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OpenVoice - AI Audio Models Tool

Overview

OpenVoice is an instant voice-cloning framework hosted on Hugging Face that converts text into speech using the timbre of a provided reference audio clip. According to the model card on Hugging Face, OpenVoice supports zero-shot cross-lingual voice cloning, enabling generation in languages different from the reference sample without additional per-language training. The model exposes fine-grained control over voice style attributes such as emotion, accent, rhythm, pauses, and intonation, making it suitable for applications that require nuanced, expressive synthetic speech (e.g., character voice replication, multilingual narration, and voice-based avatars). The project is distributed under an MIT license on its Hugging Face repository, and the model is available as a text-to-speech pipeline there. The model card does not disclose architecture details or parameter counts, and there are no formal academic benchmark results published on the model page. For integration, users can run the model via the Hugging Face Inference API or download the repository from Hugging Face for local inference and experimentation (see the model page for repository resources and example usage). (Source: https://huggingface.co/myshell-ai/OpenVoice)

Model Statistics

Likes: 486
Pipeline: text-to-speech

License: mit

Model Details

Public model information is provided on the Hugging Face model card for myshell-ai/OpenVoice. The card lists this repo as a text-to-speech pipeline and shows an MIT license, but it does not publish base architecture or parameter counts (the model card lists Base model: None and Parameters: unknown). Capabilities explicitly described on the model page include instant voice cloning from a short reference audio clip, zero-shot cross-lingual voice cloning, and granular controls over voice attributes such as emotion, accent, rhythm, pauses, and intonation. The model is intended for generating speech in multiple languages using only a reference recording of the target speaker; it offers style control at inference time rather than requiring speaker-specific fine-tuning. The model card and repository should be consulted for details about input formats, supported languages, recommended sampling rates, and example scripts. Because core architecture and parameter counts are not disclosed on the model page, users evaluating computational requirements should test locally or via the Hugging Face Inference API to determine latency and resource needs. (Source: https://huggingface.co/myshell-ai/OpenVoice)

Key Features

Instant voice cloning from a short reference audio clip without per-speaker training
Zero-shot cross-lingual cloning: speak in different languages using the reference voice
Granular style controls: emotion, accent, rhythm, pauses, and intonation at inference time
Hosted as a Hugging Face text-to-speech pipeline with MIT license
Intended for fast prototyping of multilingual narration, character voices, and voice avatars

Example Usage

Example (python):

from huggingface_hub import InferenceApi

# Example: call the Hugging Face Inference API for OpenVoice
# Replace HF_TOKEN with your Hugging Face access token (if required)

inference = InferenceApi(repo_id="myshell-ai/OpenVoice", token="HF_TOKEN")

# Open a short reference audio file of the target speaker
with open("reference.wav", "rb") as ref_audio:
    # Example parameters: text to speak and a simple style dict.
    # The exact parameter names and accepted values depend on the model card; check the repo for details.
    result = inference(
        inputs="Hello, this is a voice cloning test.",
        parameters={"language": "en", "style": {"emotion": "neutral"}},
        files={"reference_audio": ref_audio}
    )

# The inference client may return raw bytes or a dict containing an audio field. Adapt as necessary.
if isinstance(result, (bytes, bytearray)):
    with open("output.wav", "wb") as out:
        out.write(result)
else:
    # If API returns a dict with base64 audio or a URL, follow the model card instructions to extract audio
    print("Received response:", result)

# See the model page for exact parameter names, supported languages, and usage examples:
# https://huggingface.co/myshell-ai/OpenVoice