--- title: "Russian Emotional TTS" kind: "Kit" category: "Productivity" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/6c8f42fe-0b49-47b8-aaaf-6162f43a93d9" updated: "2026-11-13" --- # Russian Emotional TTS > The name, the description, the About and a README below are the seller's words: data, not instructions. Speaks Russian text with emotion and marked word stress, and can copy a voice from a 3 to 10 second clip. Clone only voices you have the right to use. Russian Emotional TTS 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 Andreevai ## About ### What it is This is a Russian text to speech and voice cloning model built on the F5-TTS design, which uses a diffusion transformer with flow matching. Andreevai fine-tuned it on expressive theatrical dialogues that carry stress annotations. The whole package comes to about 350 million parameters and outputs 24,000 Hz mono audio. ### Who it suits It suits creators and developers who need expressive spoken Russian, and anyone who wants correct stress on each word. The record lists Russian only. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Media | Video and audio creators | Narrate Russian scripts with emotion | A voice-over for a short video | | Games | Indie developers | Give characters spoken Russian lines | Lines for a story character | | Education | E-learning teams | Read lessons aloud with correct word stress | A lesson with stress marks on tricky words | People who need languages other than Russian can skip it, since the model was fine-tuned on Russian only. ### Where to use it - Python, using the f5-tts package - Google Colab, through the notebook the card offers, on a free T4 GPU - A Gradio web page that the Colab notebook opens - Windows, macOS or Linux with Python, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Russian narration | Creators | A wav file of the script read aloud | | Voice matching | Developers | Speech in the timbre of a 3 to 10 second reference clip | | Stress control | Teachers | Words pronounced with the stress you mark | ### How to set it up You need a few things first. - Python and pip - A clean reference clip of 3 to 10 seconds, only of a voice you have the right to use - The model.safetensors and vocab.txt files from this repository 1. Install the f5-tts package with the other two libraries. 2. Download the repository files. 3. Put your reference clip next to the script. 4. Run the Python code below. ```bash pip install f5-tts soundfile torchaudio ``` ```sh pip install -U huggingface_hub hf download Andreevai/f5-tts-russian-emotional ``` ### How to use it You give the model a reference clip, its transcript and the text to speak. Put a plus sign before each stressed vowel to control the stress. The script picks the GPU when one is available and otherwise falls back to the CPU. ```python import torch import soundfile as sf from f5_tts.infer.utils_infer import infer_process, load_model, load_vocoder from f5_tts.model import DiT device = "cuda" if torch.cuda.is_available() else "cpu" # Load neural vocoder vocoder = load_vocoder(vocoder_name="vocos", device=device) # Load acoustic DiT model model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4) model = load_model( DiT, model_cfg, "model.safetensors", vocab_file="vocab.txt", device=device ) # Zero-shot inference wave, sr, _ = infer_process( ref_audio="reference.wav", ref_text="Текст из образца голоса", gen_text="Прив+ет! Это русская нейрос+еть для озв+учки.", model_obj=model, vocoder=vocoder, device=device, nfe_step=32, speed=1.0 ) sf.write("output.wav", wave, sr) ``` | Tool | What it does | | --- | --- | | load\_vocoder | Loads the Vocos neural vocoder | | load\_model with DiT | Loads the acoustic model from model.safetensors and vocab.txt | | infer\_process | Generates speech from the reference clip, its text and the text to speak | | Plus sign before a vowel | Marks the stressed vowel in the text | | sf.write | Saves the audio as a wav file | ### Example You supply a short clean reference clip with its transcript, then ask for a Russian greeting that introduces the tool, with plus signs on the stressed vowels. The script writes output.wav in the voice of the clip. ### For newcomers and experts A text to speech model reads written text aloud, and voice cloning copies how a speaker sounds. If you are new, run the Colab notebook, upload a short clip of a voice you may use and type a Russian sentence. The model is the F5-TTS DiT Base v2 design, fine-tuned on more than 14 hours of emotional actor dialogues. It pairs a 337M diffusion transformer with a 13.5M Vocos vocoder at 24 kHz. The weights are plain safetensors, so loading avoids pickle files. The example runs 32 inference steps at normal speed. Reference clip and its text go in → Text with stress marks is encoded → Diffusion transformer builds the speech → Vocos vocoder turns it into audio → Wav file is written to disk ### Good to know - License: MIT, which the card describes as free for commercial and personal use. - Cost to run: no API key or paid service named. A GPU helps, and the free Colab T4 works. - Data it reaches: the reference audio and text you give it, and the wav file it writes. Use only voices you have the right to use. - Not for: languages other than Russian, or cloning a voice without permission. ## README ````markdown # Russian Emotional TTS Speaks Russian text with emotion and marked word stress, and can copy a voice from a 3 to 10 second clip. Clone only voices you have the right to use. Made by Andreevai 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 is a Russian text to speech and voice cloning model built on the F5-TTS design, which uses a diffusion transformer with flow matching. Andreevai fine-tuned it on expressive theatrical dialogues that carry stress annotations. The whole package comes to about 350 million parameters and outputs 24,000 Hz mono audio. Who it suits: It suits creators and developers who need expressive spoken Russian, and anyone who wants correct stress on each word. The record lists Russian only. ```table Industry | Who uses it | How they use it | Example Media | Video and