Audio
Real-Time-Voice-Cloning
Clone a voice from 5 seconds of audio and generate arbitrary speech in real time
★ 60,2k estrellasNOASSERTION
Ver la guía →MARS5-TTS es un proyecto de audio de código abierto de Camb-ai (Camb-ai/MARS5-TTS en GitHub). Tiene 2.821 estrellas en GitHub y 246 bifurcaciones, se publica bajo la licencia AGPL-3.0 y se actualizó por última vez en GitHub el 1 de agosto de 2024.
MARS5 speech model from CAMB.AI — clone a voice from 5 seconds of audio with striking prosody
Gather the text you want spoken and a clean reference recording of the target voice (1–12 seconds; around 6 seconds is optimal) — plus its transcript for best quality.
Install the dependencies with pip; the model itself loads through torch.hub, so cloning is optional (clone if you want the Hugging Face/local path).
Create an InferenceConfig: choose deep_clone=True with the reference transcript for quality, or False for fast shallow cloning, and tune temperature/top_k if needed.
Call mars5.tts(...) with your text, reference audio and config, and save the returned 24 kHz audio tensor.
An autoregressive transformer produces coarse EnCodec features, then a multinomial DDPM model refines the remaining codebooks before vocoding.
Speaker identity comes from a reference clip of 2–12 seconds; no transcript needed for a shallow clone.
Providing the reference transcript enables deep cloning — slower, but typically higher quality.
Trained on raw audio with byte-pair-encoded text, so commas add pauses and capitalization adds emphasis — a natural way to steer delivery.
Load AR and NAR models in two lines with torch.hub.load('Camb-ai/mars5-tts', 'mars5_english', trust_repo=True).
InferenceConfig exposes deep_clone, temperature, top_k, top_p, repetition/frequency penalties and more.
Clone MARS5-TTS if you want the local/Hugging Face path; torch.hub loading works without cloning.
git clone https://github.com/Camb-ai/MARS5-TTS.git
cd MARS5-TTSInstall the packages listed in the README (Python >= 3.10, Torch >= 2.0).
pip install --upgrade torch torchaudio librosa vocos encodec safetensors regexLoad the model from torch.hub and run the tts() call from the README example.
import torch, librosa
mars5, config_class = torch.hub.load('Camb-ai/mars5-tts', 'mars5_english', trust_repo=True)CAMB.AI maintains the model and repo; CAMB.AI also offers commercial dubbing/TTS products on its website.
The repo is AGPL-3.0 licensed, so it is free to use under AGPL terms (including copyleft obligations for network use). Check the license before commercial use.
No — inference runs locally in PyTorch; model weights load from torch.hub or Hugging Face.
gTTS calls a hosted service for standard voices. MARS5 is a local model focused on voice cloning and difficult prosody from a few seconds of reference audio.
pip install the listed dependencies, load the model with torch.hub, prepare a 2–12s reference clip (plus transcript for deep clone), and call mars5.tts().
Clone a voice from 5 seconds of audio and generate arbitrary speech in real time
🚀Clone a voice in 5 seconds to generate arbitrary speech in real-time
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Fish Speech — Fish Audio's expressive open text-to-speech and voice cloning: SOTA-quality TTS you can self-host, under the Fish Audio Community License (check terms)
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MOSS-TTS-Nano: a 0.1B open multilingual speech model that clones voices and streams in real time on CPU
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Clone a voice from 5 seconds of audio and generate arbitrary speech in real time
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Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.