ConversationTTS¶

Assigns an explicit conversation speaker and caps the generated audio duration.

Text to speechVoiceHub-nativeconversationttsParameters: Not reportedLanguages: en, zh +1Training: nativeLicense: CC-BY-NC-4.0

Parameter metadata: Not reported: the audited metadata available for the registered default does not provide an exact parameter total.

Use this model Checkpoint

Resources

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: Assigns an explicit conversation speaker and caps the generated audio duration.

Inputs and controls: Use stable integer speaker IDs when building multi-turn context.

from pathlib import Path

from voicehub import AutoModelForTextToSpeech, TTSGenerationConfig

model = AutoModelForTextToSpeech.from_pretrained(
    'AudioFoundation/SpeechFoundation',
    model_type='conversationtts',
    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"),
    ),
    speaker=0,
    max_audio_length_ms=30_000,
    temperature=0.9,
    top_k=50,
)
print(output.file_path, output.sample_rate, output.metadata)

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

Overview¶

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

Property	Value
Task	Text to speech
Architecture	`conversationtts`
Runtime	`VoiceHub-native`
Languages	`en`, `zh`, `yue`
Capabilities	`text-to-speech`, `voice-cloning`, `conversation`, `multilingual`, `fine-tuning`, `safetensors`, `voicehub-native`, `native-runtime`, `raw-audio-fine-tuning`, `preencoded-code-fine-tuning`, `noncommercial`
Reusable components	—
Normalized output	`TTSOutput`

Language support¶

Supported language abbreviations

en, zh, yue

These are the languages explicitly named in the upstream release README's podcast data.

Paper and GitHub¶

Paper: No dedicated upstream research paper is published for this integration.
Upstream GitHub: ConversationTTS
VoiceHub source: VoiceHub model implementation

Configuration¶

Load configuration without constructing the model:

from voicehub import AutoConfig

config = AutoConfig.for_model('conversationtts')
print(config.model_type)

Property	Value
Canonical model type	`conversationtts`
Configuration class	`ConversationTTSConfig`
Architecture class	`ConversationTTSForTextToSpeech`

Processing¶

Create the registered processor without allocating model weights:

from voicehub import AutoProcessor

processor = AutoProcessor.from_pretrained(
    'AudioFoundation/SpeechFoundation',
    model_type='conversationtts',
)
print(type(processor).__name__)

Inference¶

The Usage example returns TTSOutput through AutoModelForTextToSpeech.

Input and output contract¶

Property	Value
Readiness	`integrated-raw`
Data architecture	`codec-lm`
Sample rate	24,000 Hz
Contract getter	`get_tts_dataset_spec('conversationtts')`

Variant	Required fields	One of	Boundary	Other rules
`raw-text-audio`	—	text / texts; audio / audio_values	Source	at most one: text / texts; audio / audio_values; forbidden: text_token_ids, text_ids, audio_codes, codes
`raw-text-code`	—	text / texts; audio_codes / codes	Prepared	at most one: text / texts; audio_codes / codes; forbidden: text_token_ids, text_ids, audio, audio_values
`tokenized-text-audio`	—	text_token_ids / text_ids; audio / audio_values	Prepared	at most one: text_token_ids / text_ids; audio / audio_values; forbidden: text, texts, audio_codes, codes
`tokenized-text-code`	—	text_token_ids / text_ids; audio_codes / codes	Prepared	at most one: text_token_ids / text_ids; audio_codes / codes; forbidden: text, texts, audio, audio_values
`multi-codebook-batch`	`tokens`, `labels`, `tokens_mask`	—	Prepared	—

Autoregressive text/audio-token or codec-language-model 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	`native`
Family	`causal-lm`
Recipe	`single-phase`
Default phase	`codec_language_model`
Training checkpoint	`AudioFoundation/SpeechFoundation`
Native training graph	`yes`

Phase	Kind	Components	Required inputs	Loss keys
`codec_language_model`	objective	`model`	`tokens`, `labels`, `tokens_mask`	`loss`, `codebook0_loss`, `residual_loss`

The integration accepts its declared source or prepared contract directly. Call model.validate_training_support() first, then follow the training workflow.

Checkpoints, provenance, license, and limitations¶

Property	Value
Default checkpoint	`AudioFoundation/SpeechFoundation`
Hugging Face ID	`AudioFoundation/SpeechFoundation` 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.conversationtts.modeling_conversationtts.ConversationTTSForTextToSpeech`
Configuration	`voicehub.models.conversationtts.configuration_conversationtts.ConversationTTSConfig`
Source provenance	`voicehub/models/conversationtts/source/SOURCE.json`
License	CC-BY-NC-4.0

Source, checkpoints, datasets, and evaluation tools are non-commercial. Commercial use: not allowed.

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