Kokoro is an open-weight TTS model with 82 million parameters. Despite its lightweight architecture, it delivers comparable quality to larger models while being significantly faster and more cost-efficient. With Apache-licensed weights, Kokoro can be deployed anywhere from production environments to personal projects. You can run this basic cell on Google Colab. Listen to samples. For more languages and details, see Advanced Usage. Under the hood, kokoro uses misaki, a G2P library at https://github.com/hexgrad/misaki Model SHA256 Hash: 496dba118d1a58f5f3db2efc88dbdc216e0483fc89fe6e47ee1f2c53f18ad1e4 Data: Kokoro was trained exclusively on permissive/non-copyrighted audio data and IPA…
Open weights
apache-2.0
Model · Text to speech
Qwen
Qwen3-TTS covers 10 major languages (Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian) as well as multiple dialectal voice profiles to meet global application needs. In addition, the models feature strong contextual understanding, enabling adaptive control of tone, speaking rate, and emotional expression based on instructions and text semantics, and they show markedly improved robustness to noisy input text. Key features: Intelligent Text Understanding and Voice Control: Supports speech generation driven by natural language instructions, allowing for flexible control over multi-dimensional acoustic attributes such as timbre, emotion, and prosody.…
Open weights
apache-2.0
1.9B parameters
OmniVoice is a massively multilingual zero-shot text-to-speech (TTS) model supporting over 600 languages. Built on a novel diffusion language model-style architecture, it delivers high-quality speech with superior inference speed, supporting voice cloning and voice design. - 600+ Languages Supported: The broadest language coverage among zero-shot TTS models. To get started, install the omnivoice library: You can use OmniVoice for zero-shot voice cloning as follows: For more generation modes (e.g., voice design), functions (e.g., non-verbal symbols, pronunciation correction) and comprehensive usage instructions, see our GitHub Repository. You can directly discuss on GitHub Issues. You can…
Open weights
613M parameters
40,960 tokens
omnivoice
Model · Text to speech
Qwen
Qwen3-TTS is a series of advanced multilingual, controllable, robust, and streaming text-to-speech models developed by the Qwen team. This specific checkpoint is the 0.6B CustomVoice variant, based on the 12Hz tokenizer. It supports 9 premium timbres and allows for fine-grained style control over target voices via natural language instructions across 10 major languages. To use Qwen3-TTS, you can install the qwen-tts package: For Qwen3-TTS-12Hz-0.6B-CustomVoice, the following speakers are supported. We recommend using each speaker’s native language for the best results: If you find Qwen3-TTS useful for your research, please consider citing
Open weights
apache-2.0
906M parameters
Model · Text to speech
Qwen
Qwen3-TTS is a family of advanced multilingual, controllable, robust, and streaming text-to-speech models. Trained on over 5 million hours of speech data spanning 10 languages, Qwen3-TTS supports state-of-the-art 3-second voice cloning and description-based control. This specific checkpoint is the 0.6B Base model, which is capable of rapid voice cloning from a user-provided audio input. To clone a voice and synthesize new content using the Base model, you can use the following code snippet: Qwen3-TTS covers 10 major languages (Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian) as well as multiple dialectal voice profiles to meet global application…
Open weights
apache-2.0
915M parameters
VieNeu-TTS v3 Turbo is the next generation of Vietnamese TTS — 48 kHz high-fidelity speech, 23 built-in preset voices across three regions (North / Central / South), instant voice cloning, real-time streaming with an OpenAI-compatible API (16 concurrent streams on one RTX 3060), inline emotion cues, and seamless bilingual (En–Vi) code-switching. The reference implementation is the vieneu Python SDK (v3.7.1). Its minimal install is torch-free: on CPU everything runs on ONNX Runtime (PyTorch is never imported), and on a CUDA machine it auto-switches to the PyTorch engine with automatic batching and a continuous-batching stream scheduler — same API, no code change. The VieNeu-TTS v3 Turbo…
Open weights
apache-2.0
131M parameters
1,024 tokens
VibeVoice is a novel framework designed for generating expressive, long-form, multi-speaker conversational audio, such as podcasts, from text. It addresses significant challenges in traditional Text-to-Speech (TTS) systems, particularly in scalability, speaker consistency, and natural turn-taking. A core innovation of VibeVoice is its use of continuous speech tokenizers (Acoustic and Semantic) operating at an ultra-low frame rate of 7.5 Hz. These tokenizers efficiently preserve audio fidelity while significantly boosting computational efficiency for processing long sequences. VibeVoice employs a next-token diffusion framework, leveraging a Large Language Model (LLM) to understand textual…
Open weights
mit
2.7B parameters
transformers
VoxCPM2 is a tokenizer-free, diffusion autoregressive Text-to-Speech model — 2B parameters, 30 languages, 48kHz audio output, trained on over 2 million hours of multilingual speech data. - 30-Language Multilingual — No language tag needed; input text in any supported language directly - Voice Design — Generate a novel voice from a natural-language description alone (gender, age, tone, emotion, pace…); no reference audio required - Controllable Cloning — Clone any voice from a short clip, with optional style guidance to steer emotion, pace, and expression while preserving timbre - Ultimate Cloning — Provide reference audio + its transcript for audio-continuation cloning; every vocal nuance…
