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Installation

VoiceHub supports Python 3.10 through 3.12. The default installation contains all built-in TTS, ASR, and VAD code. Checkpoints are downloaded separately when a model is loaded.

Registered native runtime paths are checked in CI to import only the Python standard library, VoiceHub, and PyTorch. Optional compiler or kernel packages are used only when a compatible optimization policy selects them; eager fallback remains available.

1. Create an environment

python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip

py -3.12 -m venv .venv
.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip

Confirm that python and pip use the same environment:

python --version
python -m pip --version

2. Install PyTorch

VoiceHub requires PyTorch 2.8. Accelerator builds depend on the operating system, driver, and hardware. Select the correct command from the PyTorch installer, then verify it:

python -c "import torch; print(torch.__version__, torch.cuda.is_available())"

CPU-only users can let pip resolve PyTorch in the next step.

3. Install VoiceHub

Install the released package:

python -m pip install voicehub

Add fine-tuning tools only when needed:

python -m pip install "voicehub[training]"

The training extra adds dataset, evaluation, and reporting packages. It does not change which model integrations are registered.

Other installation modes

Install the current Git branch:

python -m pip install \
  "voicehub @ git+https://github.com/kadirnar/voicehub.git@main"

Install a downloaded wheel:

python -m pip install dist/voicehub-0.3.0-py3-none-any.whl

Create an editable development checkout:

git clone https://github.com/kadirnar/voicehub.git
cd voicehub
python -m pip install -e ".[test,training]"

Verify the installation

Lightweight discovery does not load model weights:

python - <<'PY'
import sys
import voicehub

models = voicehub.list_model_specs(task=None)
print("VoiceHub:", voicehub.__version__)
print("Registered models:", len(models))
print("PyTorch imported during discovery:", "torch" in sys.modules)
PY

Inspect the selected accelerator separately:

python - <<'PY'
import torch

print("PyTorch:", torch.__version__)
print("CUDA available:", torch.cuda.is_available())
if torch.cuda.is_available():
    print("Device:", torch.cuda.get_device_name(0))
    print("BF16:", torch.cuda.is_bf16_supported())
PY

Finally, construct one model lazily. This validates the registry and configuration without downloading its checkpoint:

from voicehub import AutoModelForTextToSpeech

model = AutoModelForTextToSpeech.from_pretrained(
    "parler-tts/parler-tts-mini-v1",
    model_type="parlertts",
    device="cuda",
    lazy_load=True,
)
print(model.config.model_type)

The first inference call loads the checkpoint. Use model.load() when a service should fail during startup instead of on its first request.

Validate release artifacts

Maintainers can check all supported package layouts with one command:

python scripts/check_distribution.py

The script builds a wheel and source distribution, installs the wheel, sdist, and editable checkout into separate temporary environments, imports VoiceHub, and verifies required tokenizer, configuration, kernel, typing, and watermark files. It uses --no-deps by default so it does not download PyTorch three times.

Run the complete dependency check on a release machine:

python scripts/check_distribution.py --with-dependencies

Common errors

  • No matching distribution found: check the Python version and platform.
  • torch.cuda.is_available() is False: install a PyTorch build compatible with the local driver and hardware.
  • Out of memory: choose a smaller checkpoint or reduce batch size. Do not change precision or quantize until the model's support matrix confirms the quality boundary.
  • A checkpoint cannot be loaded: verify model_type, repository access, revision, artifact format, and checkpoint license.
  • An optimization is rejected: start in eager mode and inspect the returned optimization plan before enabling optional kernels or compilation.

Continue with the quickstart, then use the model catalog to choose a model.