4 Commits

Author SHA1 Message Date
zengguoyang
38a76704ee update requirements for zipenhancer 2025-09-16 16:06:03 +08:00
zengguoyang
dfd487f5af update requirements 2025-09-16 13:49:44 +08:00
周逸轩
081845b35b FX: readme description 2025-09-16 13:26:14 +08:00
zengguoyang
6684b547cc FX: fixed find_package error 2025-09-16 13:22:20 +08:00
4 changed files with 48 additions and 60 deletions

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@@ -1,7 +1,7 @@
## 🎙️ VoxCPM: Tokenizer-Free TTS for Context-Aware Speech Generation and True-to-Life Voice Cloning
[![Project Page](https://img.shields.io/badge/Project%20Page-GitHub-blue)](https://github.com/OpenBMB/VoxCPM/) [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-OpenBMB-yellow)](hhttps://huggingface.co/openbmb/VoxCPM-0.5B) [![Live Playground](https://img.shields.io/badge/Live%20PlayGround-Demo-orange)](https://huggingface.co/spaces/OpenBMB/VoxCPM-Demo) [![Samples](https://img.shields.io/badge/Page-Samples-red)](https://thuhcsi.github.io/VoxCPM/)
[![Project Page](https://img.shields.io/badge/Project%20Page-GitHub-blue)](https://github.com/OpenBMB/VoxCPM/) [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-OpenBMB-yellow)](https://huggingface.co/openbmb/VoxCPM-0.5B) [![Live Playground](https://img.shields.io/badge/Live%20PlayGround-Demo-orange)](https://huggingface.co/spaces/OpenBMB/VoxCPM-Demo) [![Samples](https://img.shields.io/badge/Page-Samples-red)](https://thuhcsi.github.io/VoxCPM/)
<div align="center">
@@ -9,17 +9,17 @@
</div>
## News
* [2025.09.16] 🔥 🔥 🔥 We Open Source the VoxCPM-0.5B weights!
* [2025.09.16] 🔥 🔥 🔥 We Open Source the VoxCPM-0.5B [weights](https://huggingface.co/openbmb/VoxCPM-0.5B)!
* [2025.09.16] 🎉 🎉 🎉 We Provide the [Gradio PlayGround](https://huggingface.co/spaces/OpenBMB/VoxCPM-Demo) for VoxCPM-0.5B, try it now!
## Overview
VoxCPM is a novel tokenizer-free Text-to-Speech (TTS) system that redefines realism in speech synthesis. By modeling speech in a continuous space, it overcomes the limitations of discrete tokenization and enables two flagship capabilities: context-aware speech generation and true-to-life zero-shot voice cloning.
Unlike mainstream approaches that convert speech to discrete tokens, VoxCPM uses an end-to-end diffusion autoregressive architecture that directly generates continuous speech representations from text. Built on [MiniCPM-4](https://huggingface.co/openbmb/MiniCPM4-0.5B), it achieves implicit semantic-acoustic decoupling through hierachical language modeling and FSQ constraints, greatly enhancing both expressiveness and generation stability.
Unlike mainstream approaches that convert speech to discrete tokens, VoxCPM uses an end-to-end diffusion autoregressive architecture that directly generates continuous speech representations from text. Built on [MiniCPM-4](https://huggingface.co/openbmb/MiniCPM4-0.5B) backbone, it achieves implicit semantic-acoustic decoupling through hierachical language modeling and FSQ constraints, greatly enhancing both expressiveness and generation stability.
<div align="center">
<img src="assets/voxcpm_model.png" alt="VoxCPM Model Architecture" width="500">
<img src="assets/voxcpm_model.png" alt="VoxCPM Model Architecture" width="90%">
</div>
@@ -30,6 +30,13 @@ Unlike mainstream approaches that convert speech to discrete tokens, VoxCPM uses
## Quick Start
### 🔧 Install from PyPI
@@ -61,13 +68,13 @@ wav = model.generate(
text="VoxCPM is an innovative end-to-end TTS model from ModelBest, designed to generate highly expressive speech.",
prompt_wav_path=None, # optional: path to a prompt speech for voice cloning
prompt_text=None, # optional: reference text
cfg_value=2.0,
inference_timesteps=10,
normalize=True,
denoise=True,
retry_badcase=True, # optional: enable retrying mode
retry_badcase_max_times=3,
retry_badcase_ratio_threshold=6.0,
cfg_value=2.0, # LM guidance on LocDiT, higher for better adherence to the prompt, but maybe worse
inference_timesteps=10, # LocDiT inference timesteps, higher for better result, lower for fast speed
normalize=True, # enable external TN tool
denoise=True, # enable external Denoise tool
retry_badcase=True, # enable retrying mode for some bad cases (unstoppable)
