Welcome to the GitHub homepage for the highly optimized GGUF (float32) conversion of the nvidia/stt_pt_fastconformer_hybrid_large_pc model. This is a FastConformer Automatic Speech Recognition (ASR) model specifically trained for Portuguese (pt) speech-to-text, featuring native support for punctuation and capitalization (P&C).
Download the Model from Hugging Face Here
This model was explicitly converted and optimized to be run inside CAPIT—our custom desktop transcription software.
What is CAPIT? CAPIT is a fully offline, high-speed transcription application. It is built using Rust and Tauri, meaning it is incredibly lightweight, fast, and 100% private (no audio is ever sent to the cloud). Instead of writing python code or using terminal commands, you can simply load this model into the CAPIT desktop app and transcribe audio instantly with a beautiful user interface.
Check out the CAPIT Desktop App Here!
The model is designed for high-performance, local deployment with zero Python dependencies using the lightweight C++ parakeet-cpp engine (GGML backend).
Important
The GGUF file is fully standalone. Configuration parameters and the 128-token sentencepiece vocabulary are embedded directly inside the binary.
- Architecture: FastConformer Hybrid CTC/RNN-T (Large)
- Parameter Count: ~115M
- Vocabulary Size: 128 tokens
- Punctuation & Capitalization: Yes, natively supported (emits upper/lower case text, spaces, periods, commas, and question marks).
- Training Data: ~2,200 hours of Portuguese speech (Common Voice, Multilingual LibriSpeech, and a large proprietary corpus).
- Input Format: 16,000 Hz mono-channel WAV audio.
| Dataset / Test Set | Decoder Head | Word Error Rate (WER) | Character Error Rate (CER) |
|---|---|---|---|
| Common Voice 16.0 | RNNT | 12.03% | 3.20% |
| Common Voice 16.0 | CTC | 12.83% | 3.39% |
| Multilingual LibriSpeech | RNNT | 24.78% | 5.92% |
| Multilingual LibriSpeech | CTC | 25.70% | 6.18% |
Tested on local hardware:
- Test Audio Duration: 7.26 seconds (mono, 16000Hz).
- Metric: Real-Time Factor (RTF). Lower is faster.
| Configuration | Average Inference Time | Real-Time Factor (RTF) | CPU / RAM Footprint |
|---|---|---|---|
| Portuguese Hybrid (GPU / CUDA) | 0.490s | 0.067x (14.9x speed) | ~520 MB VRAM |
| Portuguese Hybrid (CPU) | 0.395s | 0.054x (18.5x speed) | ~140 MB RAM |
To run this model, you need the parakeet-cli C++ execution engine.
- Go to the parakeet.cpp GitHub Repository.
- Follow their build instructions to compile the
parakeet-cliexecutable for your specific operating system (Windows/Linux/macOS). - Once compiled, open your terminal and run the model using the following command:
parakeet-cli transcribe --model portuguese-fastconformer-hybrid-large.f32.gguf --input audio.wav --decoder ctc --lang pt- Original Checkpoint: nvidia/stt_pt_fastconformer_hybrid_large_pc
- License: Under NVIDIA NGC Terms of Use / CC-BY-4.0.