A curated list of completely free Text-to-Speech (TTS) models with excellent Turkish support and multilingual capabilities. No development, just a comprehensive guide to help you find the perfect free TTS solution for your needs.

🌟 Features

• ✅ Completely FREE - No costs, no subscriptions
• 🇹🇷 Turkish-First - High-quality Turkish voice synthesis
• 🌍 Multilingual - Support for 10+ languages
• 🚀 Easy Setup - One-command installation

🔍 Choosing the Right TTS Model

Feature	XTTS v2 (Coqui)	Facebook MMS-TTS
Languages	16 languages	1,100+ languages
Turkish Support	⭐⭐⭐⭐⭐ Native	⭐⭐⭐⭐ Good
GPU Required	Optional (recommended)	Optional
Voice Cloning	✅ 6-second audio clip	❌ No cloning
Single Speaker	✅ Yes	✅ Yes (per language)
Multi-Speaker	✅ Yes	❌ One model per language
Model Size for TR	~1.9GB	277.01 MB
Fine-tuning	✅ Yes	❌ Pre-trained only
License	Coqui Public License	CC-BY-NC 4.0
Quality	⭐⭐⭐⭐⭐ Excellent	⭐⭐⭐⭐ Very Good
Commercial Use	❌ No longer supported	❌ Non-commercial only
Inference Speed	Fast	Moderate
Best For	Voice cloning, production	Research, many languages

🚀 Quick Start Guides

Option 1: Coqui TTS

Coqui TTS is a state-of-the-art deep learning toolkit for Text-to-Speech with excellent Turkish support and 1100+ language models.

pip install TTS

Running a multi-speaker and multi-lingual model

import torch
from TTS.api import TTS

# Get device
device = "cuda" if torch.cuda.is_available() else "cpu"

# List available 🐸TTS models
print(TTS().list_models())

# Init TTS
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to(device)

# Run TTS
# ❗ Since this model is multi-lingual voice cloning model, we must set the target speaker_wav and language
# Text to speech list of amplitude values as output
wav = tts.tts(text="Merhaba", speaker_wav="my/cloning/audio.wav", language="tr")
# Text to speech to a file
tts.tts_to_file(text="Merhaba", speaker_wav="my/cloning/audio.wav", language="tr", file_path="output.wav")

Option 2: Facebook mms-tts

This model is part of Facebook's Massively Multilingual Speech project, aiming to provide speech technology across a diverse range of languages. You can find more details about the supported languages and their ISO 639-3 codes in the MMS Language Coverage Overview, and see all MMS-TTS checkpoints on the Hugging Face Hub: facebook/mms-tts.

Running MMS-TTS inference in Colab

In this notebook, we give an example on how to run Turkish text-to-speech inference using MMS TTS models.

By default, we run inference on a GPU.

1. Installs necessary python packages for the other sections.
2. Choose a language or and download Turkish checkpoint. (Find the ISO code for your target language here.)
3. Load the checkpoint.
4. Specify the sentence you want to synthesize and generate the audio.

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