Instructions to use Neoscopio-SA/Neo_EP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Neoscopio-SA/Neo_EP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Neoscopio-SA/Neo_EP")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Neoscopio-SA/Neo_EP") model = AutoModelForSpeechSeq2Seq.from_pretrained("Neoscopio-SA/Neo_EP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,370 Bytes
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license: apache-2.0
language:
- pt
base_model:
- openai/whisper-large-v3
library_name: transformers
tags:
- pt_PT
---
# Neo_EP
Fine-tuned version of [`inesc-id/WhisperLv3-FT`](https://huggingface.co/inesc-id/WhisperLv3-FT) for **European Portuguese** automatic speech recognition, developed by [Neoscopio](https://huggingface.co/Neoscopio-SA).
- **Architecture:** Transformer encoder-decoder (1550M parameters)
- **Base model:** [`inesc-id/WhisperLv3-FT`](https://huggingface.co/inesc-id/WhisperLv3-FT) (from [`openai/whisper-large-v3`](https://huggingface.co/openai/whisper-large-v3))
- **Language:** European Portuguese (`pt`)
- **Task:** Transcription
- **Compute type:** float16
> **Note:** A paper with full training methodology, evaluation results, and benchmarks is currently under preparation and will be published soon.
**Current results:**
| # | Modelo | WER (%) | CER (%) | RTF | Tempo (s) | eurospeech | falabracarense | MLS |
|---| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| 1 | Neo_EP | **13.67%** | 10.16% | 0.000 | 1208.3s | **28.1%** | **7.1%** | 5.8% |
| 2 | EP-X(Faster-Whisper) | 18.93% | 14.56% | 0.000 | 1259.1s | 42.5% | 7.6% | 6.6% |
| 3 | whisper-large-v3 | 26.99% | 19.57% | 0.000 | 1202.9s | 41.8% | 33.9% | **5.2%** |
| 4 | Nvidia-Canary-1b-v2 | 32.07% | 22.06% | 0.000 | 2296.4s | 45.6% | 43.5% | 7.1% | |