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hexgradKokoro-82M

Kokoro

Selects a Kokoro voice ID and explicit speaking speed.

Text to speechVoiceHub-nativekokoroParameters: 81.8MLanguages: en-US, en-GB +7Training: preprocessedLicense: 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
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: Selects a Kokoro voice ID and explicit speaking speed.

Inputs and controls: Voice IDs are checkpoint-specific; af_heart belongs to the registered Kokoro release.

from pathlib import Path

from voicehub import AutoModelForTextToSpeech, TTSGenerationConfig

model = AutoModelForTextToSpeech.from_pretrained(
    'hexgrad/Kokoro-82M',
    model_type='kokoro',
    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"),
    ),
    voice="af_heart",
    speed=1.0,
)
print(output.file_path, output.sample_rate, output.metadata)

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

Overview

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

Property Value
Task Text to speech
Architecture kokoro
Runtime VoiceHub-native
Languages en-US, en-GB, es, fr, … complete audited list below
Capabilities text-to-speech, multilingual, fine-tuning, safetensors, voicehub-native, native-runtime
Reusable components —
Normalized output TTSOutput

Language support

Supported language abbreviations

en-US, en-GB, es, fr, hi, it, pt-BR, ja, zh

Paper and GitHub

Configuration

Load configuration without constructing the model:

from voicehub import AutoConfig

config = AutoConfig.for_model('kokoro')
print(config.model_type)
Property Value
Canonical model type kokoro
Configuration class KokoroConfig
Architecture class KokoroForTextToSpeech

Processing

Create the registered processor without allocating model weights:

from voicehub import AutoProcessor

processor = AutoProcessor.from_pretrained(
    'hexgrad/Kokoro-82M',
    model_type='kokoro',
)
print(type(processor).__name__)

Inference

The Usage example returns TTSOutput through AutoModelForTextToSpeech.

Input and output contract

Property Value
Readiness preprocessed
Data architecture acoustic
Sample rate 24,000 Hz
Contract getter get_tts_dataset_spec('kokoro')
Variant Required fields One of Boundary Other rules
full-preprocessed durations input_ids / phonemes; ref_s / voice; audio_values / audio / labels Prepared —
duration-only durations, training_phase input_ids / phonemes; ref_s / voice Prepared —

Direct acoustic, mel, codec, or waveform regression 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 preprocessed
Family acoustic-regression
Recipe multi-phase
Default phase acoustic
Training checkpoint hexgrad/Kokoro-82M
Native training graph yes
Phase Kind Components Required inputs Loss keys
duration objective model.bert, model.bert_encoder, model.predictor input_ids, ref_s, durations loss
acoustic objective model.bert, model.bert_encoder, model.predictor, model.text_encoder, model.decoder input_ids, ref_s, durations, audio_values loss

Prepare the exact tensors listed in the data contract before this step. Call model.validate_training_support() first, then follow the training workflow.

Checkpoints, provenance, license, and limitations

Property Value
Default checkpoint hexgrad/Kokoro-82M
Hugging Face ID hexgrad/Kokoro-82M
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.kokoro.modeling_kokoro.KokoroForTextToSpeech
Configuration voicehub.models.kokoro.configuration_kokoro.KokoroConfig
Source provenance voicehub/models/kokoro/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

KokoroConfig

View source

KokoroConfig(**config_kwargs)

Parameters

  • **config_kwargs — Configuration fields validated by KokoroConfig.

Model

KokoroForTextToSpeech

View source

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

Parameters

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

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

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