Open weights
apache-2.0
2.3B parameters
voxcpm
VibeVoice-Realtime is a lightweight real‑time text-to-speech model supporting streaming text input and robust long-form speech generation. It can be used to build realtime TTS services, narrate live data streams, and let different LLMs start speaking from their very first tokens (plug in your preferred model) long before a full answer is generated. It produces initial audible speech in ~300 ms (hardware dependent). ▶ Watch demo video (Launch your own realtime demo via the websocket example in Usage) Although the model is primarily built for English, we found that it still exhibits a certain level of multilingual capability—and even performs reasonably well in some languages. We provide nine…
Open weights
mit
1B parameters
transformers
Indic Parler-TTS is a multilingual Indic extension of Parler-TTS Mini. It is a fine-tuned version of Indic Parler-TTS Pretrained, trained on a 1,806 hours multilingual Indic and English dataset. Indic Parler-TTS Mini can officially speak in 20 Indic languages, making it comprehensive for regional language technologies, and in English. The 21 languages supported are: Assamese, Bengali, Bodo, Dogri, English, Gujarati, Hindi, Kannada, Konkani, Maithili, Malayalam, Manipuri, Marathi, Nepali, Odia, Sanskrit, Santali, Sindhi, Tamil, Telugu, and Urdu. Thanks to its better prompt tokenizer, it can easily be extended to other languages. This tokenizer has a larger vocabulary and handles byte…
Access requested at publisher
apache-2.0
938M parameters
transformers
MOSS-TTS-v1.5 is continued from MOSS-TTS 1.0. It preserves the main 1.0 capabilities, including zero-shot voice cloning, long-form speech generation, token-level duration control, Pinyin/IPA pronunciation control, multilingual synthesis, and code-switching. For the full 1.0 feature walkthrough, input schema, decoding hyperparameters, and evaluation tables, please refer to the MOSS-TTS 1.0 README. Compared with MOSS-TTS 1.0, v1.5 focuses on the following improvements: - Stronger multilingual synthesis with language tags: when the language field is omitted, v1.5 may improve some languages and regress slightly on others compared with 1.0. When the language is specified, v1.5 is stronger than…
Open weights
apache-2.0
8.5B parameters
40,960 tokens
Check our open-source repository https://github.com/boson-ai/higgs-audio for more details! We are open-sourcing Higgs TTS 2, a powerful audio foundation model pretrained on over 10 million hours of audio data and a diverse set of text data. Despite having no post-training or fine-tuning, Higgs TTS 2 excels in expressive audio generation, thanks to its deep language and acoustic understanding. On EmergentTTS-Eval, the model achieves win rates of 75.7% and 55.7% over "gpt-4o-mini-tts" on the "Emotions" and "Questions" categories, respectively. It also obtains state-of-the-art performance on traditional TTS benchmarks like Seed-TTS Eval and Emotional Speech Dataset (ESD). Moreover, the model…
Open weights
other
5.8B parameters
2,048 tokens
transformers
Model · Text to speech
Qwen
We release Qwen3-TTS, a series of powerful speech generation models developed by Qwen, offering comprehensive support for voice cloning, voice design, ultra-high-quality human-like speech generation, and natural language-based voice control. Qwen3-TTS covers 10 major languages (Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian) as well as multiple dialectal voice profiles. Key features: Install the qwen-tts Python package from PyPI: Zero-shot speech generation on the Seed-TTS test set (Word Error Rate (WER, ↓)): If you find our paper and code useful in your research, please consider giving a star and citation
Open weights
apache-2.0
1.9B parameters
qwen-tts
03/18/2025 – We are releasing our 3B Orpheus TTS model with additional finetunes. Code is available on GitHub: CanopyAI/Orpheus-TTS Orpheus TTS is a state-of-the-art, Llama-based Speech-LLM designed for high-quality, empathetic text-to-speech generation. This model has been finetuned to deliver human-level speech synthesis, achieving exceptional clarity, expressiveness, and real-time streaming performances. Check out our Colab (link to Colab) or GitHub (link to GitHub) on how to run easy inference on our finetuned models. Do not use our models for impersonation without consent, misinformation or deception (including fake news or fraudulent calls), or any illegal or harmful activity. By…
Access requested at publisher
apache-2.0
3.8B parameters
transformers
mms - vits pipelinetag: text-to-speech This repository contains the English (eng) language text-to-speech (TTS) model checkpoint. This model is part of Facebook's Massively Multilingual Speech project, aiming to provide speech technology across a diverse range of languages. You can find more details about the supported languages and their ISO 639-3 codes in the MMS Language Coverage Overview, and see all MMS-TTS checkpoints on the Hugging Face Hub: facebook/mms-tts. MMS-TTS is available in the Transformers library from version 4.33 onwards. VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech) is an end-to-end speech synthesis model that predicts a speech…
Open weights
cc-by-nc-4.0
36M parameters
transformers
