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…
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.
Runs On
What it takes to serve Zonos-v0.1-transformer (1.6B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 3.2 GB | 3.9 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 1.6 GB | 1.9 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.8 GB | 1.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.
Model Card
By Zyphra, published under apache-2.0, revision 9d8331fc49cb.
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…
Read Zyphra's full model card
Zonos-v0.1
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.
For more details and speech samples, check out our blog here
We also have a hosted version available at playground.zyphra.com/audio
Zonos follows a straightforward architecture: text normalization and phonemization via eSpeak, followed by DAC token prediction through a transformer or hybrid backbone. An overview of the architecture can be seen below.
Usage
Python
import torch
import torchaudio
from zonos.model import Zonos
from zonos.conditioning import make_cond_dict
# model = Zonos.from_pretrained("Zyphra/Zonos-v0.1-hybrid", device="cuda")
model = Zonos.from_pretrained("Zyphra/Zonos-v0.1-transformer", device="cuda")
wav, sampling_rate = torchaudio.load("assets/exampleaudio.mp3")
speaker = model.make_speaker_embedding(wav, sampling_rate)
cond_dict = make_cond_dict(text="Hello, world!", speaker=speaker, language="en-us")
conditioning = model.prepare_conditioning(cond_dict)
codes = model.generate(conditioning)
wavs = model.autoencoder.decode(codes).cpu()
torchaudio.save("sample.wav", wavs[0], model.autoencoder.sampling_rate)
Gradio interface (recommended)
uv run gradio_interface.py
# python gradio_interface.py
This should produce a sample.wav file in your project root directory.
For repeated sampling we highly recommend using the gradio interface instead, as the minimal example needs to load the model every time it is run.
Features
- Zero-shot TTS with voice cloning: Input desired text and a 10-30s speaker sample to generate high quality TTS output
- Audio prefix inputs: Add text plus an audio prefix for even richer speaker matching. Audio prefixes can be used to elicit behaviours such as whispering which can otherwise be challenging to replicate when cloning from speaker embeddings
- Multilingual support: Zonos-v0.1 supports English, Japanese, Chinese, French, and German
- Audio quality and emotion control: Zonos offers fine-grained control of many aspects of the generated audio. These include speaking rate, pitch, maximum frequency, audio quality, and various emotions such as happiness, anger, sadness, and fear.
- Fast: our model runs with a real-time factor of ~2x on an RTX 4090
- Gradio WebUI: Zonos comes packaged with an easy to use gradio interface to generate speech
- Simple installation and deployment: Zonos can be installed and deployed simply using the docker file packaged with our repository.
Installation
At the moment this repository only supports Linux systems (preferably Ubuntu 22.04/24.04) with recent NVIDIA GPUs (3000-series or newer, 6GB+ VRAM).
See also Docker Installation
System dependencies
Zonos depends on the eSpeak library phonemization. You can install it on Ubuntu with the following command:
apt install -y espeak-ng
Python dependencies
We highly recommend using a recent version of uv for installation. If you don't have uv installed, you can install it via pip: pip install -U uv.
Installing into a new uv virtual environment (recommended)
uv sync
uv sync --extra compile
Installing into the system/actived environment using uv
uv pip install -e .
uv pip install -e .[compile]
Installing into the system/actived environment using pip
pip install -e .
pip install --no-build-isolation -e .[compile]
Confirm that it's working
For convenience we provide a minimal example to check that the installation works:
uv run sample.py
# python sample.py
Docker installation
git clone https://github.com/Zyphra/Zonos.git
cd Zonos
# For gradio
docker compose up
# Or for development you can do
docker build -t Zonos .
docker run -it --gpus=all --net=host -v /path/to/Zonos:/Zonos -t Zonos
cd /Zonos
python sample.py # this will generate a sample.wav in /Zonos
Citation
If you find this model useful in an academic context please cite as: ```bash @misc{zyphra2025zonos, title = {Zonos-v0.1: An Expressive, Open-Source TTS Model}, author = {Dario Sucic, Mohamed Osman, Gabriel Clark, Chris Warner, Beren Millidge}, year = {2025}, }
Identity and Version
- Repository
- Zyphra/Zonos-v0.1-transformer
- Publisher
- Zyphra
- Task
- Text to speech
- Modality
- Audio
- Library
- zonos
- Parameters
- 1.6B parameters
- Languages
- Not stated by the source
- Revision
- 9d8331fc49cb5ba8aad2bb56cafd809c66598f4e
- First published
- 2025-02-06
- Last updated
- 2025-06-03
Files and Weights
4 files, 3.2 GB in total. The weights are 1 file totalling 3.2 GB in safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 3.2 GB | 4ac68319d6b8 |
| config.json | Configuration | 2.5 KB | — |
| README.md | Documentation | 5.5 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 3.2 GB
Released by Zyphra through its official repository on Hugging Face. Read the license.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 3.2 GB |
| 16-bit | 3.2 GB |
| 8-bit | 1.6 GB |
| 4-bit | 0.8 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About Zonos-v0.1-transformer
How much GPU memory does Zonos-v0.1-transformer need?
About 3.9 GB at 16-bit and 1 GB at 4-bit: the weights (1.6B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run Zonos-v0.1-transformer on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use Zonos-v0.1-transformer commercially?
Yes. Zonos-v0.1-transformer is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
Similar Models
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.…
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
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…
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…
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…

