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TigreGotico/voiceclonnx-bicodec

sourceHugging Facecc-by-nc-sa-4.0updated 3mo agoView on Hugging Face

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vconnx-bicodec

ONNX artifacts for the bicodec engine of vconnx — a pure-ONNX multi-engine voice-cloning library.

BiCodec (SparkAudio/Spark-TTS, 2025) achieves zero-shot any-to-any voice conversion via explicit factorization of speech into two complementary token streams:

—Semantic tokens (content): Wav2Vec2-XLSR-53 (hidden layers 11, 14, 16 averaged) → convolutional encoder → FactorizedVQ → (1, T) int64
—Global tokens (speaker): 128-bin Slaney mel spectrogram → ECAPA-TDNN + Perceiver + FSQ → (1, 1, 32) int32 (32 fixed tokens per utterance)

Voice conversion: source semantic tokens + reference global tokens → decoder → waveform. No auto-regressive language model; single forward pass per chunk.

License

Upstream weights: CC BY-NC-SA 4.0 — non-commercial use only.

These ONNX artifacts are derived from SparkAudio/Spark-TTS-0.5B weights which are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

—You may not use these artifacts for commercial purposes.
—You must give appropriate credit to SparkAudio.
—Derivative works must carry the same CC BY-NC-SA 4.0 license.

Upstream code (SparkAudio/Spark-TTS): Apache-2.0.

Usage

python

from vconnx import VoiceCloner

cloner = VoiceCloner(engine="bicodec")
out = cloner.clone_voice("source.wav", "reference.wav", "out.wav")
print(cloner.sample_rate)   # 16000

Install: pip install vconnx

Components

File	Description	Size (fp32)	Size (INT8)
`wav2vec2_encoder.onnx`	Wav2Vec2-XLSR-53 encoder (layers 11/14/16 avg)	819 MB	206 MB
`semantic_encoder.onnx`	Conv encoder + FactorizedVQ → semantic tokens	116 MB	30 MB
`global_encoder.onnx`	ECAPA-TDNN + Perceiver + FSQ → global tokens	22 MB	6 MB
`decoder.onnx`	Token decoder → waveform	368 MB	158 MB
`mel_filterbank.npy`	128-bin Slaney mel filterbank (numpy, 128×513)	256 KB	—
`mel_config.json`	STFT parameters (n_fft=1024, hop=320, win=640)	~1 KB	—

Output sample rate: 16 kHz.

Parity vs PyTorch

Component	Metric	Value	Result
wav2vec2_encoder	max_abs	6.71e-4	PASS
semantic_encoder	exact int match	True	PASS
global_encoder	exact int match	True	PASS
mel numpy vs torchaudio	max_abs	0.00e+0	PASS
decoder	max_abs	2.53e-6	PASS

References

—Spark-TTS paper
—SparkAudio/Spark-TTS
—SparkAudio/Spark-TTS-0.5B (upstream weights)
—vconnx (inference library)