retry_badcase_max_times=3, # maximum retrying times
retry_badcase_ratio_threshold=6.0, # maximum length restriction for bad case detection (simple but effective), it could be adjusted for slow pace speech
)
sf.write("output.wav", wav, 16000)
@@ -175,41 +182,41 @@ VoxCPM achieves competitive results on public zero-shot TTS benchmarks:
| Model | Parameters | Open-Source | test-EN | | test-ZH | | test-Hard | |
|------|------|------|:------------:|:--:|:------------:|:--:|:-------------:|:--:|
| | | | WER/%⬇ | SIM/%⬆| CER/%⬇| SIM/%⬆ | CER/%⬇ | SIM/%⬆ |
| MegaTTS3 | 0.5B | ❌ | 2.79 | 77.1 | 1.52 | 79.0 | - | - |
| DiTAR | 0.6B | ❌ | 1.69 | 73.5 | 1.02 | 75.3 | - | - |
| CosyVoice3 | 0.5B | ❌ | 2.02 | 71.8 | 1.16 | 78.0 | 6.08 | 75.8 |
| CosyVoice3 | 1.5B | ❌ | 2.22 | 72.0 | 1.12 | 78.1 | 5.83 | 75.8 |
| Seed-TTS | - | ❌ | 2.25 | 76.2 | 1.12 | 79.6 | 7.59 | 77.6 |
| MiniMax-Speech | - | ❌ | 1.65 | 69.2 | 0.83 | 78.3 | - | - |
| CosyVoice | 0.3B | ✅ | 4.29 | 60.9 | 3.63 | 72.3 | 11.75 | 70.9 |
| CosyVoice2 | 0.5B | ✅ | 3.09 | 65.9 | 1.38 | 75.7 | 6.83 | 72.4 |
| CosyVoice2 | 0.5B | ✅ | 3.09 | 65.9 | 1.38 | 75.7 | **6.83** | 72.4 |
| F5-TTS | 0.3B | ✅ | 2.00 | 67.0 | 1.53 | 76.0 | 8.67 | 71.3 |
| SparkTTS | 0.5B | ✅ | 3.14 | 57.3 | 1.54 | 66.0 | - | - |
| FireRedTTS | 0.5B | ✅ | 3.82 | 46.0 | 1.51 | 63.5 | 17.45 | 62.1 |
| FireRedTTS-2 | 1.5B | ✅ | 1.95 | 66.5 | 1.14 | 73.6 | - | - |
| Qwen2.5-Omni | 7B | ✅ | 2.72 | 63.2 | 1.70 | 75.2 | 7.97 | 74.7 |
| Qwen2.5-Omni | 7B | ✅ | 2.72 | 63.2 | 1.70 | 75.2 | 7.97 | **74.7** |
| OpenAudio-s1-mini | 0.5B | ✅ | 1.94 | 55.0 | 1.18 | 68.5 | - | - |
| IndexTTS2 | 1.5B | ✅ | 2.23 | 70.6 | 1.03 | 76.5 | - | - |
| VibeVoice | 1.5B | ✅ | 3.04 | 68.9 | 1.16 | 74.4 | - | - |
| HiggsAudio-v2 | 3B | ✅ | 2.44 | 67.7 | 1.50 | 74.0 | - | - |
| CosyVoice3 | 0.5B | | 2.02 | 71.8 | 1.16 | 78.0 | 6.08 | 75.8 |
| CosyVoice3 | 1.5B | ❌ | 2.22 | 72.0 | 1.12 | 78.1 | 5.83 | 75.8 |
| MegaTTS3 | 0.5B | ❌ | 2.79 | 77.1 | 1.52 | 79.0 | - | - |
| DiTAR | 0.6B | ❌ | 1.69 | 73.5 | 1.02 | 75.3 | - | - |
| Seed-TTS | - | ❌ | 2.25 | 76.2 | 1.12 | 79.6 | 7.59 | 77.6 |
| MiniMax-Speech | - | ❌ | 1.65 | 69.2 | 0.83 | 78.3 | - | - |
| **VoxCPM** | **0.5B** | **✅** | **1.85** | **72.9** | **0.93** | **77.2** | 8.87 | 73.0 |
| **VoxCPM** | 0.5B | | **1.85** | **72.9** | **0.93** | **77.2** | 8.87 | 73.0 |
### CV3-eval Benchmark
| Model | zh | en | hard-zh | | | hard-en | | | |
|-------|:--:|:--:|:-------:|:--:|:--:|:-------:|:--:|:--:|:--:|
| | CER/%⬇ | WER/%⬇ | CER/%⬇ | SIM/%⬆ | DNSMOS⬆ | WER/%⬇ | SIM/%⬆ | DNSMOS⬆ | |
| F5-TTS | 5.47 | 8.90 | - | - | - | - | - | - | |
| SparkTTS | 5.15 | 11.0 | - | - | - | - | - | - | |
| GPT-SoVits | 7.34 | 12.5 | - | - | - | - | - | - | |
| CosyVoice2 | 4.08 | 6.32 | 12.58 | 72.6 | 3.81 | 11.96 | 66.7 | 3.95 | |
| OpenAudio-s1-mini | 4.00 | 5.54 | 18.1 | 58.2 | 3.77 | 12.4 | 55.7 | 3.89 | |
| IndexTTS2 | 3.58 | 4.45 | 12.8 | 74.6 | 3.65 | fail | fail | fail | |
| HiggsAudio-v2 | 9.54 | 7.89 | 41.0 | 60.2 | 3.39 | 10.3 | 61.8 | 3.68 | |
| CosyVoice3-0.5B | 3.89 | 5.24 | 14.15 | 78.6 | 3.75 | 9.04 | 75.9 | 3.92 | |
| CosyVoice3-1.5B | 3.91 | 4.99 | 9.77 | 78.5 | 3.79 | 10.55 | 76.1 | 3.95 | |
| **VoxCPM** | **3.40** | **4.04** | 12.9 | 66.1 | 3.59 | **7.89** | 64.3 | 3.74 | |
| Model | zh | en | hard-zh | | | hard-en | | |
|-------|:--:|:--:|:-------:|:--:|:--:|:-------:|:--:|:--:|
| | CER/%⬇ | WER/%⬇ | CER/%⬇ | SIM/%⬆ | DNSMOS⬆ | WER/%⬇ | SIM/%⬆ | DNSMOS⬆ |
| F5-TTS | 5.47 | 8.90 | - | - | - | - | - | - |
| SparkTTS | 5.15 | 11.0 | - | - | - | - | - | - |
| GPT-SoVits | 7.34 | 12.5 | - | - | - | - | - | - |
| CosyVoice2 | 4.08 | 6.32 | 12.58 | 72.6 | 3.81 | 11.96 | 66.7 | 3.95 |
| OpenAudio-s1-mini | 4.00 | 5.54 | 18.1 | 58.2 | 3.77 | 12.4 | 55.7 | 3.89 |
| IndexTTS2 | 3.58 | 4.45 | 12.8 | 74.6 | 3.65 | - | - | - |
| HiggsAudio-v2 | 9.54 | 7.89 | 41.0 | 60.2 | 3.39 | 10.3 | 61.8 | 3.68 |
| CosyVoice3-0.5B | 3.89 | 5.24 | 14.15 | 78.6 | 3.75 | 9.04 | 75.9 | 3.92 |
| CosyVoice3-1.5B | 3.91 | 4.99 | 9.77 | 78.5 | 3.79 | 10.55 | 76.1 | 3.95 |
| **VoxCPM** | **3.40** | **4.04** | 12.9 | 66.1 | 3.59 | **7.89** | 64.3 | 3.74 |

