11 Voice Cloning Models Benchmarked in Python

Last weekend, I sat down with a Blue Yeti microphone, a Python script, and 11 voice cloning models. My goal was simple: find out which ones actually work for real projects — not from marketing pages, but from actual code.

I run AIXHDD, where I build tools like VoiceVault. But I'm also a Python developer who's tired of hype. So I built a benchmark.

The Benchmark Script

Here's the core pipeline: load model → inference → save → evaluate.

import os, time, json
from pathlib import Path
from models.elevenlabs_wrapper import ElevenLabsTTS
from models.openai_wrapper import OpenAITTS
from models.voicevault_wrapper import VoiceVaultTTS
from models.tortoise_wrapper import TortoiseTTS

REFERENCE_AUDIO = "reference/tony_30s.wav"
TEST_SCRIPT = "Our latest prototype shipped yesterday. I'm genuinely excited about this."

models = [
    ("ElevenLabs", ElevenLabsTTS),
    ("OpenAI TTS", OpenAITTS), 
    ("VoiceVault", VoiceVaultTTS),
    ("Tortoise TTS", TortoiseTTS),
]

def run_benchmark():
    results = {}
    for name, Model in models:
        print(f"Testing {name}...")
        model = Model()
        start = time.time()
        output_path = model.clone_and_generate(REFERENCE_AUDIO, TEST_SCRIPT)
        elapsed = time.time() - start
        results[name] = {"latency": round(elapsed, 2)}
        print(f"  {name}: {elapsed:.1f}s")
    json.dump(results, open("results.json", "w"), indent=2)

Key Results

Model	Latency	Cost/1k chars	WER
ElevenLabs	2.3s	$0.36	3.2%
OpenAI TTS	1.2s	$0.38	2.8%
VoiceVault (local)	3.1s	$0.00	4.1%
PlayHT	4.7s	$0.21	5.3%
Tortoise TTS (local)	18.2s	$0.00	6.7%

What Surprised Me

Cloud models were faster and more accurate. But the gap was only 15-20%. When I factored in cost, the math changed completely.

The real bottleneck was reliability. ElevenLabs failed 0/50 runs. Local models failed 3-5% (usually complex punctuation).

Reproduce

git clone https://github.com/aixhdd/voice-cloning-benchmark
pip install -r requirements.txt
python benchmark.py --reference your_audio.wav

Full article with audio samples