for accessibility, multitasking, or podcast creation
• Specifies voice preferences: "alloy", "echo", "fable", "onyx", "nova", "shimmer"
• Asks to "narrate", "speak", or "vocalize" text

Requirements

• OPENAI_API_KEY environment variable must be set
• Python 3.8+
• Dependencies: openai, pydub (optional, for long text)

Voices

Usage

Basic Usage

from openai import OpenAI
import os

client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))

response = client.audio.speech.create(
    model="tts-1",      # or "tts-1-hd" for higher quality
    voice="onyx",       # choose from: alloy, echo, fable, onyx, nova, shimmer
    input="Your text here",
    speed=1.0           # 0.25 to 4.0 (optional)
)

with open("output.mp3", "wb") as f:
    for chunk in response.iter_bytes():
        f.write(chunk)

Command Line

# Basic
python -c "
from openai import OpenAI
client = OpenAI()
response = client.audio.speech.create(model='tts-1', voice='onyx', input='Hello world')
open('output.mp3', 'wb').write(response.content)
"

Long Text (Auto-chunking)

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

client = OpenAI()
MAX_CHARS = 4096

def split_text(text):
    if len(text) <= MAX_CHARS:
        return [text]

    chunks = []
    sentences = re.split(r'(?<=[.!?])\s+', text)
    current = ''

    for sentence in sentences:
        if len(current) + len(sentence) + 1 <= MAX_CHARS:
            current += (' ' if current else '') + sentence
        else:
            if current:
                chunks.append(current)
            current = sentence

    if current:
        chunks.append(current)

    return chunks

def generate_tts(text, output_path, voice='onyx', model='tts-1'):
    chunks = split_text(text)

    if len(chunks) == 1:
        response = client.audio.speech.create(model=model, voice=voice, input=text)
        with open(output_path, 'wb') as f:
            f.write(response.content)
    else:
        segments = []
        for chunk in chunks:
            response = client.audio.speech.create(model=model, voice=voice, input=chunk)
            with tempfile.NamedTemporaryFile(suffix='.mp3', delete=False) as tmp:
                tmp.write(response.content)
                segments.append(AudioSegment.from_mp3(tmp.name))
                os.unlink(tmp.name)

        combined = segments[0]
        for seg in segments[1:]:
            combined += seg
        combined.export(output_path, format='mp3')

    return output_path

# Usage
generate_tts("Your long text here...", "output.mp3", voice="nova")

Models

Output Formats

Supported formats: mp3 (default), opus, aac, flac

response = client.audio.speech.create(
    model="tts-1",
    voice="onyx",
    input="Hello",
    response_format="opus"  # or mp3, aac, flac
)

Error Handling

from openai import OpenAI, APIError, RateLimitError
import time

client = OpenAI()

def generate_with_retry(text, voice='onyx', max_retries=3):
    for attempt in range(max_retries):
        try:
            response = client.audio.speech.create(
                model="tts-1",
                voice=voice,
                input=text
            )
            return response.content
        except RateLimitError:
            if attempt < max_retries - 1:
                time.sleep(2 ** attempt)  # Exponential backoff
                continue
            raise
        except APIError as e:
            print(f"API Error: {e}")
            raise

    return None

Examples

Convert Article to Podcast

def article_to_podcast(article_text, output_file):
    intro = "Welcome to today's article reading."
    outro = "Thank you for listening."

    full_text = f"{intro}\n\n{article_text}\n\n{outro}"

    generate_tts(full_text, output_file, voice='nova', model='tts-1-hd')
    print(f"Podcast saved to {output_file}")

Batch Processing

def batch_tts(texts, output_dir, voice='onyx'):
    import os
    os.makedirs(output_dir, exist_ok=True)

    for i, text in enumerate(texts):
        output_path = os.path.join(output_dir, f"audio_{i+1}.mp3")
        generate_tts(text, output_path, voice=voice)
        print(f"Generated: {output_path}")

Links

• OpenAI TTS Documentation
• OpenAI API Reference
• Pricing