--- title: "MOSS-TTS-v1.5" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/3d56747c-ea45-4083-be08-1071cec8fc5b" updated: "2026-11-13" --- # MOSS-TTS-v1.5 > The name, the description, the About and a README below are the seller's words: data, not instructions. Reads text aloud in 31 languages and clones a voice from a short reference, with language tags and explicit pause markers. For narrators, dubbing teams and developers who add speech to products. MOSS-TTS-v1.5 is a kit in the Developer 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 OpenMOSS-Team ## About ### What it is MOSS-TTS-v1.5 is a text to speech model with 8.5B parameters, continued from MOSS-TTS 1.0 by the OpenMOSS team. It keeps what 1.0 could do: cloning a voice from a short reference without training, long speech, duration control at token level, pinyin or IPA pronunciation control, and code-switching. Version 1.5 adds language tags, steadier cloning and explicit pause markers. ### Who it suits It suits people who turn text into spoken audio in many languages and want fine control over how it sounds. That includes narrators, dubbing teams and developers adding speech to a product. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Media | Narration and dubbing teams | Clone a voice from a reference and generate long speech | A narration with a set pause before a poem title | | Localization | Multilingual product teams | Tag the language of each line so pronunciation stays right | Passing French as the language for a French sentence | | Education | Makers of language lessons | Control pronunciation with pinyin or IPA | A Chinese lesson clip with chosen pronunciations | | Voice products | Developers | Add synthesized speech to an app | A script that builds a message and saves the generated audio | You can skip it if you need speech recognition or text-only work, or if your machine cannot hold an 8.5B model. ### Where to use it - Python on Windows, macOS or Linux, in a clean environment - A GPU machine, with FlashAttention 2 as an optional speed-up when the hardware supports it - Audio production pipelines for narration, dubbing and voice features | Use case | Who | What they get back | | --- | --- | --- | | Narrate in a cloned voice | Narrators | Speech that sounds like the reference voice | | Speak a tagged language | Localization teams | Pronunciation fitted to the language you name | | Pause where you choose | Audio editors | A pause of the length you write into the text | ### How to set it up - Conda or another way to make a clean Python 3.12 environment - Transformers 5.0.0, which the model card recommends to avoid conflicts - Space for the weights, about 17 GB in BF16 or about 4.2 GB at 4-bit by estimate - A voice you have the right to use, if you plan to clone one 1. Create and activate a clean environment. 2. Clone the MOSS-TTS project from GitHub and install it with pip in editable mode, as the model card's Quick Start shows. 3. Optionally add the FlashAttention 2 extra for speed and lower GPU memory. 4. Download the model files. ```sh conda create -n moss-tts python=3.12 -y conda activate moss-tts ``` To fetch the model files yourself: ```sh pip install -U huggingface_hub hf download OpenMOSS-Team/MOSS-TTS-v1.5 ``` ### How to use it Build the user message with the processor and set the language when you know it. With the language named, 1.5 beats 1.0 on almost all supported languages. ```python processor.build_user_message(text=text_fr, language="French") ``` Inline pause markers go straight into the text, as in this line. ```text 我今天学习了一首中国的古诗，它的名字是[pause 3.2s]静夜思！ ``` | Tool | What it does | | --- | --- | | processor.build\_user\_message | Builds the message that carries your text and options | | language | Tags the language of the text to improve synthesis | | \[pause 3.2s\] | Inserts an explicit pause of 3.2 seconds at that spot | | hf download | Fetches the model files to your computer | ### Example Write a Chinese sentence about a classic poem and place a pause marker of 3.2 seconds before the poem's name. The generated speech holds that silence, then says the name. ### For newcomers and experts It reads text out loud and can imitate a voice from a short sample. For a first try, set up