Beginner Audio Processing Tutorial

Learn the basics of audio processing with NeMo Curator using the FLEURS multilingual speech dataset. This tutorial walks you through a complete audio processing pipeline from data loading to quality assessment and filtering.

Overview

This tutorial demonstrates the core audio curation workflow:

1. Load Dataset: Download and prepare the FLEURS dataset
2. ASR Inference: Transcribe audio using NeMo ASR models
3. Quality Assessment: Calculate Word Error Rate (WER)
4. Duration Analysis: Extract audio file durations
5. Filtering: Keep only high-quality samples
6. Export: Save processed results

What you’ll learn:

• How to build an end-to-end audio curation pipeline
• Loading multilingual speech datasets (FLEURS)
• Running ASR inference with NeMo models
• Calculating quality metrics (WER, duration)
• Filtering audio by quality thresholds
• Exporting curated results in JSONL format

Time to complete: Approximately 15-30 minutes (depending on dataset size and GPU availability)

Working Example Location

The complete working code for this tutorial is located at:

<nemo_curator_repository>/tutorials/audio/fleurs/
├── README.md           # Tutorial documentation
├── pipeline.py         # Main tutorial script
├── pipeline.yaml       # Configuration file for run.py
└── run.py              # Same as pipeline.py, but defines pipeline using YAML file instead

Accessing the code:

$
# Clone NeMo Curator repository
$
git clone https://github.com/NVIDIA/NeMo-Curator.git
$
cd NeMo-Curator/tutorials/audio/fleurs/

Prerequisites

• NeMo Curator installed (see Installation Guide)
• NVIDIA GPU (required for ASR inference, minimum 16GB VRAM recommended)
• Internet connection for dataset download
• Basic Python knowledge
• CUDA-compatible PyTorch installation
• Sufficient disk space (FLEURS dataset requires ~10-50GB depending on language and split)

Step-by-Step Walkthrough

Step 1: Import Required Modules

Import all necessary stages and components for the audio curation pipeline:

1
from nemo_curator.pipeline import Pipeline
2
from nemo_curator.backends.xenna import XennaExecutor
3
from nemo_curator.stages.audio.datasets.fleurs.create_initial_manifest import CreateInitialManifestFleursStage
4
from nemo_curator.stages.audio.inference.asr_nemo import InferenceAsrNemoStage
5
from nemo_curator.stages.audio.metrics.get_wer import GetPairwiseWerStage
6
from nemo_curator.stages.audio.common import GetAudioDurationStage, PreserveByValueStage
7
from nemo_curator.stages.audio.io.convert import AudioToDocumentStage
8
from nemo_curator.stages.text.io.writer import JsonlWriter
9
from nemo_curator.stages.resources import Resources

Key components:

• Pipeline: Container for organizing and executing processing stages
• XennaExecutor: Backend executor for running the pipeline
• CreateInitialManifestFleursStage: Downloads and prepares FLEURS dataset
• InferenceAsrNemoStage: Runs ASR inference with NeMo models
• GetPairwiseWerStage: Calculates Word Error Rate
• PreserveByValueStage: Filters data based on threshold values
• JsonlWriter: Exports results in JSONL format

Step 2: Create the Pipeline

Build the audio curation pipeline by adding stages in sequence:

1
def create_audio_pipeline(args):
2
    """Create audio curation pipeline."""
3
    
4
    pipeline = Pipeline(name="audio_inference", description="Process FLEURS dataset with ASR")
5
    
6
    # Stage 1: Load FLEURS dataset
7
    pipeline.add_stage(
8
        CreateInitialManifestFleursStage(
9
            lang=args.lang,           # e.g., "hy_am" for Armenian
10
            split=args.split,         # "dev", "train", or "test"
11
            raw_data_dir=args.raw_data_dir
12
        ).with_(batch_size=4)  # Process 4 samples per batch
13
    )
14
    
15
    # Stage 2: ASR inference
16
    pipeline.add_stage(
17
        InferenceAsrNemoStage(
18
            model_name=args.model_name,  # e.g., "nvidia/stt_hy_fastconformer_hybrid_large_pc"
19
            pred_text_key="pred_text"  # Field name for ASR predictions
20
        ).with_(resources=Resources(gpus=1.0))  # Allocate 1 GPU
21
    )
22
    
