README.md

metadata

library_name: gguf
base_model: nvidia/stt_pt_fastconformer_hybrid_large_pc
base_model_relation: quantized
pipeline_tag: automatic-speech-recognition
tags:
  - audio
  - speech
  - gguf
  - fastconformer
  - conformer
  - ctc
  - rnnt
  - parakeet-cpp
  - ASR
  - Portuguese
  - edge-ai
language:
  - pt
license: cc-by-4.0
model-index:
  - name: Portuguese FastConformer-Hybrid (Large) GGUF
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Mozilla Common Voice 16.0 (Portuguese)
          type: mozilla-foundation/common_voice_16_0
          args:
            language: pt
        metrics:
          - name: Test WER (RNNT Head)
            type: wer
            value: 12.03
          - name: Test CER (RNNT Head)
            type: cer
            value: 3.2
          - name: Test WER (CTC Head)
            type: wer
            value: 12.83
          - name: Test CER (CTC Head)
            type: cer
            value: 3.39
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Multilingual Librispeech (MLS) Portuguese
          type: facebook/multilingual_librispeech
          args:
            language: pt
        metrics:
          - name: Test WER (RNNT Head)
            type: wer
            value: 24.78
          - name: Test CER (RNNT Head)
            type: cer
            value: 5.92
          - name: Test WER (CTC Head)
            type: wer
            value: 25.7
          - name: Test CER (CTC Head)
            type: cer
            value: 6.18

Portuguese FastConformer-Hybrid GGUF (Portuguese ASR)

This repository contains a 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).

The model is designed for high-performance, local deployment with zero Python dependencies using the lightweight C++ parakeet-cpp engine (GGML backend).

The GGUF file is fully standalone. Configuration parameters and the 128-token sentencepiece vocabulary are embedded directly inside the binary.

Model Details

• 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.

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Evaluation & Analytics

1. Accuracy Benchmarks (WER & CER)

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%

2. Local Performance & Speed Benchmarks

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

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💻 Integrates perfectly with RenderCaption

This model was explicitly converted and optimized to be run inside RenderCaption—our custom desktop transcription software.

What is RenderCaption? RenderCaption 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 RenderCaption desktop app and transcribe audio instantly with a beautiful user interface.

Check out the RenderCaption Desktop App on GitHub Here!

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Usage Instructions

To run this model, you need the parakeet-cli C++ execution engine.

1. Go to the parakeet.cpp GitHub Repository.
2. Follow their build instructions to compile the parakeet-cli executable for your specific operating system (Windows/Linux/macOS).
3. 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

Credits and Licenses

• Original Checkpoint: nvidia/stt_pt_fastconformer_hybrid_large_pc
• License: Under NVIDIA NGC Terms of Use / CC-BY-4.0.