mms - vits pipelinetag: text-to-speech This repository contains the Bamanankan (bam) language text-to-speech (TTS) model checkpoint. This model is part of Facebook's Massively Multilingual Speech project, aiming to provide speech technology across a diverse range of languages. You can find more details about the supported languages and their ISO 639-3 codes in the MMS Language Coverage Overview, and see all MMS-TTS checkpoints on the Hugging Face Hub: facebook/mms-tts. MMS-TTS is available in the Transformers library from version 4.33 onwards. VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech) is an end-to-end speech synthesis model that predicts a speech…
Open weights
cc-by-nc-4.0
36M parameters
transformers
Fish Audio S2 Pro is a leading text-to-speech (TTS) model with fine-grained inline control of prosody and emotion. Trained on over 10M+ hours of audio data across 80+ languages, the system combines reinforcement learning alignment with a dual-autoregressive architecture. The release includes model weights, fine-tuning code, and an SGLang-based streaming inference engine. S2 Pro builds on a decoder-only transformer combined with an RVQ-based audio codec (10 codebooks, ~21 Hz frame rate) using a Dual-Autoregressive (Dual-AR) architecture: - Slow AR (4B parameters): Operates along the time axis and predicts the primary semantic codebook. - Fast AR (400M parameters): Generates the remaining 9…
Open weights
other
4.6B parameters
MOSS-TTS-Local-Transformer-v1.5 is continued from MOSS-TTS-Local-Transformer-v1.0. It preserves the main 1.0 capabilities, including zero-shot voice cloning, long-form speech generation, token-level duration control, Pinyin/IPA pronunciation control, multilingual synthesis, and code-switching. For the full 1.0 feature walkthrough, input schema, and evaluation tables, please refer to the MOSS-TTS-Local-Transformer-v1.0 README. Compared with MOSS-TTS-Local-Transformer-v1.0, v1.5 focuses on the following improvements: - Stronger multilingual synthesis with language tags: when the language field is omitted, v1.5 may improve some languages and regress slightly on others compared with 1.0. When…
Open weights
apache-2.0
4.6B parameters
32,768 tokens
transformers
GEnerative, Prosody-aware, Autoregressive text-to-speech model for Realtime Dialogue Gepard is a text-to-speech model built for real-time conversation. It starts speaking the moment text begins arriving, generating audio piece by piece instead of waiting for a full sentence — so it feels like a live voice, not a recording. It's a single language model that learned text and speech together, so the output carries natural rhythm and timing rather than the flat, stitched tone of older pipelines. The name evokes "Gepard"(/geh-PART/), German for cheetah — a nod to the model's low-latency, high-throughput streaming. - One clean pass per frame — the whole audio frame (32 orthogonal FSQ channels) is…
Open weights
apache-2.0
556M parameters
262,144 tokens
transformers
ⓍTTS is a Voice generation model that lets you clone voices into different languages by using just a quick 6-second audio clip. There is no need for an excessive amount of training data that spans countless hours. This is the same or similar model to what powers Coqui Studio and Coqui API. - Supports 17 languages. - Voice cloning with just a 6-second audio clip. - Emotion and style transfer by cloning. - Cross-language voice cloning. - Multi-lingual speech generation. - 24khz sampling rate. - 2 new languages; Hungarian and Korean - Architectural improvements for speaker conditioning. - Enables the use of multiple speaker references and interpolation between speakers. - Stability…
Open weights
other
coqui
This directory contains audio.cpp-native GGUF conversions of multiple speech models. These files are intended for use with audio.cpp. If you enjoy the project, please star audio.cpp on GitHub and this Hugging Face repository. For conversion details, supported layouts, direct-file loading, sidecar embedding, and the latest compatibility notes, see the audio.cpp GGUF guide: - https://github.com/0xShug0/audio.cpp/blob/main/docs/gguf.md!!! Converted and quantized packages are checked with automated metrics, but perceived quality can still differ for human listeners. Please validate the exact package, backend, and route to confirm the output is acceptable for your use case. The table lists the…
Open weights
other
audio.cpp
Chatterbox Multilingual V3 is the latest general-purpose multilingual TTS model in the Chatterbox family. It keeps the same 0.5B model size while improving speaker similarity, reducing hallucinations, and producing more natural, conversational speech across languages. V3 is designed for broad language coverage like V2, but with stronger stability and more expressive generation. It is the recommended multilingual model for users who want one voice cloning model that works across many languages. Try it in the Chatterbox Multilingual TTS V3 Space. Alongside V3, we are releasing the Single Language Pack: dedicated finetunes for priority languages where tighter quality control, stronger…
Open weights
mit
chatterbox
Download F5-TTS or E2 TTS and place under ckpts/ Paper: F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching
Open weights
cc-by-nc-4.0
f5-tts
Kokoro is a frontier TTS model for its size of 82 million parameters (text in/audio out). First, install the kokoro-js library from NPM using: You can then generate speech as follows: Optionally, save the audio to a file: The model is resilient to quantization, enabling efficient high-quality speech synthesis at a fraction of the original model size.
