Update README.md

README.md

@@ -1,3 +1,25 @@
+---
+task_categories:
+- automatic-speech-recognition
+- audio-classification
+language:
+- ru
+- be
+- cs
+- bg
+- ka
+- mk
+- pl
+- sr
+- sl
+- uk
+tags:
+- speech-recogniton
+- entity-tagging
+- intent-classification
+- age-prediction
+- emotion-classication
+---
 # Meta Speech Recognition Slavic Languages Dataset (Common Voice)
 
 This dataset contains metadata for Slavic language speech recognition samples from Common Voice.
@@ -110,150 +132,4 @@ tar -xzf {lang}.tar.gz
 ├── sl/
 ├── sr/
 └── uk/
-```
-
-## Training NeMo Conformer ASR for Slavic Languages
-
-### 1. Pull and Run NeMo Docker
-```bash
-# Pull the NeMo Docker image
-docker pull nvcr.io/nvidia/nemo:24.05
-
-# Run the container with GPU support
-docker run --gpus all -it --rm \
-    -v /external1:/external1 \
-    -v /external2:/external2 \
-    -v /external3:/external3 \
-    -v /cv:/cv \
-    --shm-size=8g \
-    -p 8888:8888 -p 6006:6006 \
-    --ulimit memlock=-1 \
-    --ulimit stack=67108864 \
-    nvcr.io/nvidia/nemo:24.05
-```
-
-### 2. Create Training Script
-Create a script `train_nemo_asr_slavic.py`:
-```python
-from nemo.collections.asr.models import EncDecCTCModel
-from nemo.collections.asr.data.audio_to_text import TarredAudioToTextDataset
-import pytorch_lightning as pl
-from omegaconf import OmegaConf
-import os
-
-# Load the dataset from Hugging Face
-from datasets import load_dataset
-dataset = load_dataset("WhissleAI/Meta_STT_SLAVIC_CommonVoice")
-
-# Create config
-config = OmegaConf.create({
-    'model': {
-        'name': 'EncDecCTCModel',
-        'train_ds': {
-            'manifest_filepath': None,  # Will be set dynamically
-            'batch_size': 32,
-            'shuffle': True,
-            'num_workers': 4,
-            'pin_memory': True,
-            'use_start_end_token': False,
-        },
-        'validation_ds': {
-            'manifest_filepath': None,  # Will be set dynamically
-            'batch_size': 32,
-            'shuffle': False,
-            'num_workers': 4,
-            'pin_memory': True,
-            'use_start_end_token': False,
-        },
-        'optim': {
-            'name': 'adamw',
-            'lr': 0.001,
-            'weight_decay': 0.01,
-        },
-        'trainer': {
-            'devices': 1,
-            'accelerator': 'gpu',
-            'max_epochs': 100,
-            'precision': 16,
-        }
-    }
-})
-
-# Initialize model
-model = EncDecCTCModel(cfg=config.model)
-
-# Create trainer
-trainer = pl.Trainer(**config.model.trainer)
-
-# Train
-trainer.fit(model)
-```
-
-### 3. Create Config File
-Create a config file `config_slavic.yaml`:
-```yaml
-model:
-  name: "EncDecCTCModel"
-  train_ds:
-    manifest_filepath: "train.json"
-    batch_size: 32
-    shuffle: true
-    num_workers: 4
-    pin_memory: true
-    use_start_end_token: false
-    
-  validation_ds:
-    manifest_filepath: "valid.json"
-    batch_size: 32
-    shuffle: false
-    num_workers: 4
-    pin_memory: true
-    use_start_end_token: false
-    
-  optim:
-    name: adamw
-    lr: 0.001
-    weight_decay: 0.01
-    
-  trainer:
-    devices: 1
-    accelerator: "gpu"
-    max_epochs: 100
-    precision: 16
-```
-
-### 4. Start Training
-```bash
-# Inside the NeMo container
-python -m torch.distributed.launch --nproc_per_node=1 \
-    train_nemo_asr_slavic.py \
-    --config-path=. \
-    --config-name=config_slavic.yaml
-```
-
-## Usage Notes
-
-1. The dataset includes only metadata. Audio files must be downloaded separately from Common Voice.
-2. Audio files should be placed in the `/cv/cv-corpus-15.0-2023-09-08/{lang}/` directory structure.
-3. For optimal performance:
-   - Use a GPU with at least 16GB VRAM
-   - Adjust batch size based on your GPU memory
-   - Consider gradient accumulation for larger effective batch sizes
-   - Monitor training with TensorBoard (accessible via port 6006)
-
-## Common Issues and Solutions
-
-1. **Memory Issues**: 
-   - Reduce batch size if you encounter OOM errors
-   - Use gradient accumulation for larger effective batch sizes
-   - Enable mixed precision training (fp16)
-
-2. **Training Speed**:
-   - Increase num_workers based on your CPU cores
-   - Use pin_memory=True for faster data transfer to GPU
-   - Consider using tarred datasets for faster I/O
-
-3. **Model Performance**:
-   - Adjust learning rate based on your batch size
-   - Use learning rate warmup for better convergence
-   - Consider using a pretrained model as initialization
+```