Are you curious about how to build your own custom speech recognition models on Windows using NVIDIA NeMo?
Whether you're diving into ASR (Automatic Speech Recognition) for the first time or you're an AI enthusiast looking to explore new tools, this guide is tailored just for you!
In this post, we'll walk you step-by-step through everything from installing NeMo on a Windows machine to training your first custom model. Let’s take the mystery out of machine learning together.
System Requirements and Setup
Before diving into NeMo, make sure your system is ready. NVIDIA NeMo relies heavily on GPU acceleration, so having the right hardware and drivers is critical.
| Component | Minimum Requirement |
|---|---|
| Operating System | Windows 10 (64-bit) or later |
| GPU | NVIDIA GPU with CUDA Compute Capability 7.0+ |
| CUDA Toolkit | CUDA 11.8+ |
| Python | Python 3.8 - 3.10 |
| RAM | Minimum 16GB (32GB recommended) |
Pro Tip: Make sure to install the latest NVIDIA driver that supports your CUDA version. Use nvidia-smi in the command prompt to confirm driver and GPU compatibility.
Installing NVIDIA NeMo on Windows
NVIDIA NeMo is officially supported on Linux, but you can still use it on Windows with some workarounds. The easiest method is to use the Windows Subsystem for Linux (WSL2).
- Install WSL2:
Run the following in PowerShell (Admin):
wsl --install - Install Ubuntu:
Choose Ubuntu as your WSL distro from the Microsoft Store.
- Install NVIDIA Drivers for WSL:
Download the latest WSL-compatible drivers from the official NVIDIA site.
- Install CUDA Toolkit:
Install CUDA inside WSL using the Ubuntu package manager.
- Set up a Python virtual environment:
Use Python 3.10 and set up venv or conda environment.
- Install NeMo:
Run:
pip install nemo_toolkit['asr']
💎 Key Note:
If you're using WSL2 with GPU support, confirm that your GPU is recognized inside the WSL terminal by running nvidia-smi.
How to Prepare Your Dataset
For a successful training process, your dataset must follow the format expected by NVIDIA NeMo. This typically includes audio files and a corresponding manifest file in JSON format.
Here's what a single entry in the manifest file looks like:
{
"audio_filepath": "/path/to/audio.wav",
"text": "transcription of the audio",
"duration": 3.45
}
Best practices:
✅ Use mono 16kHz WAV files for compatibility.
✅ Keep audio under 15 seconds to ensure model stability during training.
✅ Validate audio paths to prevent training interruptions.
💡 TIP: You can use open datasets like Mozilla Common Voice to practice building manifests and experimenting before training with your own data.
Training Your Custom Model
Once your dataset is ready, it's time to train your model. NVIDIA NeMo provides training scripts and pretrained models that you can fine-tune using your own dataset.
The simplest way to start is to fine-tune an existing model. Here’s a sample command:
python speech_to_text_train.py \
model.train_ds.manifest_filepath=./data/train_manifest.json \
model.validation_ds.manifest_filepath=./data/val_manifest.json \
trainer.max_epochs=50 \
trainer.devices=1 \
trainer.accelerator='gpu'
Important considerations:
✅ Use GPU acceleration to significantly reduce training time.
✅ Set appropriate batch sizes based on your VRAM.
✅ Monitor logs for warnings and NaN losses — early signs of issues.
You can resume training or tweak hyperparameters as needed. NVIDIA NeMo offers modular configs via Hydra, making it easier to experiment without editing Python scripts directly.
Common Issues and Debugging Tips
Working with speech models on Windows can introduce unique challenges, especially when using WSL2 and GPU acceleration. Here are some common issues and ways to resolve them.
✅ WSL2 not detecting GPU: Check if your driver supports WSL2. Use nvidia-smi inside WSL to confirm GPU visibility.
✅ ImportError or missing dependencies: Make sure your virtual environment is activated and packages installed with the correct flags (e.g., nemo_toolkit['asr']).
✅ Training crashes due to NaN loss: This could be due to invalid input data. Verify that your audio files are not corrupted and durations match metadata.
✅ Python version mismatch: NeMo works best with Python 3.10. Avoid using newer versions not officially supported.
✅ File path errors in JSON: Always use absolute paths or make sure your training script is run from the correct directory.
⚠️ Warning: Windows file paths with backslashes (e.g., C:\data\audio.wav) can cause issues. Use forward slashes or raw strings in JSON and Python scripts.
Resources for Further Learning
Diving deeper into NVIDIA NeMo and custom ASR models can be incredibly rewarding. Below are trusted resources where you can continue your journey, learn advanced concepts, and stay updated with best practices.
- NVIDIA NeMo Official Page
The starting point for documentation, installation guides, and model descriptions directly from NVIDIA.
- NeMo User Guide
A full user guide that walks through training, inference, configuration files, and best practices.
- NVIDIA NeMo GitHub
Explore source code, example scripts, and open issues. Great for developers looking to customize further.
- Pretrained Models on NGC
Official pretrained models hosted on NVIDIA’s GPU Cloud (NGC), ready for fine-tuning.
💡 TIP: Join the NVIDIA Developer Forums to get help from other users and developers using NeMo in real-world scenarios.
Wrapping Up
And there you have it—your first step into the world of custom speech models with NVIDIA NeMo on Windows!
While getting everything set up might feel intimidating at first, especially with the Linux-based tooling on a Windows system, the results are well worth it. Voice interfaces are the future, and by learning to build your own models, you're staying ahead of the curve.
Let us know what kind of model you're building in the comments!
Whether you're training it for your native language, a special dialect, or even for fun hobby projects, your journey matters.
Related Links
- NVIDIA Developer Blog - NeMo Tag
Read use cases, updates, and deep-dives into NeMo by NVIDIA engineers and researchers.
- Towards Data Science - NeMo Articles
Beginner-friendly tutorials and case studies written by the ML community using NeMo.
- Papers with Code - NVIDIA NeMo
Explore benchmarks, papers, and related implementations using NeMo on speech datasets.
Tags
NVIDIA NeMo, speech recognition, ASR, custom model, Windows WSL, deep learning, Python, machine learning, AI tools, open source
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