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sesamecsm-1b

CSM

Builds CSM speaker context from a stable speaker index and paired reference recording.

Text to speechVoiceHub-nativecsmParameters: 1.6BLanguage: enTraining: nativeLicense: 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: Builds CSM speaker context from a stable speaker index and paired reference recording.

Inputs and controls: Reference audio and text must be supplied together; speaker IDs must be non-negative.

from pathlib import Path

from voicehub import AutoModelForTextToSpeech, TTSGenerationConfig

REFERENCE_AUDIO = Path("reference.wav")
REFERENCE_TEXT = "The reference transcript must exactly match the authorized audio."
if not REFERENCE_AUDIO.is_file():
    raise FileNotFoundError(REFERENCE_AUDIO)

model = AutoModelForTextToSpeech.from_pretrained(
    'sesame/csm-1b',
    model_type='csm',
    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,
    speaker_audio_path=str(REFERENCE_AUDIO),
    reference_text=REFERENCE_TEXT,
    max_audio_length_ms=30_000,
)
print(output.file_path, output.sample_rate, output.metadata)

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

Overview

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

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

Language support

Supported language abbreviations

en

Paper and GitHub

Configuration

Load configuration without constructing the model:

from voicehub import AutoConfig

config = AutoConfig.for_model('csm')
print(config.model_type)
Property Value
Canonical model type csm
Configuration class CSMConfig
Architecture class CSMForTextToSpeech

Processing

Create the registered processor without allocating model weights:

from voicehub import AutoProcessor

processor = AutoProcessor.from_pretrained(
    'sesame/csm-1b',
    model_type='csm',
)
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('csm')
Variant Required fields One of Boundary Other rules
conversation — conversation / messages Source —
grouped-audios texts, speaker_ids, audios — Source —
grouped-concatenated texts, speaker_ids, audio, audio_cut_idxs — Source —
utterance text, audio — Source —
tokenized input_ids, labels — 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 sesame/csm-1b
Native training graph yes
Phase Kind Components Required inputs Loss keys
codec_language_model objective model — loss, backbone_loss, depth_decoder_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 sesame/csm-1b
Hugging Face ID sesame/csm-1b
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.csm.modeling_csm.CSMForTextToSpeech
Configuration voicehub.models.csm.configuration_csm.CSMConfig
Source provenance voicehub/models/csm/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

CSMConfig

View source

CSMConfig(**config_kwargs)

Parameters

  • **config_kwargs — Configuration fields validated by CSMConfig.

Model

CSMForTextToSpeech

View source

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

Parameters

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

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

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