---
license: apache-2.0
datasets:
- GTM-UVigo/FalAI
- juanjucm/FLEURS-SpeechT-GL-EN
- juanjucm/OpenHQ-SpeechT-GL-EN
- proxectonos/Nos_Transcrispeech-GL
- proxectonos/Nos_RG-Podcast-GL
language:
- gl
metrics:
- wer
- cer
base_model:
- facebook/w2v-bert-2.0
pipeline_tag: automatic-speech-recognition
---
# w2v-BERT 2.0 (Galician Fine-Tuned, CTC)

This model is a **fine-tuned version of `facebook/w2v-bert-2.0`** for **automatic speech recognition (ASR) in Galician (gl)**, trained using a **CTC objective**.

The model is optimised for Galician speech and evaluated across multiple domains, including read speech, broadcast-style audio and conversational content.

## Training Data

The model was trained on a **combined Galician ASR dataset** built from several public and curated corpora.  
All audio was normalised to **16 kHz**, and all transcripts were standardised to a homogeneous `text` format.

### Datasets Included

- **Common Voice v23 (Galician)**
- **OpenSLR Speech Translation GL-EN** (Galician side)
- **FLEURS GL-EN** (Galician side)
- **FalAI** (20% of validated split)
- **Transcrispeech (Galician)**
- **RG-Podcast (Galician)**

These datasets cover clean read speech, semi-spontaneous speech, and more challenging acoustic conditions.

### Dataset Preparation

- Audio resampled to **16 kHz**
- Removal of empty, corrupt or invalid audio
- Minimum audio duration: **1 second**
- Text normalisation:
  - Lowercasing
  - Unicode normalisation
  - Removal of punctuation
  - Removal of empty transcripts

## Tokenization and Vocabulary

A **character-level CTC vocabulary** was constructed specifically for Galician.

- Supported characters:  
  `abcdefghijklmnopqrstuvwxyzáéíóúñç`
- Word boundaries represented using the `|` token
- Special tokens:
  - `[UNK]`
  - `[PAD]`

The final vocabulary is stored in `vocab.json`.

## Training Procedure

Fine-tuning was performed using the 🤗 Transformers `Trainer` with a **CTC loss**.

- **Base model**: facebook/w2v-bert-2.0  
- **Architecture**: Wav2Vec2BertForCTC  
- **Adapters enabled**: Yes  

### Training Configuration

- **Effective batch size**: 16  
- **Per-device batch size**: 8  
- **Gradient accumulation steps**: 2  
- **Learning rate**: 5e-6  
- **Training epochs**: 5  
- **Warmup ratio**: 0.1  
- **Precision**: FP16  
- **Gradient checkpointing**: Enabled  
- **Max gradient norm**: 1.0  
- **Evaluation & checkpointing**: Every 2000 steps  
- **Checkpoint limit**: 2  

Audio features were extracted using `SeamlessM4TFeatureExtractor`, and text was tokenized with a custom `Wav2Vec2CTCTokenizer`.

## Evaluation Results

Evaluation was performed on held-out splits for each corpus and on a combined test set.  
Metrics are reported as **WER** (Word Error Rate) and **CER** (Character Error Rate).

### Fine-Tuned Model Results

**Per-corpus results**

| Corpus          | N     | WER    | CER    |
|-----------------|-------|--------|--------|
| FalAI           | 4776  | 0.0445 | 0.0099 |
| CommonVoice     | 14563 | 0.0628 | 0.0124 |
| OpenSLR         | 282   | 0.1340 | 0.0406 |
| FLEURS          | 212   | 0.1330 | 0.0447 |
| Transcrispeech  | 1710  | 0.1410 | 0.0481 |
| RG-Podcast      | 2015  | 0.1692 | 0.0654 |

**Combined test set**

| Dataset | N     | WER    | CER    |
|---------|-------|--------|--------|
| TOTAL   | 23558 | 0.1163 | 0.0383 |

## Comparison with Whisper

WER comparison against Whisper-based models evaluated on the same datasets:

| Corpus          | w2v-BERT WER | Whisper WER |
|-----------------|--------------|-------------|
| FalAI           | 0.0445       | 0.0097      |
| CommonVoice     | 0.0628       | 0.0688      |
| OpenSLR         | 0.1340       | 0.0808      |
| FLEURS          | 0.1330       | 0.1980      |
| Transcrispeech  | 0.1410       | 0.2097      |
| RG-Podcast      | 0.1692       | —           |

## Intended Use and Limitations

This model is intended for **Galician ASR research and transcription pipelines**, particularly in CTC-based or streaming-friendly setups.

Performance may degrade on highly spontaneous speech or extremely noisy audio.  
The model is **monolingual (Galician-only)** and not intended for multilingual ASR or speech translation.

## Contact information

For further information, send an email to proxecto.nos@usc.gal

## Licensing information

[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)

## Acknowledgements

This work is funded by the Ministerio para la Transformación Digital y de la Función Pública - Funded by EU – NextGenerationEU within the framework of the project Desarrollo de Modelos ALIA. (Esta publicación del proyecto Desarrollo de Modelos ALIA está financiada por el Ministerio para la Transformación Digital y de la Función Pública y por el Plan de Recuperación, Transformación y Resiliencia – Financiado por la Unión Europea – NextGenerationEU).

Thanks also to Balidea for the technical development of this model.

## Citation
```bib
@misc{proxectenos2026w2v-bert-2.0-gl,
  author       = {{Proxecto Nós}},
  title        = {{w2v-BERT 2.0} (Galician Fine-Tuned, CTC)},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/proxectonos/w2v-bert-2.0-gl/}},
}
```