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title: Voice Clone Bench (Chatterbox)
emoji: 🎙️
colorFrom: indigo
colorTo: purple
sdk: gradio
sdk_version: 5.29.0
app_file: app.py
pinned: false
short_description: Zero-shot voice cloning + TTS to A/B against ElevenLabs
---
# Voice Clone Bench — Chatterbox Multilingual (zero-shot voice cloning)
A standalone prototype for A/B testing open-weight **voice cloning + TTS** against ElevenLabs.
Powered by **[Chatterbox Multilingual](https://huggingface.co/ResembleAI/chatterbox)** (Resemble AI, MIT license),
which beats ElevenLabs in independent blind preference tests.
## How to use (manual A/B)
1. Upload a **reference audio** clip of the voice to clone (5–20 s of clean speech is ideal).
2. (Optional) Tick **🧹 Remove background audio from reference** to isolate the voice
(HT-Demucs) before cloning if the clip has music/noise. Use **Preview cleaned reference**
to hear the isolated result first.
3. Pick the **language** (default: English).
4. Type the **text** to speak (long scripts are auto-chunked at sentence boundaries).
5. Click **Clone & Speak** → you get audio in the cloned voice.
Tip: leave the reference empty to hear a built-in sample voice for the selected language.
### Cloning defaults (tuned for faithful cloning)
Tuned for **speaker similarity**, not expressiveness:
`exaggeration=0.4` (neutral), `cfg_weight=0.5` (balanced; ~0.3 faster pace, 0.0 cross-lingual),
`temperature=0.7` (consistent). All knobs are exposed as sliders.
## API (for bot integration later)
Gradio exposes a programmatic endpoint named **`clone`** (plus **`isolate_voice`** for
standalone background-audio removal):
```python
from gradio_client import Client, handle_file
client = Client("ZeroPointMonkey/voice-clone-bench")
sr_path = client.predict(
text="Hey, it's good to finally hear your voice.",
language_id="en",
audio_prompt_path=handle_file("reference.wav"),
exaggeration=0.4,
cfg_weight=0.5,
temperature=0.7,
seed=0,
clean_reference=False, # True = strip background music/noise first
repetition_penalty=2.0,
min_p=0.05,
top_p=1.0,
api_name="/clone",
)
print(sr_path) # path to generated wav
# Just clean a reference clip (returns isolated-voice wav):
cleaned = client.predict(handle_file("noisy_reference.wav"), api_name="/isolate_voice")
```
## Notes
- Hardware: ZeroGPU (`zero-a10g`). Outputs are PerTh-watermarked by the model.
- License: model weights are **MIT** (Resemble AI / Chatterbox) — free for commercial use.
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