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jordandecho-tts-base

EchoTTS

Exposes Echo's flow-matching step count and separate text/speaker guidance scales.

Text to speechVoiceHub-nativeecho-ditParameters: Not reportedLanguage: enTraining: preprocessedLicense: Checkpoint-specific

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

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: Exposes Echo's flow-matching step count and separate text/speaker guidance scales.

Inputs and controls: Increase num_steps only after measuring latency and audio quality on the target device.

from pathlib import Path

from voicehub import AutoModelForTextToSpeech, TTSGenerationConfig

model = AutoModelForTextToSpeech.from_pretrained(
    'jordand/echo-tts-base',
    model_type='echo',
    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"),
    ),
    num_steps=20,
    cfg_scale_text=3.0,
    cfg_scale_speaker=1.0,
)
print(output.file_path, output.sample_rate, output.metadata)

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

Overview

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

Property Value
Task Text to speech
Architecture echo-dit
Runtime VoiceHub-native
Languages en
Capabilities text-to-speech, voice-cloning, fine-tuning, flow-matching, safetensors, voicehub-native, native-runtime
Reusable components —
Normalized output TTSOutput

Language support

Supported language abbreviations

en

The release card and reference implementation demonstrate English prompts and do not claim broader language coverage.

Paper and GitHub

Configuration

Load configuration without constructing the model:

from voicehub import AutoConfig

config = AutoConfig.for_model('echo')
print(config.model_type)
Property Value
Canonical model type echo
Configuration class EchoTTSConfig
Architecture class EchoTTSForTextToSpeech

Processing

Create the registered processor without allocating model weights:

from voicehub import AutoProcessor

processor = AutoProcessor.from_pretrained(
    'jordand/echo-tts-base',
    model_type='echo',
)
print(type(processor).__name__)

Inference

The Usage example returns TTSOutput through AutoModelForTextToSpeech.

Input and output contract

Property Value
Readiness preprocessed
Data architecture diffusion
Sample rate Model/checkpoint specific
Contract getter get_tts_dataset_spec('echo')
Variant Required fields One of Boundary Other rules
flow-batch target_latents, text_input_ids, text_mask, speaker_latents, speaker_mask — Prepared —

Conditional flow-matching, rectified-flow, or diffusion 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 flow-matching
Recipe single-phase
Default phase flow
Training checkpoint jordand/echo-tts-base
Native training graph no
Phase Kind Components Required inputs Loss keys
flow objective model target_latents, text_input_ids, text_mask, speaker_latents, speaker_mask loss, flow_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 jordand/echo-tts-base
Hugging Face ID jordand/echo-tts-base
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.echo.modeling_echo.EchoTTSForTextToSpeech
Configuration voicehub.models.echo.configuration_echo.EchoTTSConfig
Source provenance voicehub/models/echo/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

EchoTTSConfig

View source

EchoTTSConfig(**config_kwargs)

Parameters

  • **config_kwargs — Configuration fields validated by EchoTTSConfig.

Model

EchoTTSForTextToSpeech

View source

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

Parameters

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

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

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