Open weights
apache-2.0
transformers.js
GGUF weights for qwentts.cpp, a C++17/GGML port of Qwen3-TTS 12 Hz (Qwen team, Alibaba). Multilingual zero shot TTS with named speakers and Mandarin dialects, 24 kHz mono. Runs on CPU, CUDA, Metal, Vulkan. qwen-talker-{size}-{mode}-{variant}.gguf Qwen3 LM + code predictor MTP head + optional speaker encoder, text -> 12 Hz codes qwen-tokenizer-12hz-{variant}.gguf SEANet + ConvNeXt + DAC v2 + RVQ, 12 Hz codes 24 kHz audio Three modes are available across two talker sizes: The tokenizer is shared across every talker. Set GGMLBACKEND to force a device, otherwise the runtime picks the best one available. Tokenizer GGUFs are not uniform quants. Three categories get a Conv kernel rows (K=7,3,1)…
Open weights
apache-2.0
gguf
This repository provides all the necessary tools for using a HiFIGAN vocoder trained with LibriTTS (with multiple speakers). The sample rate used for the vocoder is 22050 Hz. The pre-trained model takes in input a spectrogram and produces a waveform in output. Typically, a vocoder is used after a TTS model that converts an input text into a spectrogram. Alternatives to this models are the following: - tts-hifigan-libritts-16kHz (same model trained on the same dataset, but for a sample rate of 16000 Hz) - tts-hifigan-ljspeech (same model trained on LJSpeech for a sample rate of 22050 Hz). Please notice that we encourage you to read our tutorials and learn more about To perform inference on…
Open weights
apache-2.0
speechbrain
This repository hosts the downloadable runtime files for Higgs Audio v3 Studio, a Windows desktop app for local Higgs Audio v3 TTS, voice cloning, speech continuation, and multi-speaker generation. This repository is not the original upstream model release. It provides GGUF model builds, the Windows CUDA engine DLL package, checksums, and a manifest used by the desktop app downloader. Higgs Audio v3 Studio is a Rust/Tauri desktop application that runs a ported native C++/CUDA implementation of Higgs Audio v3 locally. - Engine dependency diagnostics for missing CUDA/MSVC runtime DLLs Use the desktop app from GitHub releases: https://github.com/Saganaki22/Higgs-Audio-v3-Studio/releases…
Open weights
other
MeloTTS is a high-quality multi-lingual text-to-speech library by MIT and MyShell.ai. Supported languages include: - The Chinese speaker supports mixed Chinese and English. - Fast enough for CPU real-time inference. - Wenliang Zhao at Tsinghua University - Xumin Yu at Tsinghua University - Zengyi Qin (project lead) at MIT and MyShell Citation An unofficial live demo is hosted on Hugging Face Spaces. There are hundreds of TTS models on MyShell, much more than MeloTTS. See examples here. More can be found at the widget center of MyShell.ai. Follow the installation steps here before using the following snippet: Open Source AI Grant We are actively sponsoring open-source AI projects. The…
Open weights
mit
transformers
Zero-shot voice tuner for Kokoro-82M. Outputs base Kokoro compatible voice packs @ [510, 1, 256]. Same passage for every voice, enrolled from the references. LibriTTS-R speakers are dev-clean held out from training. Integrated into Kokoro-FastAPI (v0.9.0+) The pack is a plain tensor; torch.save(pack, "voices/amme.pt") makes it a voice file like any other, prefixed by accent and gender like the stock packs. The pitch-tracking ceiling is set automatically from the reference's harmonic spacing, so band-limited or archival sources land in the right octave without tuning. - enroll(..., fmax=180) overrides it if a voice still reads the wrong register. Enrollment embeds an input audio sample via…
Open weights
apache-2.0
10M parameters
Fun-CosyVoice 3.0 is an advanced text-to-speech (TTS) system based on large language models (LLM), surpassing its predecessor (CosyVoice 2.0) in content consistency, speaker similarity, and prosody naturalness. It is designed for zero-shot multilingual speech synthesis in the wild. - [x] release Fun-CosyVoice3-0.5B-2512 base model, rl model and its training/inference script - [x] release Fun-CosyVoice3-0.5B modelscope gradio space - [x] Thanks to the contribution from NVIDIA Yuekai Zhang, add triton trtllm runtime support and cosyvoice2 grpo training support - [x] release Fun-CosyVoice 3.0 eval set - [x] add CosyVoice2-0.5B vllm support - [x] 25hz CosyVoice2-0.5B released - [x] 25hz…
Open weights
apache-2.0
