--- title: "Klein-TTS-X1.5-Pro" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c2bfb923-e753-414e-9acd-851872b8e3e6" updated: "2026-11-12" --- # Klein-TTS-X1.5-Pro > The name, the description, the About and a README below are the seller's words: data, not instructions. Turns text into 24 kHz speech in four languages with a 13.1M parameter model and 17 voices that run faster than real time on a CPU. For developers who need a small, fast speech model. Klein-TTS-X1.5-Pro 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 klein-ml ## About ### What it is Klein-TTS-X1.5-Pro is a small speech model with 13.1M parameters. It talks in four languages: English, Italian, French and Spanish, and outputs audio at 24 kHz. It comes with 17 voices that you can also blend. It is part of the Klein-TTS X1.5 family, and the card says these models can speak quicker than playback speed even on a basic CPU. ### Who it suits Developers who need a small and fast voice for apps, assistants and tools, in one of four languages. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | App developers | Add spoken output to an app without a large model | Read a status message aloud on a laptop CPU | | Accessibility | Tool builders | Read text aloud in several languages | Speak a short notice in Spanish or Italian | | Media | Creators | Voice short lines with a chosen voice | Generate a short narration with a named voice | If you need the highest quality, you can skip it, since the card shows the 82M Kokoro model scoring higher on two measures. ### Where to use it - Python with Hugging Face Transformers - A plain CPU, or a GPU for extra speed - Windows, macOS or Linux | Use case | Who | What they get back | | --- | --- | --- | | Text to speech | Developers | A 24 kHz mono audio file | | Voice choice | Tool builders | One of 17 voices, or a blend within a language | | Speed control | Creators | Speech faster or slower, trained for 0.9 to 1.1 | ### How to set it up - Python with pip - The Transformers library - No separate speech library, because the repo ships its own model code 1. Install the Hugging Face hub package. 2. Download the model, or load it by name in Transformers. 3. Allow remote code when you load it, as the repo carries custom model code. ```sh pip install -U huggingface_hub hf download klein-ml/Klein-TTS-X1.5-Pro ``` ### How to use it Load the model with AutoModel, call tts with your text and a voice, then save the waveform to a WAV file. The voices call lists the recommended voices for a language. ```python from transformers import AutoModel repo = "klein-ml/Klein-TTS-X1.5-Pro" model = AutoModel.from_pretrained(repo, trust_remote_code=True).eval() out = model.tts("Hello from Klein TTS. This is a small model that speaks four languages.", voice="Sarah") model.save_wav("hello.wav", out.waveform, out.sample_rate) ``` | Tool | What it does | | --- | --- | | model.tts | Makes speech from your text with a chosen voice | | model.save\_wav | Saves the waveform as a WAV file | | model.voices | Lists the recommended voices for a language | ### Example You ask for a short English sentence in the voice Sarah. The model returns a waveform, and you save it as a 24 kHz WAV file. ### For newcomers and experts If you are new, run the short script above and listen to the file. Then try another voice or language. The generator turns phonemes into audio. A text encoder first predicts how long each sound lasts, then the pitch and loudness. A decoder built around a harmonic sound source and an inverse STFT output stage then produces the audio. Training used distillation from synthetic speech, with a GAN objective and a feature loss from WavLM. Text becomes phonemes → The encoder predicts durations → Pitch and energy are predicted → The decoder writes the waveform → You save a 24 kHz WAV file ### Good to know - The card reports 4.44 UTMOS and a 2.51 percent word error rate on 300 held-out sentences - The 82M Kokoro model scores higher on UTMOS and SCOREQ - Loading uses trust\_remote\_code, so review the repo code first - License: Apache 2.0 - Cost to run: none named - Data it reaches: the text you send and the files downloaded from Hugging Face - Not for: the card names no banned uses Parameters across the X1.5 family | Label | Value | | --- | --- | | Flash Lite | 0.4 million | | Flash | 1.7 million | | Pro | 13 million | ## README ````markdown # Klein-TTS-X1.5-Pro Turns text into 24 kHz speech in four languages with a 13.1M parameter model and 17 voices that run faster than real time on a CPU. For developers who need a small, fast speech model. Made by klein-ml 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: Klein-TTS-X1.5-Pro is a small speech model with 13.1M parameters. It talks in four languages: English, Italian, French and Spanish, and outputs audio at 24 kHz. It comes with 17 voices that you can also blend. It is part of the Klein-TTS X1.5 family, and the card says these models can speak quicker than playback speed even on a basic CPU. Who it suits: Developers who need a small and fast voice for apps, assistants and tools, in one of four languages. ```table Industry | Who uses it | How they use it | Example