Unlike every existing SVC (RVC, Diff-SVC, SO-VITS-SVC, DDSP-SVC, Reflow-VAE-SVC), NICO_SVC does NOT pass audio through a 16kHz speech encoder. Instead, it works directly with Mel spectrograms at native 44.1kHz sample rate.

🔥 Why?

Every SVC system in 2023-2025 uses a speech encoder (ContentVec/HuBERT/Whisper) that internally operates at 16kHz. This creates a bottleneck:

Voice 48kHz → Encoder 16kHz → Lost upper frequencies → Vocoder guesses → Plastic sound

NICO_SVC eliminates this entirely:

Voice 44.1kHz → Mel spectrogram (direct) → Reflow model → BigVGAN v2 → Real 44.1kHz output

🏗️ Architecture

Component	Details
Input	Mel spectrogram 44.1kHz (128 bands, hop=512, n_fft=2048)
F0 Extractor	RMVPE
Speaker Embedding	Learnable (trained per speaker)
Model	Reflow (Rectified Flow Matching) with Conv + Attention blocks
Parameters	~100M
Vocoder	BigVGAN v2 44kHz 128band (NVIDIA, pretrained)
Output	44.1kHz WAV
Training	Single-speaker fine-tune, no pretrain needed
Inference	~50 Euler steps, real-time capable

📊 Key Differences from Existing SVC

Feature	RVC / Diff-SVC / SO-VITS	NICO_SVC
Speech Encoder	ContentVec 16kHz	❌ None
Max Audio Frequency	~8kHz (encoder limit)	22kHz (full Mel)
Pretrain Required	Yes (multi-speaker)	No
Mel Source	From encoder output	Direct from WAV
Vocoder	HiFi-GAN / NSF-HiFiGAN	BigVGAN v2 (NVIDIA)
Architecture	Diffusion / DDSP	Reflow (faster, cleaner)
Sound Quality	Plastic, missing harmonics	Natural, full spectrum

🎯 Design Principles

No encoders — they all operate at 16kHz and destroy timbre
Native 44.1kHz — at every stage of the pipeline
Single-speaker focus — one model = one voice = perfect copy
Fine-tune only — no pretrain means no "average voice" contamination
Free tools only — runs on Google Colab free tier (T4 GPU)
Fast inference — 2-3 minutes per cover, not 18 minutes

📦 Pipeline

Preprocessing:

WAV 44.1kHz → Mel spectrogram (torchaudio)
            → F0 extraction (RMVPE)
            → Speaker embedding (learnable)

Training:

Mel (target) + noise → Reflow model learns to denoise
Conditioning: F0 + Speaker Embedding
Loss: MSE between predicted and target flow vectors
Optimizer: AdamW + fp16 mixed precision

Inference (Cover):

Input acapella → RMVPE (extract F0)
              → F0 * 2^(key/12) (pitch shift)
              → Reflow model (generate Mel with target speaker)
              → BigVGAN v2 (Mel → WAV)
              → Output cover

🎤 Training Data

Speaker: SomNICO_Ai (lyric tenor, natural flat tone without vibrato)
Dataset: up to 1951 files, ~3.92 hours
Languages: Russian, English, Japanese, Chinese
Quality: 48kHz WAV, single microphone, clean recording
Content: Pure singing, no background noise

🔧 Requirements

Google Colab (free tier, T4 GPU)
Google Drive (for checkpoints and data)
Python 3.12
PyTorch 2.x
torchaudio, librosa, BigVGAN v2, RMVPE

📈 Results

Metric	Value
Training loss	< 0.5 (272 files, 19M model)
Perceptual quality	Near-indistinguishable from original on test set
Inference speed	~50 steps, real-time on T4
Checkpoint size	~400MB (100M model)

👤 Author

Developer: SomNICO_Ai / Kedo
Age: 20
Vocal experience: 12 years (since age 6)
AI voice experience: 5 years, 143+ RVC models trained
YouTube: SomNICO_Ai
HuggingFace: 0qwpifs

📜 Philosophy

"I'm tired of using other people's tools. Every SVC crushes my voice through 16kHz encoders. I hear my voice differently — it has more detail, more character. NICO_SVC was built to preserve EVERYTHING."

🗓️ Timeline

Day 1: Full pipeline from scratch — Mel, F0, Reflow, BigVGAN working
Week 1: 100M model + Attention, first covers
Week 2-3: Volume conditioning, TensorBoard, production quality
Goal: Voice clone indistinguishable from real recording

License

MIT

Credits

BigVGAN v2: NVIDIA (pretrained vocoder)
RMVPE: yxlllc (F0 extraction)
Architecture inspired by Rectified Flow Matching papers
Built entirely in Google Colab, free tier

0qwpifs/NICO_SVC

NICO_SVC — Singing Voice Conversion without Encoders

🎤 What is NICO_SVC?