See also the pre-print research paper, the project page, the Colab example, the GitHub repository, and the repository of voices. This is a model for streaming text-to-speech (TTS). Unlike offline text-to-speech, where the model needs the entire text to produce the audio, our model starts to output audio as soon as the first few words from the text have been given as input. This model is actually 1.8B parameters, not 1.6B as the name might suggest. The model architecture is a hierarchical Transformer that consumes tokenized text and generateds audio tokenized by Mimi, see the Moshi paper. The frame rate is 12.5 Hz and each audio frame is represented by 32 audio tokens, although you can use…
Open weights
cc-by-4.0
moshi
Download F5-TTS or E2 TTS and place under ckpts/ Paper: E2 TTS: Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS
Open weights
cc-by-nc-4.0
f5-tts
sano (सानो) — Nepali for "small." A family of tiny neural text-to-speech voices — 294k to 2.27M parameters — that run with LM386 and a speaker), or live in the browser via WASM. every voice synthesizes your text live in the browser, no server, no upload. Both packages stream their weights from this repo by default. Python needs sanotts >= 0.3.0, the browser sanotts-web >= 0.3.0. Both fall back to the GitHub releases or the Pages host if Hugging Face cannot be reached, so an outage here cannot break an install. Python packages land in ~/.cache/sanotts/; set SANOTTSVOICESOURCE=hf or =github to pin one host. In the browser, passing voiceBase yourself turns the fallback off, so a self-hosted…
Open weights
gpl-3.0
sanotts
alt="Title card" style="width: 500px; Zonos-v0.1 is a leading open-weight text-to-speech model trained on more than 200k hours of varied multilingual speech, delivering expressiveness and quality on par with—or even surpassing—top TTS providers. Our model enables highly natural speech generation from text prompts when given a speaker embedding or audio prefix, and can accurately perform speech cloning when given a reference clip spanning just a few seconds. The conditioning setup also allows for fine control over speaking rate, pitch variation, audio quality, and emotions such as happiness, fear, sadness, and anger. The model outputs speech natively at 44kHz. Zonos follows a straightforward…
Open weights
apache-2.0
1.6B parameters
zonos
VieNeu-TTS-0.3B-Q4-0-GGUF is a Q40 quantized version of VieNeu-TTS-0.3B. This model is specifically optimized to run directly on the CPU, providing extremely fast speech synthesis without the need for a dedicated GPU. Training high-quality TTS models requires significant GPU resources. If you find this model useful, please consider supporting the development: eSpeak NG is mandatory for phonemization. Use the source code from GitHub for the best experience with full text preprocessing support: In the UI, select Backbone: VieNeu-TTS-0.3B-q4-gguf and Device: CPU. Install the SDK to integrate VieNeu-TTS-0.3B into your research or applications: This model is released under the CC BY-NC 4.0…
Open weights
cc-by-nc-4.0
SeamlessM4T is a collection of models designed to provide high quality translation, allowing people from different linguistic communities to communicate effortlessly through speech and text. This repository hosts Hugging Face's implementation of SeamlessM4T. You can find the original weights, as well as a guide on how to run them in the original hub repositories (large and medium checkpoints). SeamlessM4T v2, an improved version of this version with a novel architecture, has been released here. This new model improves over SeamlessM4T v1 in quality as well as inference speed in speech generation tasks. SeamlessM4T v2 is also supported by Transformers, more on it in the model card of this…
Open weights
cc-by-nc-4.0
4,096 tokens
transformers
2025/05/20 - CSM is availabile natively in Hugging Face Transformers as of version 4.52.1 2025/03/13 - We are releasing the 1B CSM variant. The checkpoint is hosted on Hugging Face. CSM (Conversational Speech Model) is a speech generation model from Sesame that generates RVQ audio codes from text and audio inputs. The model architecture employs a Llama backbone and a smaller audio decoder that produces Mimi audio codes. A fine-tuned variant of CSM powers the interactive voice demo shown in our blog post. A hosted HuggingFace space is also available for testing audio generation. CSM supports full-graph compilation with CUDA graphs! CSM can be fine-tuned using Transformers' Trainer. Does this…
Access requested at publisher
apache-2.0
1.6B parameters
transformers
