supertonicsynth

A flat-layout Python synthesis package for Supertonic-3. OnnxVoice owns catalog resolution, managed asset installation and integrity, ONNX Runtime sessions, providers, and inference. SupertonicSynth owns text synthesis, language selection, voice styles, chunking, static voice-level calibration, output gain, and synthesis metadata.

Install

pip install "supertonicsynth[cpu]"

The project uses dynamic VCS versioning through setuptools_scm. Model weights are not included in the wheel.

The package requires OnnxVoice >=0.2.0,<0.3. The 0.2.x line provides the Supertonic catalog, semantic voice refs, managed installation, local-open, and runtime APIs used by this package.

Python usage

from supertonicsynth import (
    SupertonicRuntime,
    SynthesisConfig,
    VoiceLevelConfig,
)

config = SynthesisConfig(
    voice_level=VoiceLevelConfig(mode="calibrated"),
)

with SupertonicRuntime.from_pretrained("supertonic-3") as tts:
    result = tts.synthesize_text(
        "A prepared German sentence.",
        voice="F1",
        language="de",
        config=config,
    )
    result.write_wav("output.wav")

print(result.metadata["voice_ref"])
print(result.metadata["voice_level"]["calibration_key"])

result.pcm16() and result.write_wav(path) use the same finite-audio validation and clipping policy.

Atomic synthesis

SupertonicRuntime.synthesize is the strict one-request API. It encodes one request and performs at most one inference. It does not split text, join audio chunks, insert inter-chunk silence, peak-normalize, or apply output gain. synthesize_text and package-level synthesize remain convenience APIs that preserve the existing multi-chunk composition behavior.

from supertonicsynth import GenerationConfig, SupertonicRuntime, SynthesisRequest

request = SynthesisRequest(id="job-42", text="A prepared German sentence.", language="de")
with SupertonicRuntime.from_pretrained("supertonic-3") as tts:
    measurement = tts.measure_request(request)
    result = tts.synthesize(
        request,
        voice="F1",
        config=GenerationConfig(steps=5, speed=1.05, seed=42),
    )
    result.write_wav("atomic.wav")

print(measurement.amount, measurement.maximum, measurement.fits)

RequestMeasure reports encoded token count and only reports a maximum when model metadata declares max_input_tokens. If no maximum is declared, maximum and fits are None; callers must not infer a capacity limit.

Metadata-only discovery

from supertonicsynth import discover_models, runtime_identity

for model in discover_models(language="de"):
    print(model.id, model.voice_ids, model.max_input_tokens)
    print(runtime_identity(model))

Discovery reads typed OnnxVoice catalog metadata. It does not install model assets or open runtime sessions. Pass offline=True to use cached catalog metadata only. Optional catalog fields, including max_input_tokens, remain unknown when the catalog does not declare them.

CLI

supertonicsynth synthesize \
  "A prepared German sentence." \
  --model supertonic-3 \
  --voice F1 \
  --language de \
  --voice-level calibrated \
  --no-normalize-audio \
  --output-gain 1.0 \
  -o output.wav

Use --voice-gain-db FLOAT for an explicit static dB override. That override takes precedence over the catalog and works for local or custom styles. --output-gain FLOAT is a separate request-level linear gain. Final output is clipped to [-1, 1] for safety.

List voices

supertonicsynth voices --model supertonic-3

voices lists catalog voice metadata without installing model assets or opening runtime sessions. --offline uses cached catalog metadata only; omit it to allow catalog metadata retrieval. Use --refresh-catalog, --cache-dir, and --catalog-path to control catalog lookup.

Voice identity and language

OnnxVoice semantic voice refs are the canonical voice identity. A managed F1 style from supertonic-3 has the voice ref supertonic:supertonic-3/F1. Language remains a separate synthesis condition. Calibration therefore uses a pair such as (supertonic:supertonic-3/F1, de), serialized as supertonic:supertonic-3/F1@de. The @de suffix belongs to the calibration key, not the voice ref.

Automatic catalog calibration applies only to managed OnnxVoice installations and catalog-backed string styles. Local bundles and caller-created VoiceStyle values do not receive calibration by matching their directory or style name. They remain unchanged unless an explicit voice_level.gain_db override is provided.

Loudness calibration and output gain

Calibration is an offline, reviewed static correction. The benchmark measures prepared speech and the promotion tool stores measured gain against the canonical voice ref and synthesis language. Normal synthesis performs no LUFS measurement and does not contact a network service for calibration. Missing identity or missing calibration leaves the audio unchanged.

VoiceLevelConfig(mode="off") is the default. mode="calibrated" opts into the packaged catalog. normalize_audio is a separate deterministic peak-normalization control. output_gain is a separate linear user-requested gain. Neither feature performs final program mastering, which remains an external responsibility.

The package ships 252 reviewed, statistically eligible voice/language calibrations from the Supertonic-3 counting benchmark. Coverage is partial, not the full 310-key matrix: 48 completed but high-variability identities and all 10 Croatian (hr) identities are intentionally absent because a Croatian counting stimulus could not be prepared. Missing entries remain unchanged at runtime (0 dB). The na unknown-language sentinel is excluded because it is not a spoken calibration language. See the benchmark and promotion guide.

Further documentation

• Architecture
• Troubleshooting
• Benchmark and promotion

Local bundle

with SupertonicRuntime.from_local("/models/supertonic-3") as tts:
    result = tts.synthesize_text("Hello.", voice="F1", language="en")

Local bundles are unmanaged and do not claim a canonical OnnxVoice voice ref for automatic calibration.

Repository layout

The package deliberately has no src/ layer:

supertonicsynth/
tests/
examples/
benchmarks/
pyproject.toml

Licensing

SupertonicSynth project code is Apache-2.0. Portions of the frontend and text behavior are derived from the archived MIT-licensed supertone-oss-archive/supertonic-py; the upstream MIT notice is preserved under licenses/SUPERTONIC-PY-MIT.txt and in NOTICE.

Supertonic-3 model assets are not included in this package and retain their upstream OpenRAIL-M model license.