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myshell-aiOpenVoiceV2

OpenVoice

Runs OpenVoice tone-color transfer from a source utterance to an authorized target-speaker recording.

Text to speechVoiceHub-nativeopenvoice-v2-converterParameters: 32.8MLanguages: en, es +4Training: customLicense: Checkpoint-specific

Parameter metadata: Exact learned-parameter total for VoiceHub's audited native primary graph at the registered default selection; separately loaded auxiliary models are excluded.

Use this model Checkpoint
Resources
Paper Upstream GitHub VoiceHub source Open in Colab

Usage

Complete the VoiceHub installation once, then run this repository-authored example. Model pages intentionally contain no package-install command.

This example is maintained against VoiceHub's public API; it is not copied from an upstream demo or package README.

Model-specific path: Runs OpenVoice tone-color transfer from a source utterance to an authorized target-speaker recording.

Inputs and controls: base.wav must contain the utterance to convert; reference.wav supplies only the target tone color.

from pathlib import Path

from voicehub import AutoModelForTextToSpeech, TTSGenerationConfig

BASE_AUDIO = Path("base.wav")
REFERENCE_AUDIO = Path("reference.wav")
for audio_file in (BASE_AUDIO, REFERENCE_AUDIO):
    if not audio_file.is_file():
        raise FileNotFoundError(audio_file)

model = AutoModelForTextToSpeech.from_pretrained(
    'myshell-ai/OpenVoiceV2',
    model_type='openvoice',
    device="cuda",
    lazy_load=True,
)
output = model.generate(
    'VoiceHub keeps model integrations explicit and reproducible.',
    generation_config=TTSGenerationConfig(
        seed=42,
        output_file=Path("output.wav"),
    ),
    base_audio=str(BASE_AUDIO),
    speaker_audio_path=str(REFERENCE_AUDIO),
    tau=0.3,
)
print(output.file_path, output.sample_rate, output.metadata)

Use authorized recordings. Verify hardware needs and pin a revision in production.

Overview

openvoice is a VoiceHub text to speech integration. This page is generated from its registry contract. Open the openvoice Colab notebook.

Property Value
Task Text to speech
Architecture openvoice-v2-converter
Runtime VoiceHub-native
Languages en, es, fr, zh, ja, ko
Capabilities text-to-speech, voice-cloning, multilingual, fine-tuning, safetensors, voicehub-native, native-runtime, paired-waveform-training, explicit-base-waveform
Reusable components wavmark
Normalized output TTSOutput

Language support

Supported language abbreviations

en, es, fr, zh, ja, ko

Paper and GitHub

Configuration

Load configuration without constructing the model:

from voicehub import AutoConfig

config = AutoConfig.for_model('openvoice')
print(config.model_type)
Property Value
Canonical model type openvoice
Configuration class OpenVoiceConfig
Architecture class OpenVoiceForTextToSpeech

Processing

Create the registered processor without allocating model weights:

from voicehub import AutoProcessor

processor = AutoProcessor.from_pretrained(
    'myshell-ai/OpenVoiceV2',
    model_type='openvoice',
)
print(type(processor).__name__)

Inference

The Usage example returns TTSOutput through AutoModelForTextToSpeech.

Input and output contract

Property Value
Readiness integrated-raw
Data architecture vits
Sample rate 22,050 Hz
Contract getter get_tts_dataset_spec('openvoice')
Variant Required fields One of Boundary Other rules
paired-waveforms source_audio, target_audio — Source —
paired-waveform-aliases audio, target_waveform — Source —

VITS/GAN text, waveform, spectrogram, and adversarial data. See the data workflow.

Training and optimization

Use available_optimization_passes() to discover reversible public passes. Unsupported runtime or hardware fails closed before mutation.

Training contract

Property Value
Support custom
Family vits
Recipe single-phase
Default phase generator
Training checkpoint myshell-ai/OpenVoiceV2
Native training graph yes
Phase Kind Components Required inputs Loss keys
generator generator model.enc_q, model.flow, model.dec, model.ref_enc source_spectrogram, source_lengths, target_waveform, target_lengths loss

This profile uses model-specific phases; inspect and honor each phase boundary. Call model.validate_training_support() first, then follow the training workflow.

Checkpoints, provenance, license, and limitations

Property Value
Default checkpoint myshell-ai/OpenVoiceV2
Hugging Face ID myshell-ai/OpenVoiceV2
Repository availability verified through the Hugging Face model API on 2026-08-11; pin a revision before production use.
Checkpoint status Registry default; pin an immutable revision for production and reproducible evidence
Optional dependency extra Core package
Hardware and runtime Usage selects cuda; verify checkpoint-specific requirements
Real-checkpoint evidence Release evidence; a registry default alone is not execution evidence
Implementation voicehub.models.openvoice.modeling_openvoice.OpenVoiceForTextToSpeech
Configuration voicehub.models.openvoice.configuration_openvoice.OpenVoiceConfig
Source provenance voicehub/models/openvoice/source/SOURCE.json
License Checkpoint-specific

No VoiceHub-specific license override is registered. Verify the checkpoint and upstream source terms before use.

Confirm the checkpoint revision, access terms, provenance, and license.

Limitations

  • No integration-specific checkpoint limitation is registered. Verify the selected checkpoint revision and its documented runtime requirements.
  • Validate memory, precision, and optional dependencies on the target system.
  • Public optimizations fail closed when the runtime or hardware cannot satisfy their validation contract; an unavailable pass is not reported as applied.
  • Contract tests do not replace the linked released-checkpoint evidence.

Public API

Use the stable configuration, processor, and task-model facades below.

Configuration

OpenVoiceConfig

View source

OpenVoiceConfig(**config_kwargs)

Parameters

  • **config_kwargs — Configuration fields validated by OpenVoiceConfig.

Model

OpenVoiceForTextToSpeech

View source

AutoModelForTextToSpeech.from_pretrained(
    pretrained_model_name_or_path,
    *,
    model_type='openvoice',
    config=None,
    **model_kwargs,
)

Parameters

  • pretrained_model_name_or_path — Hub ID or compatible local directory.
  • model_type — Canonical model type; use 'openvoice'.
  • config — Optional preloaded OpenVoiceConfig instance.
  • **model_kwargs — Model-specific loading arguments.
from voicehub import get_model_spec

spec = get_model_spec('openvoice')
print(spec.display_name, spec.task.value)
Purpose Public object
Discover get_model_spec('openvoice')
Load and run AutoModelForTextToSpeech
Configure OpenVoiceConfig
Process AutoProcessor
Model implementation OpenVoiceForTextToSpeech
Normalized output TTSOutput
Training contract get_training_spec('openvoice')
Optimization lifecycle available_optimization_passes, apply_optimization_plan, optimization_manifest, restore_optimization_plan

See all model guides, inference, and the training matrix.