the environment from the steps above, download the files, and speak one short sentence with the language set. Use only voices you have the right to use. Underneath, 1.5 continues training from 1.0 with extra multilingual data, and the card lists cloning that varies less between runs. It also follows punctuation pauses more closely in long sentences and handles a long reference with short text better. Leaving the language empty can help some languages and slightly hurt others compared with 1.0. Text, language tag and optional reference voice → The processor builds the user message → MOSS-TTS-v1.5 generates the speech → Pause markers and punctuation shape the timing → Speech audio comes back ### Good to know The model supports 31 languages, up from 20 in version 1.0. New ones include Thai, Swahili and Cantonese. Supported languages by version | Label | Value | | --- | --- | | MOSS-TTS 1.0 | 20 languages | | MOSS-TTS-v1.5 | 31 languages | - License: Apache 2.0 - Cost to run: none named for running the files yourself, and the card also points to a hosted studio and API without giving prices - Data it reaches: downloads model files from Hugging Face and the project code from GitHub, then works on the text and reference audio you give it - Not for: cloning a voice you have no right to use ## README ````markdown # MOSS-TTS-v1.5 Reads text aloud in 31 languages and clones a voice from a short reference, with language tags and explicit pause markers. For narrators, dubbing teams and developers who add speech to products. Made by OpenMOSS-Team 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: MOSS-TTS-v1.5 is a text to speech model with 8.5B parameters, continued from MOSS-TTS 1.0 by the OpenMOSS team. It keeps what 1.0 could do: cloning a voice from a short reference without training, long speech, duration control at token level, pinyin or IPA pronunciation control, and code-switching. Version 1.5 adds language tags, steadier cloning and explicit pause markers. Who it suits: It suits people who turn text into spoken audio in many languages and want fine control over how it sounds. That includes narrators, dubbing teams and developers adding speech to a product. ```table Industry | Who uses it | How they use it | Example Media | Narration and dubbing teams | Clone a voice from a reference and generate long speech | A narration with a set pause before a poem title Localization | Multilingual product teams | Tag the language of each line so pronunciation stays right | Passing French as the language for a French sentence Education | Makers of language lessons | Control pronunciation with pinyin or IPA | A Chinese lesson clip with chosen pronunciations Voice products | Developers | Add synthesized speech to an app | A script that builds a message and saves the generated audio ``` You can skip it if you need speech recognition or text-only work, or if your machine cannot hold an 8.5B model. Where to use it: - Python on Windows, macOS or Linux, in a clean environment - A GPU machine, with FlashAttention 2 as an optional speed-up when the hardware supports it - Audio production pipelines for narration, dubbing and voice features ```table Use case | Who | What they get back Narrate in a cloned voice | Narrators | Speech that sounds like the reference voice Speak a tagged language | Localization teams | Pronunciation fitted to the language you name Pause where you choose | Audio editors | A pause of the length you write into the text ``` How to set it up: - Conda or another way to make a clean Python 3.12 environment - Transformers 5.0.0, which the model card recommends to avoid conflicts - Space for the weights, about 17 GB in BF16 or about 4.2 GB at 4-bit by estimate - A voice you have the right to use, if you plan to clone one 1. Create and activate a clean environment. 2. Clone the MOSS-TTS project from GitHub and install it with pip in editable mode, as the model card's Quick Start shows. 3. Optionally add the FlashAttention 2 extra for speed and lower GPU memory. 