--- title: "Silencio-2" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/b6ecc2a3-db42-481d-add7-56b692bd8675" updated: "2026-11-12" --- # Silencio-2 > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns English text into 24 kHz speech with emotion and sound tags, and can copy a voice from a short reference clip. A tiny text to speech model fine-tuned from Kyutai's Pocket TTS. Silencio-2 is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by BlueRoseLabs ## About ### What it is Silencio-2 is a small English text to speech model from BlueRoseLabs. It reads text aloud at 24 kHz in a bundled voice or in a voice copied from a few seconds of reference audio. You steer it inside the text with tags, such as a sigh or a whisper. It is a fine-tune of Kyutai's Pocket TTS. ### Who it suits Developers, game makers and content creators who want expressive English speech from a model that runs on a normal computer. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Add local speech output to an app | A narrator that reads status messages | | Games | Game makers | Voice characters with emotions and sounds | A line spoken in a whisper | | Media | Content creators | Draft voice-overs with sighs and laughs in the right places | A short clip with a laugh | | Accessibility | Tool builders | Read text aloud with a small, fast model | A screen reader prototype | If you need languages other than English, you can skip it, since the card lists English only. ### Where to use it - Windows, macOS or Linux with Python - Python programs that import the silencio2 package from the repository - CPU-only machines, where the card reports faster than real-time speech | Use case | Who | What they get back | | --- | --- | --- | | Plain text to speech | Developers | A wav file in one of the bundled voices | | Tagged speech | Creators | Sighs, laughs and emotions placed where you write them | | Voice cloning | Creators with permission | Speech in a voice taken from a short clean clip | | Streaming | App builders | Audio chunks played as they are generated | ### How to set it up - Python, to run the Hugging Face download tool - Room for a single 112 MB weights file - The requirements file from the repository, installed from a clone or with the snapshot folder on the Python path 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Install the packages in requirements.txt. 4. Import Silencio2TTS in Python and load the model. ```bash pip install -U huggingface_hub hf download BlueRoseLabs/Silencio-2 pip install -r requirements.txt ``` ### How to use it Load the model once, then call save to write a wav file. Sounds go directly into the text, an emotion tag applies to the text after it, and a voice can be a bundled name or a clip of your own. ```python from silencio2 import Silencio2TTS tts = Silencio2TTS.from_pretrained() tts.save("sigh.wav", "What a long day. [sigh]") tts.save("mixed.wav", " Keep your voice down. I said stop!") tts.save("clone.wav", "Hi! This is my voice.", voice="my_clip.wav") ``` | Tool | What it does | | --- | --- | | save | Writes the speech to a wav file | | generate | Returns the audio as a tensor, with a seed for repeatable takes | | stream | Yields audio chunks as they are produced | | sounds | Tags for a laugh, sigh, gasp, cough or throat clear | | emotions | Tags for happy, sad, angry, fear, surprised or whisper | ### Example You save the line "\ Keep your voice down" with the default bundled voice. The model writes a wav file in which that sentence is spoken in a whisper. If you write a tag the model does not know, it stops with an error and does not speak it. ### For newcomers and experts Start with one sentence and one bundled voice, then add a tag such as a sigh and listen to the change. Try voice cloning only with a clean clip of a voice you have the right to use. Under the hood it is a flow-based language model. For each audio frame it predicts Mimi codec latents, with the text and a voice prompt as input. Cloning relies on an encoder that takes up the rest of the 27.9M parameters, while about 18.2M handle the speech itself. A tokenizer with 11 extra tokens covers the tags. Write text with tags → Tokenizer reads text and tags → Model predicts Mimi codec latents → Decoder outputs 24 kHz audio ### Good to know - License: CC BY 4.0 - Cost to run: none named - Data it reaches: downloads files from Hugging Face, then runs locally and reads any reference clip you give it - Not for: languages other than English - Not for: cloning a voice you have no right to use, so use only voices you have permission for ## README ````markdown # Silencio-2 Turns English text into 24 kHz speech with emotion and sound tags, and can copy a voice from a short reference clip. A tiny text to speech model fine-tuned from Kyutai's Pocket TTS. Made by BlueRoseLabs 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: Silencio-2 is a small English text to speech model from BlueRoseLabs. It reads text aloud at 24 kHz in a bundled voice or in a voice copied from a few seconds of reference audio. You steer it inside the text with tags, such as a sigh or a whisper. It is a fine-tune of Kyutai's Pocket TTS. Who it suits: Developers, game makers and content creators who want expressive English speech from a model that runs on a normal computer. ```table Industry | Who uses it | How they use it | Example Software | Developers | Add local speech output to an app | A narrator that reads status messages Games | Game makers | Voice characters with emotions and sounds | A line spoken in a whisper Media | Content creators | Draft voice-overs with sighs and laughs in the right places | A short clip with a laugh Accessibility | Tool builders | Read text aloud with a small, fast model | A screen reader prototype ``` If you need languages other than English, you can skip it, since the card lists English only. Where to use it: - Windows, macOS or Linux with Python - Python programs that import the silencio2 package from the repository - CPU-only machines, where the card reports faster than real-time speech ```table Use case | Who | What they get back Plain text to speech | Developers | A wav file in one of the bundled voices Tagged speech | Creators | Sighs, laughs and emotions placed where you write them Voice cloning | Creators with permission | Speech in a voice taken from a short clean clip Streaming | App builders | Audio chunks played as they are generated ``` How to set it up: - Python, to run the Hugging Face download tool - Room for a single 112 MB weights file - The requirements file from the repository, installed from a clone or with the snapshot folder on the Python path 1. Install the Hugging Face hub tools with pip. 2. Download the model files with the hf command. 3. Install the packages in requirements.txt. 4. Import Silencio2TTS in Python and load the model. ```bash pip install -U huggingface_hub hf download BlueRoseLabs/Silencio-2 pip install -r requirements.txt ``` How to use it: Load the model once, then call save to write a wav file. Sounds go directly into the text, an emotion tag applies to the text after it, and a voice can be a bundled name or a clip of your own. ```python from silencio2 import Silencio2TTS tts = Silencio2TTS.from_pretrained() tts.save("sigh.wav", "What a long day. [sigh]") tts.save("mixed.wav", " Keep your voice down. I said stop!") tts.save("clone.wav", "Hi! This is my voice.", voice="my_clip.wav") ``` ```table Tool | What it does save | Writes the speech to a wav file generate | Returns the audio as a tensor, with a seed for repeatable takes stream | Yields audio chunks as they are produced sounds | Tags for a laugh, sigh, gasp, cough or throat clear emotions | Tags for happy, sad, angry, fear, surprised or whisper ``` Example: You save the line " Keep your voice down" with the default bundled voice. The model writes a wav file in which that sentence is spoken in a whisper. If you write a tag the model does not know, it stops with an error and does not speak it. For newcomers and experts: Start with one sentence and one bundled voice, then add a tag such as a sigh and listen to the change. Try voice cloning only with a clean clip of a voice you have the right to use. Under the hood it is a flow-based language model. For each audio frame it predicts Mimi codec latents, with the text and a voice prompt as input. Cloning relies on an encoder that takes up the rest of the 27.9M parameters, while about 18.2M handle the speech itself. A tokenizer with 11 extra tokens covers the tags. ```flow Write text with tags Tokenizer reads text and tags Model predicts Mimi codec latents Decoder outputs 24 kHz audio ``` Good to know: - License: CC BY 4.0 - Cost to run: none named - Data it reaches: downloads files from Hugging Face, then runs locally and reads any reference clip you give it - Not for: languages other than English - Not for: cloning a voice you have no right to use, so use only voices you have permission for ## On Hugging Face, 13 November 2026 - Task: text to speech - Library: pytorch - Parameters: 27.9M - Weights: about 112 MB in F32; about 14 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 351 - Likes: 22 - License: cc-by-4.0 - Languages: en - Based on: kyutai/pocket-tts - Tags: pytorch, safetensors, text-to-speech, voice-cloning, emotional-speech, streaming, on-device ## Links - Model: https://huggingface.co/BlueRoseLabs/Silencio-2 - Files: https://huggingface.co/BlueRoseLabs/Silencio-2/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download BlueRoseLabs/Silencio-2 ``` 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 [BlueRoseLabs/Silencio-2 on Hugging Face](https://huggingface.co/BlueRoseLabs/Silencio-2) ## 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 | | --- | --- | --- | --- | --- | --- | | Silencio-2 (this page) | 351 | 22 | 27.9M | cc-by-4.0 | 2026-11-01 | | [Maya 2 Native](https://mindket.com/p/1c8868e5-58de-48eb-9c9b-a9de50e1f0f7) | 5,774 | 170 | 2.5B | apache-2.0 | 2026-11-03 | | [phonon-31m-en](https://mindket.com/p/8936ffbe-babc-4766-b999-5315308551db) | 248,578 | 160 | 41.1M | apache-2.0 | 2026-10-22 | | [Klein-TTS-X1.5-Pro](https://mindket.com/p/c2bfb923-e753-414e-9acd-851872b8e3e6) | 258 | 10 | 13.1M | apache-2.0 | 2026-11-03 | | [RubatoCodec](https://mindket.com/p/d379055b-79cb-481c-b464-606aa8844898) | 670 | 18 | 246M | apache-2.0 | 2026-10-28 | | [Earlybird by sahilmahendrakar](https://mindket.com/p/63374aaf-908f-49b3-9cfc-811cda44e66e) | 89 | 24 | — | mit | 2026-11-11 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · Nikuson · 2026-11-02](https://news.ycombinator.com/item?id=50343715): Show HN: Silencio 2 – an 18M TTS with emotion tags that runs on a laptop ## 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: 2026-11-01 - First published: 2026-10-31 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open BlueRoseLabs/Silencio-2 on Hugging Face](https://huggingface.co/BlueRoseLabs/Silencio-2) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/b6ecc2a3-db42-481d-add7-56b692bd8675) - [Its page on Mindket](https://mindket.com/p/b6ecc2a3-db42-481d-add7-56b692bd8675)