23
    # Stage 3: Calculate WER
24
    pipeline.add_stage(
25
        GetPairwiseWerStage(
26
            text_key="text",           # Ground truth field
27
            pred_text_key="pred_text", # ASR prediction field
28
            wer_key="wer"             # Output WER field
29
        )
30
    )
31
    
32
    # Stage 4: Extract duration
33
    pipeline.add_stage(
34
        GetAudioDurationStage(
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            audio_filepath_key="audio_filepath",
36
            duration_key="duration"
37
        )
38
    )
39
    
40
    # Stage 5: Filter by WER threshold
41
    pipeline.add_stage(
42
        PreserveByValueStage(
43
            input_value_key="wer",
44
            target_value=args.wer_threshold,  # e.g., 75.0
45
            operator="le"  # less than or equal
46
        )
47
    )
48
    
49
    # Stage 6: Convert to DocumentBatch for export
50
    pipeline.add_stage(AudioToDocumentStage())
51
    
52
    # Stage 7: Export results
53
    result_dir = f"{args.raw_data_dir}/result"
54
    pipeline.add_stage(
55
        JsonlWriter(
56
            path=result_dir,
57
            write_kwargs={"force_ascii": False}
58
        )
59
    )
60
    
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    return pipeline

Stage explanations:

1. CreateInitialManifestFleursStage: Downloads FLEURS dataset from Hugging Face and creates audio manifest
2. InferenceAsrNemoStage: Loads NeMo ASR model and generates transcriptions (requires GPU)
3. GetPairwiseWerStage: Compares ground truth and predictions to calculate WER
4. GetAudioDurationStage: Reads audio files to extract duration metadata
5. PreserveByValueStage: Filters samples, keeping only those with WER ≤ threshold
6. AudioToDocumentStage: Converts AudioBatch to DocumentBatch format for export
7. JsonlWriter: Saves filtered results as JSONL manifest

Step 3: Run the Pipeline

Configure pipeline parameters and execute:

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def main():
2
    # Configuration
3
    class Args:
4
        lang = "hy_am"  # Armenian language
5
        split = "dev"   # Development split
6
        raw_data_dir = "/data/fleurs_output"
7
        model_name = "nvidia/stt_hy_fastconformer_hybrid_large_pc"
8
        wer_threshold = 75.0
9
    
10
    args = Args()
11
    
12
    # Create pipeline
13
    pipeline = create_audio_pipeline(args)
14
    
15
    # Create executor
16
    executor = XennaExecutor()
17
    
18
    # Run pipeline
19
    pipeline.run(executor)
20
    
21
    print("Pipeline completed!")
22
    print(f"Results saved to: {args.raw_data_dir}/result/")
23
24
if __name__ == "__main__":
25
    main()

Configuration parameters:

• lang: Language code from FLEURS dataset (e.g., “en_us”, “ko_kr”, “es_419”)
• split: Dataset split to process (“dev”, “train”, or “test”)
• raw_data_dir: Directory for downloading and storing FLEURS data
• model_name: NeMo ASR model identifier from NGC or Hugging Face
• wer_threshold: Maximum acceptable WER percentage (samples above this are filtered out)

Running the Complete Example

To run the working tutorial:

$
cd tutorials/audio/fleurs/
$
$
python tutorials/audio/fleurs/pipeline.py \
>
  --raw_data_dir ./example_audio/fleurs \
>
  --model_name nvidia/stt_hy_fastconformer_hybrid_large_pc \
>
  --lang hy_am \
>
  --split dev \
>
  --wer_threshold 75 \
>
  --gpus 1 \
>
  --clean \
>
  --verbose

Command-line options:

• --raw_data_dir: Output directory for dataset and results (required)
• --lang: FLEURS language code (default: “hy_am”)
• --split: Dataset split to process (default: “dev”)
• --model_name: NeMo ASR model name (default: matches language)
• --wer_threshold: Maximum WER for filtering (default: 75.0)

Expected execution time:

• Dataset download (first run): 5-15 minutes
• ASR inference: 1-5 minutes for dev split (~100 samples) with GPU
• Quality assessment and export: < 1 minute