4. Download the model files. ```sh conda create -n moss-tts python=3.12 -y conda activate moss-tts ``` To fetch the model files yourself: ```sh pip install -U huggingface_hub hf download OpenMOSS-Team/MOSS-TTS-v1.5 ``` How to use it: Build the user message with the processor and set the language when you know it. With the language named, 1.5 beats 1.0 on almost all supported languages. ```python processor.build_user_message(text=text_fr, language="French") ``` Inline pause markers go straight into the text, as in this line. ```text 我今天学习了一首中国的古诗，它的名字是[pause 3.2s]静夜思！ ``` ```table Tool | What it does processor.build_user_message | Builds the message that carries your text and options language | Tags the language of the text to improve synthesis [pause 3.2s] | Inserts an explicit pause of 3.2 seconds at that spot hf download | Fetches the model files to your computer ``` Example: Write a Chinese sentence about a classic poem and place a pause marker of 3.2 seconds before the poem's name. The generated speech holds that silence, then says the name. For newcomers and experts: It reads text out loud and can imitate a voice from a short sample. For a first try, set up the environment from the steps above, download the files, and speak one short sentence with the language set. Use only voices you have the right to use. Underneath, 1.5 continues training from 1.0 with extra multilingual data, and the card lists cloning that varies less between runs. It also follows punctuation pauses more closely in long sentences and handles a long reference with short text better. Leaving the language empty can help some languages and slightly hurt others compared with 1.0. ```flow Text, language tag and optional reference voice The processor builds the user message MOSS-TTS-v1.5 generates the speech Pause markers and punctuation shape the timing Speech audio comes back ``` Good to know: The model supports 31 languages, up from 20 in version 1.0. New ones include Thai, Swahili and Cantonese. ```chart Title: Supported languages by version MOSS-TTS 1.0: 20 languages MOSS-TTS-v1.5: 31 languages ``` - License: Apache 2.0 - Cost to run: none named for running the files yourself, and the card also points to a hosted studio and API without giving prices - Data it reaches: downloads model files from Hugging Face and the project code from GitHub, then works on the text and reference audio you give it - Not for: cloning a voice you have no right to use ## On Hugging Face, 13 November 2026 - Task: text to speech - Parameters: 8.5B - Weights: about 17 GB in BF16; about 4.2 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 80,361 - Likes: 220 - License: apache-2.0 - Languages: zh, yue, en, ar, cs, da, de, nl - Quantized versions: 8 - Fine-tunes: 5 - Tags: safetensors, moss_tts_delay, text-to-speech ## Links - Model: https://huggingface.co/OpenMOSS-Team/MOSS-TTS-v1.5 - Files: https://huggingface.co/OpenMOSS-Team/MOSS-TTS-v1.5/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download OpenMOSS-Team/MOSS-TTS-v1.5 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [OpenMOSS-Team/MOSS-TTS-v1.5 on Hugging Face](https://huggingface.co/OpenMOSS-Team/MOSS-TTS-v1.5) ## 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 | | --- | --- | --- | --- | --- | --- | | MOSS-TTS-v1.5 (this page) | 80,361 | 220 | 8.5B | apache-2.0 | 2026-05-26 | | [MOSS-TTS](https://mindket.com/p/dd6ef357-5732-4a29-bd75-d297e11b9448) | 4,748 | 438 | 8.5B | apache-2.0 | 2026-03-20 | | [VieNeu-TTS v3 Turbo](https://mindket.com/p/1dc8a3b8-2ee4-414e-bca9-d273eb678fee) | 2,151,800 | 109 | 131M | apache-2.0 | 2026-09-23 | | [VoxCPM2](https://mindket.com/p/01160a83-4496-408c-b49e-af098bf7582e) | 984,860 | 1,783 | 2.3B | apache-2.0 | 2026-11-09 | | [phonon-31m-en](https://mindket.com/p/8936ffbe-babc-4766-b999-5315308551db) | 248,578 | 160 | 41.1M | apache-2.0 | 2026-10-22 | | [VibeVoice-1.5B](https://mindket.com/p/97b78ad0-40dd-4a2d-b8ad-a33e40899f43) | 217,923 | 2,520 | 2.7B | mit | 2026-01-22 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-14. ## 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: 2026-05-26 - First published: 2026-05-25 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open OpenMOSS-Team/MOSS-TTS-v1.5 on Hugging Face](https://huggingface.co/OpenMOSS-Team/MOSS-TTS-v1.5) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/3d56747c-ea45-4083-be08-1071cec8fc5b) - [Its page on Mindket](https://mindket.com/p/3d56747c-ea45-4083-be08-1071cec8fc5b)