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# ReVoice-2025 — Speech Enhancement Hackathon (Baseline)
This repository represents a **baseline** (basic solution) for participating in the **ReVoice-2025** hackathon. The project is based on the **Miipher** model and adapted for the competition. We tried to make the code as clean, fast, and convenient as possible.
## 🚀 Quick Start
### 1. Environment Setup
Python 3.10.11 is recommended.
```bash
git clone https://github.com/mtuciru/ReVoice-2025
cd ReVoice-2025
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
pip install --no-dependencies git+https://github.com/Wataru-Nakata/ssl-vocoders.git
export PYTHONPATH=./src
```
### 2. Downloading Pre-trained Weights
The script will automatically download Miipher and HiFiGAN weights to the `./models` folder.
```bash
python3 scripts/download_weights.py
```
### 3. Dataset Preparation
Training the model requires a prepared dataset (clean + noisy audio + phonemes).
The script takes your folder with clean audio, adds noise (using the degrader config), and generates phonemes (using GigaAM for transcription if no text is present).
**Important**: Before running, edit `examples/configs/degrader_config.yaml`, specifying the path to your noise files (`noise_dir` parameter etc., if used).
```bash
python3 scripts/prepare_dataset.py \
--input_dir /path/to/clean_audio \
--output_dir /path/to/processed_dataset \
--degrader_config examples/configs/degrader_config.yaml
```
### 4. Training Configuration
All training settings are located in `examples/configs/config.yaml`.
Main parameters to check:
* `data.train_dataset_path`: Path to the folder you created in step 3.
* `data.val_dataset_path`: Path to the validation set.
* `train.trainer.devices`: Number and IDs of GPUs (default `1`).
### 5. Starting Training
```bash
python3 examples/train.py
```
### 6. Monitoring (TensorBoard)
Monitor training progress and metrics:
```bash
tensorboard --logdir logs/
```
### 7. Inference (Speech Restoration)
To restore speech from noisy files, use the `run_miipher.py` script. It takes a folder with input files and a folder to save the result.
```bash
python3 scripts/run_miipher.py \
--input_dir /path/to/noisy_audio \
--output_dir /path/to/restored_audio \
--lang_code rus \
--miipher_ckpt ./models/miipher.ckpt \
--vocoder_ckpt ./models/hifigan.ckpt
```
Arguments:
* `--input_dir`: Folder with noisy files (`.wav`, `.mp3`, `.flac`).
* `--output_dir`: Folder where restored files will be saved.
* `--lang_code`: Language code for phonetization (default `rus`). If text transcripts (`.txt`) exist, the script will try to find them. Otherwise, ASR (GigaAM) will be used.
### 8. Quality Evaluation (Metrics)
To calculate metrics (SI-SNR, STOI, MelLoss), use `eval.py`. The script compares the folder with restored files (hypotheses) and the folder with clean reference files (references).
```bash
python3 eval.py \
--hyp_dir /path/to/restored_audio \
--ref_dir /path/to/clean_reference_audio \
--output_csv metrics_results.csv
```
Arguments:
* `--hyp_dir`: Folder with your restored files.
* `--ref_dir`: Folder with clean original files (files must have matching names).
* `--output_csv`: Path to save the results table (default `metrics_results.csv`).
---
## 📂 Project Structure
* `examples/train.py` — Main script for starting training.
* `examples/configs/config.yaml` — Configuration for hyperparameters, paths, and the model.
* `run_miipher.py` — Script for running inference on a folder.
* `eval.py` — Script for calculating metrics on a folder.
* `scripts/prepare_dataset.py` — Script for dataset generation (augmentation + phonemization).
* `scripts/download_weights.py` — Weight downloader.
* `src/miipher/lightning_module.py` — Training logic (Pytorch Lightning), training step, validation, metrics.
* `src/miipher/dataset` — Data loading logic (Dataset, DataModule).
* `src/miipher/metrics/eval_metrics.py` — Implementation of SI-SNR, STOI, MelLoss metrics.
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