System requirements during execution:

• GPU memory: 8-16GB (depending on model size)
• Disk space: 10-50GB (dataset + results)
• RAM: 8GB minimum

Understanding the Results

After running the pipeline, you’ll find:

• Downloaded data: FLEURS audio files and transcriptions in <raw_data_dir>/downloads/
• Processed manifest: JSONL file(s) with ASR predictions and quality metrics in <raw_data_dir>/result/
• Filtered results: Only samples meeting the WER threshold

Example output entry:

1
{
2
    "audio_filepath": "/data/fleurs_output/dev/sample.wav",
3
    "text": "բարև աշխարհ",
4
    "pred_text": "բարև աշխարհ", 
5
    "wer": 0.0,
6
    "duration": 2.3
7
}

Field descriptions:

• audio_filepath: Absolute path to audio file
• text: Ground truth transcription from FLEURS
• pred_text: ASR model prediction
• wer: Word Error Rate percentage (0.0 = perfect match)
• duration: Audio duration in seconds

Analyzing results:

$
# Count filtered samples
$
cat /data/fleurs_output/result/*.jsonl | wc -l
$
$
# View first 5 samples
$
head -n 5 /data/fleurs_output/result/*.jsonl | jq .
$
$
# Calculate average WER
$
cat /data/fleurs_output/result/*.jsonl | jq -r '.wer' | awk '{sum+=$1; count+=1} END {print "Average WER:", sum/count "%"}'

Using Python:

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import json
2
import pandas as pd
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from pathlib import Path
4
5
# Load results
6
result_files = list(Path("/data/fleurs_output/result").glob("*.jsonl"))
7
data = []
8
for file in result_files:
9
    with open(file, 'r') as f:
10
        for line in f:
11
            data.append(json.loads(line))
12
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df = pd.DataFrame(data)
14
15
# Summary statistics
16
print(f"Total samples: {len(df)}")
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print(f"Average WER: {df['wer'].mean():.2f}%")
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print(f"Average duration: {df['duration'].mean():.2f}s")
19
print(f"WER range: {df['wer'].min():.2f}% - {df['wer'].max():.2f}%")

Troubleshooting

Common Issues

GPU out of memory:

RuntimeError: CUDA out of memory

Solution: Reduce batch size or use a smaller ASR model:

1
pipeline.add_stage(
2
    CreateInitialManifestFleursStage(...).with_(batch_size=2)  # Reduce from 4 to 2
3
)

Dataset download fails:

ConnectionError: Failed to download FLEURS dataset

Solution: Check internet connection and retry. The stage will resume from where it left off.

No GPU available:

RuntimeError: No CUDA GPUs are available

Solution: Ensure CUDA is installed and GPU is accessible:

$
nvidia-smi  # Check GPU availability
$
python -c "import torch; print(torch.cuda.is_available())"

Model download fails:

OSError: Model 'nvidia/stt_...' not found

Solution: Verify model name is correct and you have internet access. Check available models at NGC Catalog.

Performance Optimization

• Increase batch size for faster processing (if GPU memory allows)
• Use multiple GPUs by setting Resources(gpus=2.0) or higher
• Process subset of data by using split="dev" (smaller than “train”)
• Skip ASR inference if you already have both predicted and target transcriptions. (remove InferenceAsrNemoStage)

Next Steps

After completing this tutorial, explore:

• Custom Manifests: Process your own audio datasets
• Process Data: Advanced quality filtering techniques
• Save & Export: Export curated audio datasets

Best Practices

• Start with dev split: Test your pipeline on the smaller development split before processing the full training set
• Adjust WER thresholds by language: Some languages may require more lenient thresholds (e.g., 75-80% for low-resource languages)
• Monitor GPU usage: Use nvidia-smi to track GPU memory and utilization during processing
• Validate results: Always inspect a sample of output records to verify quality
• Document parameters: Keep track of configuration values (thresholds, models) for reproducibility

Related Topics

• Audio Curation Quickstart: Quick introduction to audio curation
• Load Data: Audio data loading options
• Process Data: Comprehensive quality metrics guide
• Save & Export: Advanced export options and formats