Home › Audio Models › OpenVoice V2

OpenVoice V2 - AI Audio Models Tool

Overview

OpenVoice V2 is an open-source text-to-speech model focused on high-fidelity voice cloning and flexible style control. According to the model card on Hugging Face, it supports instant voice cloning and aims to reproduce tone color and prosody closely, while offering zero-shot cross-lingual synthesis so a single voice can speak target text in other languages without per-voice retraining. The project is released under the MIT License and is described as suitable for both research and commercial use (see the Hugging Face model page). Compared with its predecessor, OpenVoice V2 emphasizes improved audio quality and more robust cross-lingual behavior. The model is published as a text-to-speech pipeline on Hugging Face and has gathered community attention (likes on the model page) while detailed parameter counts and standard benchmark numbers are not published on the model card. Users should consult the model README on the Hugging Face page for usage notes, real-time requirements, and available pre/post-processing scripts.

Model Statistics

Likes: 475
Pipeline: text-to-speech

License: mit

Model Details

OpenVoice V2 is presented as a text-to-speech pipeline on Hugging Face (pipeline: text-to-speech). The model card indicates the project provides instant voice cloning and style control features, plus zero-shot cross-lingual synthesis, but does not publish an explicit base model or parameter count on the model page. The repository and model card list an MIT license, enabling broad reuse (commercial and research) per the model metadata. Practical capabilities described on the model page include: cloning a target speaker's voice from a provided sample, controlling voice style and prosody at inference time, and synthesizing speech in languages different from the source sample without fine-tuning. The model card does not provide formal architecture diagrams, training dataset statistics, or exact parameter counts; users should check the model README linked from the Hugging Face page for any added technical notes or code for encoder/decoder details and recommended inference runtimes.

Key Features

Instant voice cloning from a provided sample audio without per-voice retraining
Zero-shot cross-lingual synthesis enabling a voice to speak other languages
Flexible voice style and prosody control at inference time
Improved audio quality compared to the model's previous version (per model card)
Released under the permissive MIT license for research and commercial use

Example Usage

Example (python):

from transformers import pipeline

# Example usage — actual invocation and return format may vary; check the model's README.
# Replace 'myshell-ai/OpenVoiceV2' with the exact model repo if different.

tts = pipeline("text-to-speech", model="myshell-ai/OpenVoiceV2")
result = tts("Hello! This is a short demo of OpenVoice V2.")

# Many TTS pipelines return a dict containing raw audio bytes or a waveform array.
# This example assumes the pipeline returns a bytes-like object under the 'wav' key.
if isinstance(result, dict) and "wav" in result:
    with open("openvoice_v2_output.wav", "wb") as f:
        f.write(result["wav"])
else:
    # Fallback: try writing raw result if it's bytes
    try:
        with open("openvoice_v2_output.raw", "wb") as f:
            f.write(result)
    except Exception:
        print("Check the model card for the correct inference output format and required libraries.")