ModelPublicadoExterno

Distill Neucodec Onnx

ONNX export of the DistillNeuCodec encoder for lightweight voice cloning inference.

Fuentehuggingface.co/KevinAHM/distill-neucodec-onnx/tree/2cd5cf022b7a1e689e561f0492787768cfe8395d

TipoOtherModelo baseneuphonic/distill-neucodecVersiónRevision 2cd5cf0Ccalidad

Fuente del modelo

Tipo: Other
Tamaño del archivo: 264 MB
Modelo base: neuphonic/distill-neucodec
Versión: vRevision 2cd5cf0
Actualizado: 14 oct 2026
Descargas: 0
Fuente: Hugging Face

Descripción de la fuente

DistillNeuCodec ONNX Encoder

ONNX export of the DistillNeuCodec encoder for lightweight voice cloning inference.

Model Description

This is an ONNX-optimized encoder that produces speech codes compatible with the NeuTTS voice cloning pipeline. The encoder extracts acoustic and semantic features from reference audio to enable zero-shot voice cloning.

Verification

This ONNX export achieves 100% identical output codes compared to the original PyTorch model across all tested audio files:

Test File	Duration	Codes	Match
`dave.wav`	7.45s	373	✓ 100%
`jo.wav`	13.06s	654	✓ 100%
`nellie.wav`	7.33s	367	✓ 100%

Usage

import numpy as np
import soundfile as sf
import onnxruntime

# Load model
sess = onnxruntime.InferenceSession("onnx/distill_neucodec_encoder.onnx")

# Load audio (must be 16kHz)
audio, sr = sf.read("reference.wav")
assert sr == 16000, f"Audio must be 16kHz, got {sr}Hz"

# IMPORTANT: Pre-pad to multiple of 320 samples
T = len(audio)
pad_for_wav = 320 - (T % 320)
audio = np.pad(audio, (0, pad_for_wav))

# Run inference
audio_input = audio[np.newaxis, np.newaxis, :].astype(np.float32)
codes = sess.run(None, {"audio": audio_input})[0].flatten().tolist()

print(f"Generated {len(codes)} codes")

Input/Output Specification

Name	Shape	Type	Description
Input: `audio`	`[1, 1, T]`	float32	16kHz audio, T must be divisible by 320
Output: `codes`	`[1, 1, F]`	int32	Speech codes, F ≈ T/320

Pre-padding Requirement

⚠️ Important: Input audio length must be padded to a multiple of 320 samples before inference:

T = len(audio)
pad_for_wav = 320 - (T % 320)
audio = np.pad(audio, (0, pad_for_wav))

This matches the behavior of the original PyTorch model's _prepare_audio() function.

Files

onnx/
├── distill_neucodec_encoder.onnx       # ONNX model
└── distill_neucodec_encoder.onnx.data  # External weights

Requirements

onnxruntime>=1.16.0
soundfile
numpy

License

Apache 2.0 - same as the base model.

Fuentes

1 fuente

Hugging FacePrincipalVersión 2cd5cf022b7a1e689e561f0492787768cfe8395d

huggingface.co/KevinAHM/distill-neucodec-onnx/tree/2cd5cf022b7a1e689e561f0492787768cfe8395d

Verificado 28 ago

Artefactos del modelo

1 artefacto

onnx/distill_neucodec_encoder.onnx

onnx · 264 MB · SHA-256 04af54f6af51…3a23 · Hugging Face

Descargar

Extractos de fuentes

2 extractos

Readme28 agohuggingface.co/KevinAHM/distill-neucodec-onnx

--- license: apache-2.0 base_model: - neuphonic/distill-neucodec tags: - audio - speech - audio-to-audio - speech-language-models - onnx - voice-cloning - neural-codec datasets: - amphion/Emilia-Dataset - facebook/multilingual_librispeech - CSTR-Edinburgh/vctk - google/fleurs - mozilla-foundation/common_voice_13_0 - mythicinfinity/libritts_r pipeline_tag: audio-to-audio --- # DistillNeuCodec ONNX Encoder ONNX export of the [DistillNeuCodec](https://huggingface.co/neuphonic/distill-neucodec) encoder for lightweight voice cloning inference. ## Model Description This is an ONNX-optimized encoder that produces speech codes compatible with the NeuTTS voice cloning pipeline. The encoder extracts acoustic and semantic features from reference audio to enable zero-shot voice cloning. ## Verification This ONNX export achieves **100% identical output codes** compared to the original PyTorch model across all tested audio files: | Test File | Duration | Codes | Match | |-----------|----------|-------|-------| | `dave.wav` | 7.45s | 373 | ✓ 100% | | `jo.wav` | 13.06s | 654 | ✓ 100% | | `nellie.wav` | 7.33s | 367 | ✓ 100% | ## Usage ```python import numpy as np import soundfile as sf import onnxruntime # Load model sess = onnxruntime.InferenceSession("onnx/distill_neucodec_encoder.onnx") # Load audio (must be 16kHz) audio, sr = sf.read("reference.wav") assert sr == 16000, f"Audio must be 16kHz, got {sr}Hz" # IMPORTANT: Pre-pad to multiple of 320 samples T = len(audio) pad_for_wav = 320 - (T % 320) audio = np.pad(audio, (0, pad_for_wav)) # Run inference audio_input = audio[np.newaxis, np.newaxis, :].astype(np.float32) codes = sess.run(None, {"audio": audio_input})[0].flatten().tolist() print(f"Generated {len(codes)} codes") ``` ## Input/Output Specification | Name | Shape | Type | Description | |------|-------|------|-------------| | **Input:** `audio` | `[1, 1, T]` | float32 | 16kHz audio, **T must be divisible by 320** | | **Output:** `codes` | `[1, 1, F]` | int32 | Speech codes, F ≈ T/320 | ## Pre-padding Requirement ⚠️ **Important:** Input audio length must be padded to a multiple of 320 samples before inference: ```python T = len(audio) pad_for_wav = 320 - (T % 320) audio = np.pad(audio, (0, pad_for_wav)) ``` This matches the behavior of the original PyTorch model's `_prepare_audio()` function. ## Files ``` onnx/ ├── distill_neucodec_encoder.on...

Note28 agohuggingface.co/KevinAHM/distill-neucodec-onnx

Source context: 0 downloads · 2 likes · Pipeline audio-to-audio · Repo KevinAHM/distill-neucodec-onnx