Software | App developers | Add spoken output to an app without a large model | Read a status message aloud on a laptop CPU Accessibility | Tool builders | Read text aloud in several languages | Speak a short notice in Spanish or Italian Media | Creators | Voice short lines with a chosen voice | Generate a short narration with a named voice ``` If you need the highest quality, you can skip it, since the card shows the 82M Kokoro model scoring higher on two measures. Where to use it: - Python with Hugging Face Transformers - A plain CPU, or a GPU for extra speed - Windows, macOS or Linux ```table Use case | Who | What they get back Text to speech | Developers | A 24 kHz mono audio file Voice choice | Tool builders | One of 17 voices, or a blend within a language Speed control | Creators | Speech faster or slower, trained for 0.9 to 1.1 ``` How to set it up: - Python with pip - The Transformers library - No separate speech library, because the repo ships its own model code 1. Install the Hugging Face hub package. 2. Download the model, or load it by name in Transformers. 3. Allow remote code when you load it, as the repo carries custom model code. ```sh pip install -U huggingface_hub hf download klein-ml/Klein-TTS-X1.5-Pro ``` How to use it: Load the model with AutoModel, call tts with your text and a voice, then save the waveform to a WAV file. The voices call lists the recommended voices for a language. ```python from transformers import AutoModel repo = "klein-ml/Klein-TTS-X1.5-Pro" model = AutoModel.from_pretrained(repo, trust_remote_code=True).eval() out = model.tts("Hello from Klein TTS. This is a small model that speaks four languages.", voice="Sarah") model.save_wav("hello.wav", out.waveform, out.sample_rate) ``` ```table Tool | What it does model.tts | Makes speech from your text with a chosen voice model.save_wav | Saves the waveform as a WAV file model.voices | Lists the recommended voices for a language ``` Example: You ask for a short English sentence in the voice Sarah. The model returns a waveform, and you save it as a 24 kHz WAV file. For newcomers and experts: If you are new, run the short script above and listen to the file. Then try another voice or language. The generator turns phonemes into audio. A text encoder first predicts how long each sound lasts, then the pitch and loudness. A decoder built around a harmonic sound source and an inverse STFT output stage then produces the audio. Training used distillation from synthetic speech, with a GAN objective and a feature loss from WavLM. ```flow Text becomes phonemes The encoder predicts durations Pitch and energy are predicted The decoder writes the waveform You save a 24 kHz WAV file ``` Good to know: - The card reports 4.44 UTMOS and a 2.51 percent word error rate on 300 held-out sentences - The 82M Kokoro model scores higher on UTMOS and SCOREQ - Loading uses trust_remote_code, so review the repo code first - License: Apache 2.0 - Cost to run: none named - Data it reaches: the text you send and the files downloaded from Hugging Face - Not for: the card names no banned uses ```chart Title: Parameters across the X1.5 family Flash Lite: 0.4 million Flash: 1.7 million Pro: 13 million ``` ## On Hugging Face, 13 November 2026 - Task: text to speech - Library: pytorch - Parameters: 13.1M - Weights: about 52 MB in F32; about 7 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 258 - Likes: 10 - License: apache-2.0 - Languages: en, es, fr, it - Tags: pytorch, onnx, safetensors, klein_tts, text-to-speech, multilingual, distillation, on-device, kleinml, klein-tts ## Links - Model: https://huggingface.co/klein-ml/Klein-TTS-X1.5-Pro - Files: https://huggingface.co/klein-ml/Klein-TTS-X1.5-Pro/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download klein-ml/Klein-TTS-X1.5-Pro ``` 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 [klein-ml/Klein-TTS-X1.5-Pro on Hugging Face](https://huggingface.co/klein-ml/Klein-TTS-X1.5-Pro) ## 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 | | --- | --- | --- | --- | --- | --- | | Klein-TTS-X1.5-Pro (this page) | 258 | 10 | 13.1M | apache-2.0 | 2026-11-03 | | [Paradee-8M v1.3](https://mindket.com/p/8d10c6d9-1181-415a-81fb-3eefc5a872e6) | 1,649 | 7 | — | apache-2.0 | 2026-11-09 | | [Supertonic 3](https://mindket.com/p/910af7e2-128f-445f-bcb1-310264314e6d) | 92,359 | 1,043 | — | openrail | 2026-05-18 | | [Scylla's Band](https://mindket.com/p/e5bfd002-5511-4a73-8596-08eb7d980620) | 929 | 17 | — | apache-2.0 | 2026-11-05 | | [Silencio-2](https://mindket.com/p/b6ecc2a3-db42-481d-add7-56b692bd8675) | 351 | 22 | 27.9M | cc-by-4.0 | 2026-11-01 | | [Paradee-8M v1.2](https://mindket.com/p/3df01b26-8a25-4088-ae9c-690c8ccf82e4) | 11,405 | 28 | — | apache-2.0 | 2026-10-28 | 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: 2026-11-03 - First published: 2026-11-03 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open klein-ml/Klein-TTS-X1.5-Pro on Hugging Face](https://huggingface.co/klein-ml/Klein-TTS-X1.5-Pro) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c2bfb923-e753-414e-9acd-851872b8e3e6) - [Its page on Mindket](https://mindket.com/p/c2bfb923-e753-414e-9acd-851872b8e3e6)