This repository hosts the kokoro models exported for the library as ExecuTorch.pte programs, ready to run on device. These files are published for the ExecuTorch v1.4.1 runtime. ExecuTorch gives no forward compatibility guarantee, so an older runtime may fail to load them. To use them in React Native ExecuTorch, pass the model constant shipped in the library's model registry to the corresponding task pipeline. See the To load these files in your own ExecuTorch runtime, read the first. Kokoro synthesizes speech in 8 languages here, including fine-tuned Polish and German voices. The synthesizer and duration predictor support input shape dynamism over a range of 1 to 128 tokens. Synthesis…
Open weights
apache-2.0
executorch
Higgs TTS 3 is built for voice chat: it speaks, not just reads. It turns model responses into expressive conversational speech across 100+ languages, with zero-shot voice cloning and inline control over emotion, style, prosody, pauses, and sound effects. Higgs autoregressive decoder consumes interleaved text and audio tokens. Audio is encoded by the Higgs Tokenizer into 8 codebooks at 25 fps, staggered via a delay pattern, then mapped to backbone hidden states through a multi-codebook fused embedding. Output codes pass through a multi-codebook fused head, are de-delayed, and decoded back to waveform. The model reaches single-digit WER/CER on 102 languages, which split into · Chichewa/Nyanja…
Open weights
other
4.7B parameters
32,768 tokens
transformers
This model was converted to MLX format from hexagrad/Kokoro-82M using mlx-audio version 0.0.1. Refer to the original model card for more details on the model.
Open weights
apache-2.0
mlx
MOSS-TTS-Nano is an open-source multilingual tiny speech generation model from MOSI.AI and the OpenMOSS team. With only 0.1B parameters, it is designed for realtime speech generation, can run directly on CPU without a GPU, and keeps the deployment stack simple enough for local demos, web serving, and lightweight product integration. 2026.4.10: We release MOSS-TTS-Nano. A demo Space is available at OpenMOSS-Team/MOSS-TTS-Nano. You can also view the demo and more details at openmoss.github.io/MOSS-TTS-Nano-Demo/. MOSS-TTS-Nano focuses on the part of TTS deployment that matters most in practice: small footprint, low latency, good enough quality for realtime products, and simple local setup. It…
Open weights
apache-2.0
32,768 tokens
svara-TTS is a developer-first multilingual TTS model for 19 languages (18 Indic + Indian English). Built on an Orpheus-style discrete audio token approach, it targets clarity, expressiveness, and low-latency on commodity GPUs/CPUs. It supports light-weight emotion/style control (e.g.,,,, ) and simple speaker identities (Language (Gender)), with zero-shot adaptation paths. Try it live on the Demo Space, or on Colab Deployment scripts and inference repo will be available soon. Watch our Github for updates - Place style/emotion tags at the end of the sentence: आज... सच में अच्छी खबर है — शाम को मिलते हैं! - Use punctuation to hint prosody (ellipses, commas, exclamation). - For technical or…
Open weights
apache-2.0
3.3B parameters
131,072 tokens
transformers
VieNeu-TTS-v2 is the next generation of Vietnamese TTS, designed for Natural Communication, Podcasts, and Bilingual (En-Vi) Code-switching. This project features the flagship VieNeu-TTS-v2 architecture: Tác giả: Phạm Nguyễn Ngọc Bảo Training high-quality TTS models requires significant GPU resources. If you find this model useful, please consider supporting the development: Install the SDK to integrate VieNeu-TTS-0.3B into your research or applications: Deploy VieNeu-TTS as a high-performance API Server (powered by LMDeploy) with a single command. Start the Server with a Public Tunnel (No port forwarding needed): Once the server is running, you can connect from anywhere (Colab, Web Apps…
Open weights
apache-2.0
294M parameters
4,096 tokens
VibeVoice is a novel framework designed for generating expressive, long-form, multi-speaker conversational audio, such as podcasts, from text. It addresses significant challenges in traditional Text-to-Speech (TTS) systems, particularly in scalability, speaker consistency, and natural turn-taking. A core innovation of VibeVoice is its use of continuous speech tokenizers (Acoustic and Semantic) operating at an ultra-low frame rate of 7.5 Hz. These tokenizers efficiently preserve audio fidelity while significantly boosting computational efficiency for processing long sequences. VibeVoice employs a next-token diffusion framework, leveraging a Large Language Model (LLM) to understand textual…
Open weights
mit
2.7B parameters
65,536 tokens
transformers
