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# The Free, Open-Source Alternative to ElevenLabs Is Finally Here

**[Dev Squad](https://daily.dev/sources/devsquad)** · [@bytefer](https://daily.dev/bytefer) · 4 min read · 0 upvotes · 0 comments

## Summary

VoxCPM2 is a free, open-source TTS model from OpenBMB with 2B parameters supporting 30 languages, voice cloning, voice design, and 48kHz audio output. The post walks through local deployment on macOS using mlx-audio and uv, covering zero-shot generation, voice design with natural language prompts, standard voice cloning, and long-form 'ultimate cloning' with reference audio and transcript. Code examples are provided for each use case, making it a practical guide for self-hosting a capable alternative to ElevenLabs.

## Content

Recently, the OpenBMB team released the latest VoxCPM2 model, which has 2B parameters, supports voice cloning, voice design, and high-quality speech synthesis, and supports **30** mainstream languages, including English, Chinese, Japanese, Korean, German, and French.

## VoxCPM2 Features

- Supports 30 languages, no language tags required, direct input of text in any supported language.
- Supports voice design, input natural language descriptions to generate new voices without reference audio.
- Supports voice cloning, with style guidance to control emotion, pace, and expressiveness.
- Supports generating high-quality **48kHz** audio.

If your work requires the use of ElevenLabs, you can continue reading the content below. By following these steps, you will be able to deploy a completely free TTS service locally. However, if you are looking for open-source alternatives to other popular paid software, you can visit the [BestAlternative](https://www.bestalternative.dev/en) website, which features open-source substitutes for hundreds of popular software.

## VoxCPM Architecture

VoxCPM adopts a tokenizer-free, diffusion autoregressive architecture that models speech in continuous latent space rather than discrete tokens.

![voxcpm2-arch.webp](https://media.daily.dev/image/upload/s--3chGCNxV--/f_auto/v1779676565/ugc/content_b48a4fbc-1ca6-47d5-a7cc-3e6b65283ecd?_a=BAMAMiWQ0)

## Local Deployment

The official VoxCPM2 documentation has detailed how to run VoxCPM2 models using PyTorch and CUDA. Next, I will introduce how to deploy VoxCPM2 models locally on macOS using mlx-audio.

**1.Configure the virtual environment**

```
uv venv .venv
source .venv/bin/activate
```

**2.Install mlx-audio and soundfile**

```
uv pip install "git+https://github.com/Blaizzy/mlx-audio" --prerelease=allow
uv pip install soundfile
```

**3.Download the model.**

You can download the corresponding quantization model based on your computer configuration and actual needs.

![mlx-voxcpm2.png](https://media.daily.dev/image/upload/s--AJcSsfU5--/f_auto/v1779676623/ugc/content_1de91df9-37e3-475a-8184-865feef00b83?_a=BAMAMiWQ0)

```
hf download mlx-community/VoxCPM2-4bit --local-dir ./models/VoxCPM2-4bit
# or
hf download mlx-community/VoxCPM2-8bit --local-dir ./models/VoxCPM2-8bit
# or
hf download mlx-community/VoxCPM2-bf16 --local-dir ./models/VoxCPM2-bf16
```

![voxcpm2-models.webp](https://media.daily.dev/image/upload/s--h0x_dEDw--/f_auto/v1779676717/ugc/content_87eb941f-b736-4762-9b26-92aefb0b804d?_a=BAMAMiWQ0)

**4.Zero-shot Generation**

```
import numpy as np
from mlx_audio.tts.utils import load
from mlx_audio.audio_io import write as audio_write

MODEL_DIR = "models/VoxCPM2-8bit"
OUTPUT_PATH = "zero_shot.wav"
model = load(MODEL_DIR)

result = next(model.generate("Hello, this is VoxCPM2 on Apple Silicon."))
audio_mx = result.audio

audio_write(
    str(OUTPUT_PATH),
    np.array(audio_mx),
    model.sample_rate,
    format="wav",
)
```

It should be noted that if the text to be synthesized contains parentheses, it needs to be escaped; otherwise, the speech cannot be synthesized correctly.

**5.Voice Design**

```
import numpy as np
from mlx_audio.tts.utils import load
from mlx_audio.audio_io import write as audio_write

MODEL_DIR = "models/VoxCPM2-8bit"
OUTPUT_PATH = "voice_design.wav"
model = load(MODEL_DIR)

result = next(model.generate(
    text="Hello, welcome to VoxCPM2.",
    instruct="A young woman, warm and gentle voice",
))
audio_mx = result.audio

audio_write(
    str(OUTPUT_PATH),
    np.array(audio_mx),
    model.sample_rate,
    format="wav",
)
```

**6.Voice Cloning**

```
import numpy as np
from mlx_audio.tts.utils import load
from mlx_audio.audio_io import write as audio_write

MODEL_DIR = "models/VoxCPM2-8bit"
OUTPUT_PATH = "voice_cloning.wav"
model = load(MODEL_DIR)

result = next(model.generate(
    text="Hello, this is VoxCPM2 on Apple Silicon.",
    ref_audio="lisa.wav",
))
audio_mx = result.audio

audio_write(
    str(OUTPUT_PATH),
    np.array(audio_mx),
    model.sample_rate,
    format="wav",
)
```

**7.Ultimate Cloning**

For scenarios involving the production of long-form content such as audiobooks, you need to provide both reference audio and the corresponding transcribed text to ensure consistency.

```
import numpy as np
from mlx_audio.tts.utils import load
from mlx_audio.audio_io import write as audio_write

MODEL_DIR = "models/VoxCPM2-8bit"
OUTPUT_PATH = "ultimate_cloning.wav"
model = load(MODEL_DIR)

result = next(model.generate(
    text="2B-parameter multilingual tokenizer-free TTS model with 48kHz studio-quality output. Supports zero-shot generation, voice design, voice cloning, and continuation for long-form speech. 30 languages including English, Chinese, Indonesian, Japanese, Korean, and more.",
    prompt_text="VoxCPM2 is a tokenizer-free, diffusion autoregressive Text-to-Speech model",
    prompt_audio="lisa.wav",
))

audio_mx = result.audio

audio_write(
    str(OUTPUT_PATH),
    np.array(audio_mx),
    model.sample_rate,
    format="wav",
)
```

## Summary

[VoxCPM2](https://github.com/OpenBMB/VoxCPM) is a powerful TTS model. If you have speech synthesis needs, you can evaluate its capabilities. If it doesn't meet your needs, you can test Qwen3-TTS, which offers 0.6B and 1.7B sizes and supports 10 mainstream languages ​​including English, Chinese, Japanese, Korean, German, and French. It also supports speech synthesis, voice design, and voice cloning, but you'll need to switch between different models.

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#python](https://daily.dev/tags/python), [#open-source](https://daily.dev/tags/open-source), [#text-to-speech](https://daily.dev/tags/text-to-speech), [#voice-cloning](https://daily.dev/tags/voice-cloning)

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