Update README.md

README.md

@@ -26,112 +26,7 @@ This dataset contains only the metadata (JSON/Parquet) for English speech recogn
   - [train-clean-360](https://www.openslr.org/resources/12/train-clean-360.tar.gz)
   - [train-other-500](https://www.openslr.org/resources/12/train-other-500.tar.gz)
 
-## Setup Instructions
 
-### 1. Download and Organize Audio Files
-After downloading, organize your audio files as follows:
-- `/cv` for CommonVoice audio
-- `/peoplespeech_audio` for People's Speech audio
-- `/librespeech-en` for LibriSpeech audio
-
-### 2. Convert Parquet Files to NeMo Manifests
-
-Create a script `parquet_to_manifest.py`:
-```python
-from datasets import load_dataset
-import json
-import os
-
-def convert_to_manifest(dataset, split, output_file):
-    with open(output_file, 'w') as f:
-        for item in dataset[split]:
-            # Ensure paths match your mounted directories
-            if item['source'] == 'commonvoice':
-                item['audio_filepath'] = os.path.join('/cv', item['audio_filepath'])
-            elif item['source'] == 'peoplespeech':
-                item['audio_filepath'] = os.path.join('/peoplespeech_audio', item['audio_filepath'])
-            elif item['source'] == 'librespeech':
-                item['audio_filepath'] = os.path.join('/librespeech-en', item['audio_filepath'])
-            
-            manifest_entry = {
-                'audio_filepath': item['audio_filepath'],
-                'text': item['text'],
-                'duration': item['duration']
-            }
-            f.write(json.dumps(manifest_entry) + '\n')
-
-# Load the dataset from Hugging Face
-dataset = load_dataset("WhissleAI/Meta_speech_recognition_EN_v1")
-
-# Convert each split to manifest
-for split in dataset.keys():
-    output_file = f"{split}_manifest.json"
-    convert_to_manifest(dataset, split, output_file)
-    print(f"Created manifest for {split}: {output_file}")
-```
-
-Run the conversion:
-```bash
-python parquet_to_manifest.py
-```
-
-This will create manifest files (`train_manifest.json`, `valid_manifest.json`, etc.) in NeMo format.
-
-### 3. 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 and mounted volumes
-docker run --gpus all -it --rm \
-    -v /external1:/external1 \
-    -v /external2:/external2 \
-    -v /external3:/external3 \
-    -v /cv:/cv \
-    -v /peoplespeech_audio:/peoplespeech_audio \
-    -v /librespeech-en:/librespeech-en \
-    --shm-size=8g \
-    -p 8888:8888 -p 6006:6006 \
-    --ulimit memlock=-1 \
-    --ulimit stack=67108864 \
-    --device=/dev/snd \
-    nvcr.io/nvidia/nemo:24.05
-```
-
-### 4. Fine-tuning Instructions
-
-#### A. Create a config file (e.g., `config.yaml`):
-```yaml
-model:
-  name: "ConformerCTC"
-  pretrained_model: "nvidia/stt_en_conformer_ctc_large" # or your preferred model
-  
-  train_ds:
-    manifest_filepath: "train_manifest.json"  # Path to the manifest created in step 2
-    batch_size: 32
-    
-  validation_ds:
-    manifest_filepath: "valid_manifest.json"  # Path to the manifest created in step 2
-    batch_size: 32
-    
-  optim:
-    name: adamw
-    lr: 0.001
-    
-  trainer:
-    devices: 1
-    accelerator: "gpu"
-    max_epochs: 100
-```
-
-#### B. Start Fine-tuning:
-```bash
-# Inside the NeMo container
-python -m torch.distributed.launch --nproc_per_node=1 \
-    examples/asr/speech_to_text_finetune.py \
-    --config-path=. \
-    --config-name=config.yaml
-```
 
 ## Dataset Statistics
 
@@ -174,21 +69,4 @@ python -m torch.distributed.launch --nproc_per_node=1 \
   "duration": 12.51,
   "source": "librespeech-en"
 }
-```
-
-
-## Usage Notes
-
-1. The metadata in this repository contains paths to audio files that must match your local setup.
-2. When fine-tuning, ensure your manifest files use the correct paths for your mounted directories.
-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. **Path Mismatches**: Ensure audio file paths in manifests match the mounted directories in Docker
-2. **Memory Issues**: Reduce batch size or use gradient accumulation
-3. **Docker Permissions**: Ensure proper permissions for mounted volumes and audio devices
+```