03/18/2025 – We are releasing our 3B Orpheus TTS model with additional finetunes. Code is available on GitHub: CanopyAI/Orpheus-TTS Orpheus TTS is a state-of-the-art, Llama-based Speech-LLM designed for high-quality, empathetic text-to-speech generation. This model has been finetuned to deliver human-level speech synthesis, achieving exceptional clarity, expressiveness, and real-time streaming performances. Check out our Colab (link to Colab) or GitHub (link to GitHub) on how to run easy inference on our finetuned models. Do not use our models for impersonation without consent, misinformation or deception (including fake news or fraudulent calls), or any illegal or harmful activity. By…
Open weights
apache-2.0
3.3B parameters
131,072 tokens
transformers
MeloTTS is a high-quality multi-lingual text-to-speech library by MyShell.ai. Supported languages include: - The Chinese speaker supports mixed Chinese and English. - Fast enough for CPU real-time inference. An unofficial live demo is hosted on Hugging Face Spaces. There are hundreds of TTS models on MyShell, much more than MeloTTS. See examples here. More can be found at the widget center of MyShell.ai. Follow the installation steps here before using the following snippet: Open Source AI Grant We are actively sponsoring open-source AI projects. The sponsorship includes GPU resources, fundings and intellectual support (collaboration with top research labs). We welcome both reseach and…
Open weights
mit
transformers
MOSS‑TTS Family is an open‑source speech and sound generation model family from MOSI.AI and the OpenMOSS team. It is designed for high‑fidelity, high‑expressiveness, and complex real‑world scenarios, covering stable long‑form speech, multi‑speaker dialogue, voice/character design, environmental sound effects, and real‑time streaming TTS. When a single piece of audio needs to sound like a real person, pronounce every word accurately, switch speaking styles across content, remain stable over tens of minutes, and support dialogue, role‑play, and real‑time interaction, a single TTS model is often not enough. The MOSS‑TTS Family breaks the workflow into five production‑ready models that can be…
Open weights
apache-2.0
2.1B parameters
40,960 tokens
A trustremotecode packaging of bosonai/higgs-audio-v3-tts-4b that loads with plain transformers (no SGLang). The weights are the original checkpoint, copied unchanged; only a small modeling.py / configuration.py pair and an automap were added. The model is a standard Qwen3-4B backbone plus a fused multi-codebook audio embedding/head. Reference-audio encoding and waveform decoding use the transformers-native bosonai/higgs-audio-v2-tokenizer (higgsaudiov2tokenizer), loaded automatically on first use. Requires transformers >= 5.5. generatespeech returns a mono 24 kHz waveform as a CPU float32 tensor [L]. - Generation uses Higgs' delay pattern across 8 codebooks (vocab 1026, incl. BOC/EOC…
Open weights
other
4.7B parameters
32,768 tokens
transformers
F5-TTSRUSSIAN - дообученная версия оригинальной модели F5-TTS, адаптированная для синтеза русской речи. Демо: демо работы модели (F5-TTSRUSSIAN/F5TTSv1Base) и сравнение с XTTS и FishSpeech — F5-TTSRUSSIAN/F5TTSv1Base — первая версия модели, использованная для генерации демо-записей. F5-TTSRUSSIAN/F5TTSv1Baseaccenttune — дообученная версия с полной разметкой ударений (100% предложений в обучающем датасете). Рекомендуется использовать символы ударения для лучшего качества синтеза. F5-TTSRUSSIAN/F5TTSv1Basev2 — дообученная версия (+16 эпох). Добавлена фильтрация данных (удалено ~5% записей с артефактами, soft-clean), с полной разметка ударений в тексте.
Open weights
cc-by-nc-4.0
f5-tts
https://github.com/vibevoice-community/VibeVoice VibeVoice is a novel framework designed for generating expressive, long-form, multi-speaker conversational audio, such as podcasts, from text. It addresses significant challenges in traditional Text-to-Speech (TTS) systems, particularly in scalability, speaker consistency, and natural turn-taking. A core innovation of VibeVoice is its use of continuous speech tokenizers (Acoustic and Semantic) operating at an ultra-low frame rate of 7.5 Hz. These tokenizers efficiently preserve audio fidelity while significantly boosting computational efficiency for processing long sequences. VibeVoice employs a next-token diffusion framework, leveraging a…
Open weights
mit
2.7B parameters
transformers
Kokoro is a frontier TTS model for its size of 82 million parameters (text in/audio out). First, install the kokoro-js library from NPM using: You can then generate speech as follows: Optionally, save the audio to a file: The model is resilient to quantization, enabling efficient high-quality speech synthesis at a fraction of the original model size.
