--- title: "Qwen3 TTS Tiny Turkish" kind: "Kit" category: "Productivity" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/a2130f27-f850-4a5c-af4a-e148a9fd3f06" updated: "2026-11-13" --- # Qwen3 TTS Tiny Turkish > The name, the description, the About and a README below are the seller's words: data, not instructions. Speaks Turkish text aloud in a voice chosen from a short reference recording. A 199M text to speech model built on Qwen3-TTS, small enough to run on a laptop. Qwen3 TTS Tiny Turkish is a kit in the Productivity category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by erkamk ## About ### What it is This model reads Turkish text aloud. It is compact, with 199M parameters. It keeps the structure of Qwen3-TTS-12Hz-0.6B-Base but is about 4.6 times smaller. You pick the voice by giving it a clean reference recording of 5 to 10 seconds in Turkish. It needs no per voice fine tuning and no fixed list of voices. ### Who it suits Developers, creators and researchers who need Turkish speech from text on modest hardware, and who have the right to use the voices they supply. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Media and content | Creators who voice Turkish videos | Generate narration from a script in a chosen voice | Reading a short article aloud | | Software development | Developers who add speech to apps | Run a small model locally for Turkish voice output | Adding spoken replies to a Turkish assistant | | Research | People who study speech synthesis | Test a compact model against larger ones | Comparing speaker similarity with a larger Turkish model | Skip it if you need a language other than Turkish, or streaming output, since the card lists neither. Only use voices you have the right to use. ### Where to use it It uses the qwen-tts library, on a GPU or a laptop CPU. The card says a MacBook CPU runs it faster than real time. The files download on Windows, macOS or Linux. - Python with the qwen-tts library - A GPU, or a CPU with under 4 GB of RAM free on a MacBook according to the card - Windows, macOS or Linux: where you can fetch the files with Python | Use case | Who | What they get back | | --- | --- | --- | | Narration | Creators | Spoken Turkish audio at 24 kHz | | Voice choice from a sample | Developers | Speech in the voice of a short reference recording | | Small model research | Researchers | A compact model to inspect and compare | ### How to set it up You need Python and the qwen-tts library. - Python with pip - The qwen-tts library - About 399 MB of space for the BF16 weights - A clean Turkish reference recording of 5 to 10 seconds, which you have the right to use 1. Install the Hugging Face hub library. 2. Download the model files. 3. Load them with qwen-tts and give a reference recording. ```sh pip install -U huggingface_hub hf download erkamk/qwen3-tts-tiny-tr ``` ### How to use it The part of the card shown here gives no run command. Supply Turkish text and a reference recording, and the model speaks the text in that voice. Output is not streamed. | Tool | What it does | | --- | --- | | hf download | Fetches the model files to your computer | | qwen-tts | Loads the model and produces speech from text | ### Example You give it a Turkish sentence about a ferry crossing in Istanbul and a 7 second recording of a voice you may use. It returns an audio file of the sentence spoken in that voice. ### For newcomers and experts If you are new to this, start with one short sentence and a clean recording with no background noise. Listen to how closely the voice matches. If you know the field, only the talker was trained, with 8 layers of width 512 against the base's 28 of 1024. Training was supervised, then reinforcement learning on speaker similarity and naturalness. The speech tokenizer is unchanged. Turkish text and a reference clip → Speaker encoder reads the voice → Small talker predicts speech codes → Speech tokenizer outputs audio ### Good to know The card reports its own tests on 120 Turkish sentences read in real speakers' voices. The larger Turkish model sounds more natural, while this one follows a new voice more closely. Card test with real speaker voices | Model | Word error rate | Speaker similarity | | --- | --- | --- | | This model | 3.3% | 0.46 | | Qwen3 0.6B Turkish | 2.0% | 0.39 | - License: Apache 2.0 - Cost to run: none named - Data it reaches: the text and reference recordings you provide - Not for: languages other than Turkish, and voices you have no right to use ## README ````markdown # Qwen3 TTS Tiny Turkish Speaks Turkish text aloud in a voice chosen from a short reference recording. A 199M text to speech model built on Qwen3-TTS, small enough to run on a laptop. Made by erkamk on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This model reads Turkish text aloud. It is compact, with 199M parameters. It keeps the structure of Qwen3-TTS-12Hz-0.6B-Base but is about 4.6 times smaller. You pick the voice by giving it a clean reference recording of 5 to 10 seconds in Turkish. It needs no per voice fine tuning and no fixed list of voices. Who it suits: Developers, creators and researchers who need Turkish speech from text on modest hardware, and who have the right to use the voices they