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@@ -27,19 +27,16 @@ classifiers = [
]
requires-python = ">=3.8"
dependencies = [
"torch==2.5.1",
"torchaudio==2.5.1",
"transformers==4.50.1",
"torch>=2.5.0",
"torchaudio>=2.5.0",
"transformers>=4.36.2",
"einops",
"gradio",
"inflect",
"WeTextProcessing",
"addict",
"modelscope==1.22.0",
"simplejson",
"datasets==2.18.0",
"sortedcontainers",
"librosa",
"WeTextProcessing",
"modelscope>=1.22.0",
"datasets>=2,<4",
"huggingface-hub",
"pydantic",
"tqdm",
@@ -69,7 +66,7 @@ Documentation = "https://github.com/OpenBMB/VoxCPM#readme"
[tool.setuptools.packages.find]
where = ["src"]
include = ["voxcpm"]
include = ["voxcpm*"]
[tool.setuptools.package-dir]
"" = "src"

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@@ -1,16 +0,0 @@
torch==2.5.1
torchaudio==2.5.1
transformers==4.50.1
einops
gradio
inflect
WeTextProcessing
addicts
modelscope==1.22.0
simplejson
datasets==2.18.0
addicts
sortedcontainers
librosa
huggingface-hub
spaces

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@@ -88,7 +88,7 @@ class UnifiedCFM(torch.nn.Module):
shape: (n_timesteps + 1,)
mu (torch.Tensor): output of encoder
shape: (batch_size, n_feats)
cond: Not used but kept for future purposes
cond: condition -- prefix prompt
cfg_value (float, optional): cfg value for guidance. Defaults to 1.0.
"""
t, _, dt = t_span[0], t_span[-1], t_span[0] - t_span[1]