Open weights
apache-2.0
transformers.js
SpeechT5 model fine-tuned for speech synthesis (text-to-speech) on LibriTTS. This model was introduced in SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei. SpeechT5 was first released in this repository, original weights. The license used is MIT. Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech/text representation learning. The…
Open weights
mit
transformers
2026.01.14 - Soprano-1.1-80M released! 95% fewer hallucinations and a 63% preference rate over Soprano-80M. 2026.01.13 - Soprano-Factory released! You can now train/fine-tune your own Soprano models. 2025.12.22 - Soprano-80M released! Code | Demo Soprano is an ultra‑lightweight, on-device text‑to‑speech (TTS) model designed for expressive, high‑fidelity speech synthesis at unprecedented speed. Soprano was designed with the following features: - Up to 2000x real-time generation on GPU and 20x real-time on CPU - Lossless streaming with one plus one, etc) If Soprano produces unsatisfactory results, you can easily regenerate it for a new, potentially better generation. You may also change the…
Open weights
apache-2.0
80M parameters
1,024 tokens
transformers
MeloTTS is a high-quality multi-lingual text-to-speech library by MyShell.ai. Supported languages include: - The Chinese speaker supports mixed Chinese and English. - Fast enough for CPU real-time inference. An unofficial live demo is hosted on Hugging Face Spaces. There are hundreds of TTS models on MyShell, much more than MeloTTS. See examples here. More can be found at the widget center of MyShell.ai. Follow the installation steps here before using the following snippet: Open Source AI Grant We are actively sponsoring open-source AI projects. The sponsorship includes GPU resources, fundings and intellectual support (collaboration with top research labs). We welcome both reseach and…
Open weights
mit
transformers
Fine-tuned Vietnamese Kokoro TTS artifacts. - kokorovi.pth: PyTorch Kokoro KModel checkpoint for inference. - kokorovi.onnx: ONNX Runtime export of the acoustic model. - kokorovivoicepack.pt: default Vietnamese voicepack. - config.json: Kokoro config/vocab used by both PyTorch and ONNX inference. - voicepacks/.pt: additional Vietnamese voicepacks. The ONNX CLI downloads kokorovi.onnx, kokorovivoicepack.pt, and config.json from this repository when local paths are not provided. Install onnxruntime-gpu and pass --device cuda to use CUDAExecutionProvider when available. Vietnamese G2P is handled by vig2p, matching the GitHub inference and training code.
Open weights
apache-2.0
GGUF weights for omnivoice.cpp, a C++17/GGML port of OmniVoice (k2-fsa/OmniVoice). Multilingual zero shot TTS, 646 languages, 24 kHz mono. Runs on CPU, CUDA, ROCm, Metal, Vulkan. omnivoice-base-{variant}.gguf Qwen3 0.6B backbone, text -> tokens omnivoice-tokenizer-{variant}.gguf HuBERT + DAC + RVQ, tokens 24 kHz audio Set GGMLBACKEND to force a device, otherwise the runtime picks the best one available. Tokenizer GGUFs are not uniform quants. Three categories get a Same fallback as llama.cpp tensortypefallback: F16 has no block size and matches the runtime target dtype on every backend. The base LM (Qwen3 0.6B, hidden = 1024) has all dimensions divisible by 256 so the fallback never…
Open weights
cc-by-nc-4.0
gguf
mms - vits pipelinetag: text-to-speech This repository contains the Hindi (hin) language text-to-speech (TTS) model checkpoint. This model is part of Facebook's Massively Multilingual Speech project, aiming to provide speech technology across a diverse range of languages. You can find more details about the supported languages and their ISO 639-3 codes in the MMS Language Coverage Overview, and see all MMS-TTS checkpoints on the Hugging Face Hub: facebook/mms-tts. MMS-TTS is available in the Transformers library from version 4.33 onwards. VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech) is an end-to-end speech synthesis model that predicts a speech…
Open weights
cc-by-nc-4.0
36M parameters
transformers
L
Model · Text to speech
LM
GGUF files released for the audio.cpp echotts model. Includes the s1 codec weights. f16 and q80 This work is not affiliated or endorsed by the original author, Jordan Darefsky (https://huggingface.co/jordand/echo-tts-base) Echo-TTS is a diffusion-based text-to-speech model. The weights in this repository are intended for research and non-commercial use only. By using this model, you agree not to use it for: - Deception, fraud, or impersonation, including: - Generating audio that mimics a real person’s voice without their explicit consent. - Creating deepfakes meant to mislead others about who is speaking. - Harassment, abuse, or discrimination, including targeted abuse or hate content…
Open weights
GGUF packages of Soprano-1.1-80M for audio.cpp. Original model: ekwek1/soprano — an ultra-lightweight (~80M) English-only TTS model by @ekwek1. ` ash python3 tools/modelmanagerv2.py install soprano1180mq80 This conversion is provided as a community package for use with audio.cpp. All rights to the model and its weights belong to the original authors.
Open weights
apache-2.0
audiocpp
This model was trained by ftshijt using vctk recipe in espnet. Follow the ESPnet installation instructions if you haven't done that already.
Open weights
cc-by-4.0
espnet