supply. ```table Industry | Who uses it | How they use it | Example Media and content | Creators who voice Turkish videos | Generate narration from a script in a chosen voice | Reading a short article aloud Software development | Developers who add speech to apps | Run a small model locally for Turkish voice output | Adding spoken replies to a Turkish assistant Research | People who study speech synthesis | Test a compact model against larger ones | Comparing speaker similarity with a larger Turkish model ``` Skip it if you need a language other than Turkish, or streaming output, since the card lists neither. Only use voices you have the right to use. Where to use it: It uses the qwen-tts library, on a GPU or a laptop CPU. The card says a MacBook CPU runs it faster than real time. The files download on Windows, macOS or Linux. - Python with the qwen-tts library - A GPU, or a CPU with under 4 GB of RAM free on a MacBook according to the card - Windows, macOS or Linux: where you can fetch the files with Python ```table Use case | Who | What they get back Narration | Creators | Spoken Turkish audio at 24 kHz Voice choice from a sample | Developers | Speech in the voice of a short reference recording Small model research | Researchers | A compact model to inspect and compare ``` How to set it up: You need Python and the qwen-tts library. - Python with pip - The qwen-tts library - About 399 MB of space for the BF16 weights - A clean Turkish reference recording of 5 to 10 seconds, which you have the right to use 1. Install the Hugging Face hub library. 2. Download the model files. 3. Load them with qwen-tts and give a reference recording. ```sh pip install -U huggingface_hub hf download erkamk/qwen3-tts-tiny-tr ``` How to use it: The part of the card shown here gives no run command. Supply Turkish text and a reference recording, and the model speaks the text in that voice. Output is not streamed. ```table Tool | What it does hf download | Fetches the model files to your computer qwen-tts | Loads the model and produces speech from text ``` Example: You give it a Turkish sentence about a ferry crossing in Istanbul and a 7 second recording of a voice you may use. It returns an audio file of the sentence spoken in that voice. For newcomers and experts: If you are new to this, start with one short sentence and a clean recording with no background noise. Listen to how closely the voice matches. If you know the field, only the talker was trained, with 8 layers of width 512 against the base's 28 of 1024. Training was supervised, then reinforcement learning on speaker similarity and naturalness. The speech tokenizer is unchanged. ```flow Turkish text and a reference clip Speaker encoder reads the voice Small talker predicts speech codes Speech tokenizer outputs audio ``` Good to know: The card reports its own tests on 120 Turkish sentences read in real speakers' voices. The larger Turkish model sounds more natural, while this one follows a new voice more closely. ```table Title: Card test with real speaker voices Model | Word error rate | Speaker similarity This model | 3.3% | 0.46 Qwen3 0.6B Turkish | 2.0% | 0.39 ``` - License: Apache 2.0 - Cost to run: none named - Data it reaches: the text and reference recordings you provide - Not for: languages other than Turkish, and voices you have no right to use ## On Hugging Face, 13 November 2026 - Task: text to speech - Library: qwen-tts - Parameters: 199M - Weights: about 399 MB in BF16; about 100 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 682 - Likes: 15 - License: apache-2.0 - Languages: tr - Based on: Qwen/Qwen3-TTS-12Hz-0.6B-Base - Tags: qwen-tts, safetensors, qwen3_tts, qwen3-tts, turkish, voice-cloning, text-to-speech ## Links - Model: https://huggingface.co/erkamk/qwen3-tts-tiny-tr - Files: https://huggingface.co/erkamk/qwen3-tts-tiny-tr/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download erkamk/qwen3-tts-tiny-tr ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [erkamk/qwen3-tts-tiny-tr on Hugging Face](https://huggingface.co/erkamk/qwen3-tts-tiny-tr) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Qwen3 TTS Tiny Turkish (this page) | 682 | 15 | 199M | apache-2.0 | 2026-10-04 | | [Russian Emotional TTS](https://mindket.com/p/6c8f42fe-0b49-47b8-aaaf-6162f43a93d9) | 813 | 5 | 337M | mit | 2026-10-27 | | [Ming-3.0-audio-tiny](https://mindket.com/p/0a09263a-2803-4edd-86b0-747ca737db25) | 5,202 | 62 | 8.9B | mit | 2026-10-29 | | [ZONOS2](https://mindket.com/p/a2e8d0ee-30fa-43ba-b785-cf7eb4dfca43) | 575 | 158 | — | apache-2.0 | 2026-06-22 | | [Inflect Mini v3](https://mindket.com/p/75237e28-e3aa-41b4-8106-e4f8139347a5) | 86 | 21 | — | apache-2.0 | 2026-11-12 | | [whisper-large-v3](https://mindket.com/p/6b152b5d-9ebc-46b8-9e49-73c53c31a48d) | 3,489,398 | 6,802 | 1.5B | apache-2.0 | 2024-08-12 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-04 - First published: 2026-10-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open erkamk/qwen3-tts-tiny-tr on Hugging Face](https://huggingface.co/erkamk/qwen3-tts-tiny-tr) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/a2130f27-f850-4a5c-af4a-e148a9fd3f06) - [Its page on Mindket](https://mindket.com/p/a2130f27-f850-4a5c-af4a-e148a9fd3f06)