Voiceclonnx Openvoice v2
ONNX export of the OpenVoice v2 tone-color converter (myshell-ai/OpenVoice, MIT license) for use with vconnx — a pure-ONNX zero-shot voice conversion toolkit.
Source du modèle
- Type
- Other
- Taille du fichier
- 2,21–122 MB
- Version
- vRevision 34d010c
- Mis à jour
- 25 oct. 2026
- Téléchargements
- 94
- Source
- Hugging Face
Description de la source
vconnx-openvoice-v2
ONNX export of the OpenVoice v2 tone-color converter (myshell-ai/OpenVoice, MIT license) for use with vconnx — a pure-ONNX zero-shot voice conversion toolkit.
Export details
Both components are exported from the upstream SynthesizerTrn
(myshell-ai/OpenVoice) with strict state-dict loading — no architecture
reconstruction. The checkpoint loads with 0 missing keys and 0 unexpected keys.
| Component | File | Size |
|---|---|---|
| Reference encoder (FP32) | tone_ref_encoder.onnx | 3.1 MB |
| Reference encoder (INT8) | tone_ref_encoder_q8.onnx | 2.2 MB |
| Voice converter (FP32) | tone_converter.onnx | 122.1 MB |
| Voice converter (INT8) | tone_converter_q8.onnx | 38.9 MB |
Architecture
| Sub-graph | ONNX inputs | ONNX output |
|---|---|---|
tone_ref_encoder.onnx | spec (B, T, 513) float32 — linear STFT magnitude | tone_embedding (B, 256) |
tone_converter.onnx | spec (B, 513, T), spec_lengths (B,), src_g (B, 256, 1), tgt_g (B, 256, 1) | audio (B, 1, samples) float32 — raw waveform |
The converter includes the full VITS-style flow decoder and HiFi-GAN vocoder; it outputs raw audio directly. No separate vocoder step is needed at inference.
Preprocessing: linear magnitude spectrogram matching upstream
spectrogram_torch — Hann window, n_fft=1024, hop=256, win=1024, reflect-pad
384 on each side, sqrt(Re2 + Im2 + 1e-6). No log compression.
Parity (upstream torch vs ONNX)
| Component | max_abs_delta | mean_abs_delta | Status |
|---|---|---|---|
tone_ref_encoder | 8.64e-07 | 2.45e-07 | PASS |
tone_converter (5 seeds) | 1.08e-02 (worst) | 1.26e-04 (avg) | PASS |
The converter max_abs divergence is due to float32 accumulation through 4
residual coupling blocks in the flow — the quality-relevant metric is
mean_abs, which passes at 1e-3.
E2E sanity check
Converted a 2 s synthetic source (220 Hz harmonics) to a 330 Hz reference:
| Metric | Value |
|---|---|
| Output duration | 1.997 s (source 2.000 s, ratio 0.998) |
| RMS | 0.268 |
| Spectral flatness | 0.072 (tonal, not noise) |
| Sample rate | 22050 Hz |
Usage
from vconnx import VoiceCloner
cloner = VoiceCloner(engine="openvoice")
cloner.clone_voice("source.wav", "reference.wav", "output.wav")
Or with the low-level adapter:
from vconnx.engines.openvoice import OpenVoiceV2Adapter
adapter = OpenVoiceV2Adapter(quantized=False)
adapter.clone_voice("source.wav", "reference.wav", "output.wav")
Install with: pip install vconnx[openvoice]
Provenance
- Upstream weights: myshell-ai/OpenVoiceV2
- Upstream source: myshell-ai/OpenVoice
- License: MIT ("Starting from April 2024, both V2 and V1 are released under MIT License. Free for commercial use." — official README)
- Export method: legacy TorchScript ONNX exporter (
dynamo=False), opset 14 (new dynamo exporter fails on GRU) - Strict load: 0 missing keys, 0 unexpected keys
Sources
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Extraits de sources
3 extraits--- license: mit pipeline_tag: audio-to-audio tags: - voice-conversion - onnx - vconnx - openvoice language: - en --- # vconnx-openvoice-v2 ONNX export of the **OpenVoice v2** tone-color converter ([myshell-ai/OpenVoice](https://github.com/myshell-ai/OpenVoice), MIT license) for use with [vconnx](https://github.com/TigreGotico/vconnx) — a pure-ONNX zero-shot voice conversion toolkit. ## Export details Both components are exported from the upstream `SynthesizerTrn` (`myshell-ai/OpenVoice`) with **strict state-dict loading** — no architecture reconstruction. The checkpoint loads with 0 missing keys and 0 unexpected keys. | Component | File | Size | |---|---|---| | Reference encoder (FP32) | `tone_ref_encoder.onnx` | 3.1 MB | | Reference encoder (INT8) | `tone_ref_encoder_q8.onnx` | 2.2 MB | | Voice converter (FP32) | `tone_converter.onnx` | 122.1 MB | | Voice converter (INT8) | `tone_converter_q8.onnx` | 38.9 MB | ## Architecture | Sub-graph | ONNX inputs | ONNX output | |---|---|---| | `tone_ref_encoder.onnx` | `spec` `(B, T, 513)` float32 — linear STFT magnitude | `tone_embedding` `(B, 256)` | | `tone_converter.onnx` | `spec` `(B, 513, T)`, `spec_lengths` `(B,)`, `src_g` `(B, 256, 1)`, `tgt_g` `(B, 256, 1)` | `audio` `(B, 1, samples)` float32 — raw waveform | The converter includes the full VITS-style flow decoder **and** HiFi-GAN vocoder; it outputs raw audio directly. No separate vocoder step is needed at inference. **Preprocessing:** linear magnitude spectrogram matching upstream `spectrogram_torch` — Hann window, n_fft=1024, hop=256, win=1024, reflect-pad 384 on each side, `sqrt(Re² + Im² + 1e-6)`. No log compression. ## Parity (upstream torch vs ONNX) | Component | max_abs_delta | mean_abs_delta | Status | |---|---|---|---| | `tone_ref_encoder` | 8.64e-07 | 2.45e-07 | **PASS** | | `tone_converter` (5 seeds) | 1.08e-02 (worst) | 1.26e-04 (avg) | **PASS** | The converter `max_abs` divergence is due to float32 accumulation through 4 residual coupling blocks in the flow — the quality-relevant metric is `mean_abs`, which passes at 1e-3. ## E2E sanity check Converted a 2 s synthetic source (220 Hz harmonics) to a 330 Hz reference: | Metric | Value | |---|---| | Output duration | 1.997 s (source 2.000 s, ratio 0.998) | | RMS | 0.268 | | Spectral flatness | 0.072 (tonal, not noise) | | Sample rate | 22050 Hz | ## Usage ```python from vc...
Source context: 94 downloads · 0 likes · Pipeline audio-to-audio · Repo TigreGotico/voiceclonnx-openvoice-v2
Source context: 16 downloads · 0 likes · Pipeline audio-to-audio · Repo TigreGotico/voiceclonnx-openvoice-v2
