# Our MERT & BEST-RQ
Our implementation on MERT model. Files modified:
- mert_fairseq/models/mert/mert_model.py
- mert_fairseq/data/mert_dataset.py
- run_training_mulNodes_wotorchdist_womodelparsize.sh

# Prepare

The MERT training is implemented with [fairseq](https://github.com/pytorch/fairseq). You need to clone the fairseq repo inside our repo at ./src/fairseq and MERT implementation codes as a fairseq example projcet.

You can do that by following the steps:
```
mkdir -c ./src/fairseq
cd ./src
git clone https://github.com/pytorch/fairseq
```


# Docker
```
mirrors.tencent.com/cloudezhou/mert:v3
```

# Start 

### 1-node training

```
bash run_training_sglNodes.sh 0 dummy MERT_RVQ-VAE_CQT_330M_multinodes_debug1node
```


### 1-node training (BEST-RQ)

```
bash run_training_sglNodes.sh 0 dummy MERT_RVQ-VAE_CQT_95M_bestrq
```

### 4-node training
```
bash run_training_mulNodes_wotorchdist_womodelparsize.sh 0 dummy MERT_RVQ-VAE_CQT_330M_multinodes
bash run_training_mulNodes_wotorchdist_womodelparsize.sh 1 dummy MERT_RVQ-VAE_CQT_330M_multinodes
bash run_training_mulNodes_wotorchdist_womodelparsize.sh 2 dummy MERT_RVQ-VAE_CQT_330M_multinodes
bash run_training_mulNodes_wotorchdist_womodelparsize.sh 3 dummy MERT_RVQ-VAE_CQT_330M_multinodes
```


### 4-node training (BEST-RQ)
```
bash run_training_mulNodes_wotorchdist_womodelparsize.sh $INDEX dummy MERT_RVQ-VAE_CQT_95M_bestrq_multinodes BEST_RQ $CHIEF_IP
```

### 4-node training (MusicFM)
```
bash run_training_mulNodes_wotorchdist_womodelparsize.sh $INDEX dummy MusicFM_95M_multinodes MUSICFM $CHIEF_IP
```

### 4-node training (EAT)
```
bash run_training_eat.sh $INDEX dummy EAT_pretraining_music_multinodes EAT $CHIEF_IP
```

You could set the parameters in [mert_fairseq/config/pretrain/MERT_RVQ-VAE_CQT_330M.yaml](mert_fairseq/config/pretrain/MERT_RVQ-VAE_CQT_330M.yaml)

Our latest checkpoints is loaded at [data/fairseq_savedir/ckpt_MERT_RVQ-VAE_CQT/MERT_RVQ-VAE_CQT_330M/checkpoint_last.pt](data/fairseq_savedir/ckpt_MERT_RVQ-VAE_CQT/MERT_RVQ-VAE_CQT_330M/checkpoint_last.pt)