import whisperx

device = "cuda"  # or "cpu"
compute_type = "float16"

model = whisperx.load_model(
    "inesc-id/WhisperLv3-EP-X",
    device=device,
    compute_type=compute_type,
    language="pt",
    task="transcribe"
)

## Citation


**BibTeX:**

@inproceedings{camoes, title={{CAMÕES: A Comprehensive Automatic Speech Recognition Benchmark for European Portuguese}}, author={Carlos Carvalho, Francisco Teixeira, Catarina Botelho, Anna Pompili, Rubén Solera-Ureña, Sérgio Paulo, Mariana Julião, Thomas Rolland, John Mendonça, Diogo Pereira, Isabel Trancoso, Alberto Abad}, booktitle={Proceedings of the IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)}, year={2025}, }

inesc-id/WhisperLv3-EP-X

Base model: openai/whisper-large-v3
Architecture: Transformer encoder–decoder
Training: Fine-tuned on around 425 hours of Portuguese speech
Task: Transcription ( task=&quot;transcribe&quot; )
Compute type: float16 (recommended)

WhisperX Large-v3 — PRE-ACORDO

This model is a fine-tuned Whisper variant of openai/whisper-large-v3, trained for European Portuguese (EP) (around 425h) automatic speech recognition (ASR). From CAMÕES work. This is the best model for long-form inference. The text is normalized.

🧠 Model Description

🧩 Usage

inesc-id/
WhisperLv3-EP-X

This model is a fine-tuned Whisper variant of `openai/whisper-large-v3`, trained for European Portuguese (EP) (around 425h) automatic speech recognition (ASR). From CAMÕES work. This is the best model for long-form inference. The text is normalized.