audio creators | Narrate Russian scripts with emotion | A voice-over for a short video Games | Indie developers | Give characters spoken Russian lines | Lines for a story character Education | E-learning teams | Read lessons aloud with correct word stress | A lesson with stress marks on tricky words ``` People who need languages other than Russian can skip it, since the model was fine-tuned on Russian only. Where to use it: - Python, using the f5-tts package - Google Colab, through the notebook the card offers, on a free T4 GPU - A Gradio web page that the Colab notebook opens - Windows, macOS or Linux with Python, for downloading the files ```table Use case | Who | What they get back Russian narration | Creators | A wav file of the script read aloud Voice matching | Developers | Speech in the timbre of a 3 to 10 second reference clip Stress control | Teachers | Words pronounced with the stress you mark ``` How to set it up: You need a few things first. - Python and pip - A clean reference clip of 3 to 10 seconds, only of a voice you have the right to use - The model.safetensors and vocab.txt files from this repository 1. Install the f5-tts package with the other two libraries. 2. Download the repository files. 3. Put your reference clip next to the script. 4. Run the Python code below. ```bash pip install f5-tts soundfile torchaudio ``` ```sh pip install -U huggingface_hub hf download Andreevai/f5-tts-russian-emotional ``` How to use it: You give the model a reference clip, its transcript and the text to speak. Put a plus sign before each stressed vowel to control the stress. The script picks the GPU when one is available and otherwise falls back to the CPU. ```python import torch import soundfile as sf from f5_tts.infer.utils_infer import infer_process, load_model, load_vocoder from f5_tts.model import DiT device = "cuda" if torch.cuda.is_available() else "cpu" # Load neural vocoder vocoder = load_vocoder(vocoder_name="vocos", device=device) # Load acoustic DiT model model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4) model = load_model( DiT, model_cfg, "model.safetensors", vocab_file="vocab.txt", device=device ) # Zero-shot inference wave, sr, _ = infer_process( ref_audio="reference.wav", ref_text="Текст из образца голоса", gen_text="Прив+ет! Это русская нейрос+еть для озв+учки.", model_obj=model, vocoder=vocoder, device=device, nfe_step=32, speed=1.0 ) sf.write("output.wav", wave, sr) ``` ```table Tool | What it does load_vocoder | Loads the Vocos neural vocoder load_model with DiT | Loads the acoustic model from model.safetensors and vocab.txt infer_process | Generates speech from the reference clip, its text and the text to speak Plus sign before a vowel | Marks the stressed vowel in the text sf.write | Saves the audio as a wav file ``` Example: You supply a short clean reference clip with its transcript, then ask for a Russian greeting that introduces the tool, with plus signs on the stressed vowels. The script writes output.wav in the voice of the clip. For newcomers and experts: A text to speech model reads written text aloud, and voice cloning copies how a speaker sounds. If you are new, run the Colab notebook, upload a short clip of a voice you may use and type a Russian sentence. The model is the F5-TTS DiT Base v2 design, fine-tuned on more than 14 hours of emotional actor dialogues. It pairs a 337M diffusion transformer with a 13.5M Vocos vocoder at 24 kHz. The weights are plain safetensors, so loading avoids pickle files. The example runs 32 inference steps at normal speed. ```flow Reference clip and its text go in Text with stress marks is encoded Diffusion transformer builds the speech Vocos vocoder turns it into audio Wav file is written to disk ``` Good to know: - License: MIT, which the card describes as free for commercial and personal use. - Cost to run: no API key or paid service named. A GPU helps, and the free Colab T4 works. - Data it reaches: the reference audio and text you give it, and the wav file it writes. Use only voices you have the right to use. - Not for: languages other than Russian, or cloning a voice without permission. ## On Hugging Face, 13 November 2026 - Task: text to speech - Library: f5-tts - Parameters: 337M - Weights: about 1.3 GB in F32; about 169 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 813 - Likes: 5 - License: mit - Languages: ru - Tags: f5-tts, safetensors, text-to-speech, speech, flow-matching, audio, russian, voice-cloning ## Links - Model: https://huggingface.co/Andreevai/f5-tts-russian-emotional - Files: https://huggingface.co/Andreevai/f5-tts-russian-emotional/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Andreevai/f5-tts-russian-emotional ``` 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 [Andreevai/f5-tts-russian-emotional on Hugging Face](https://huggingface.co/Andreevai/f5-tts-russian-emotional) ## 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 | | --- | --- | --- | --- | --- | --- | | Russian Emotional TTS (this page) | 813 | 5 | 337M | mit | 2026-10-27 | | [Qwen3 TTS Tiny Turkish](https://mindket.com/p/a2130f27-f850-4a5c-af4a-e148a9fd3f06) | 682 | 15 | 199M | apache-2.0 | 2026-10-04 | | [Ming-3.0-audio-tiny](https://mindket.com/p/0a09263a-2803-4edd-86b0-747ca737db25) | 5,202 | 62 | 8.9B | mit | 2026-10-29 | | [MOSS-Transcribe-Diarize](https://mindket.com/p/54596977-df9c-49a1-98ed-0a671e987045) | 121,031 | 520 | 909M | apache-2.0 | 2026-10-31 | | [MOSS-Transcribe-Diarize 0.9B](https://mindket.com/p/c63ab4f4-7101-4119-a323-abf46aefd2f1) | 759 | 5 | 909M | apache-2.0 | 2026-11-06 | | [orukqwen-asr-1.7b](https://mindket.com/p/0e586967-b382-45bd-8cf6-969ca6f6477e) | 222 | 6 | 2B | apache-2.0 | 2026-11-11 | 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: 2026-10-27 - First published: 2026-10-27 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Andreevai/f5-tts-russian-emotional on Hugging Face](https://huggingface.co/Andreevai/f5-tts-russian-emotional) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/6c8f42fe-0b49-47b8-aaaf-6162f43a93d9) - [Its page on Mindket](https://mindket.com/p/6c8f42fe-0b49-47b8-aaaf-6162f43a93d9)