README.md

---
license: mit
language:
  - ru
  - en
  - ja
  - zh
tags:
  - singing-voice-conversion
  - svc
  - reflow
  - bigvgan
  - voice-cloning
  - no-encoder
  - mel-spectrogram
  - audio
  - music
pipeline_tag: audio-to-audio
---

# NICO_SVC — Singing Voice Conversion without Encoders

## 🎤 What is NICO_SVC?

A custom Singing Voice Conversion (SVC) system built from scratch.
**No ContentVec. No HuBERT. No Whisper. No encoders at all.**

# NICO_SVC — Singing Voice Conversion without Encoders

## 🎤 What is NICO_SVC?

A custom Singing Voice Conversion (SVC) system built from scratch.
**No ContentVec. No HuBERT. No Whisper. No encoders at all.**

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

1. **No encoders** — they all operate at 16kHz and destroy timbre
2. **Native 44.1kHz** — at every stage of the pipeline
3. **Single-speaker focus** — one model = one voice = perfect copy
4. **Fine-tune only** — no pretrain means no "average voice" contamination
5. **Free tools only** — runs on Google Colab free tier (T4 GPU)
6. **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
```