README.md

---

license: cc-by-4.0
task_categories:
- automatic-speech-recognition
language:
- fo
pretty_name: FPSC
size_categories:
- 10K<n<100K
---


# FPSC — Faroese Parliament Speech Corpus

FPSC is a large-scale Faroese parliamentary speech corpus constructed from publicly available recordings from *Løgtingið*, the Parliament of the Faroe Islands.

The dataset contains approximately:

- 1,600 hours of speech
- 89,000+ parliamentary speeches
- 368 parliamentary meetings
- 75 unique speakers
- speaker demographic metadata
- dialect metadata
- machine-generated weak transcripts
- ROVER voting metadata
- audio aligned at speech level

The corpus was created as part of the paper:

> **FPSC: A Sustainable Pipeline for Building a Faroese Parliamentary Speech Corpus**  
> Dávid í Lág, Barbara Scalvini, Carlos Mena, Jón Guðnason  
> LREC 2026

The dataset represents the first large-scale corpus of natural spoken Faroese and is intended for:

- Automatic Speech Recognition (ASR)
- Low-resource speech technology
- Parliamentary speech analysis
- Sociolinguistic research
- Dialect research
- Weakly supervised ASR training
- Continual pretraining
- Multilingual transfer learning

---

# Dataset Structure

The dataset follows the Hugging Face `Audio` dataset format and contains one row per parliamentary speech segment.

Each entry contains:

- segmented WAV audio
- machine-generated transcript
- parliamentary metadata
- speaker metadata
- dialect metadata
- ROVER voting information
- ASR ensemble metadata

---

# Features

## Audio and Speech Fields

| Field | Description |
|---|---|
| `audio` | Hugging Face `Audio` object containing the speech segment |
| `audio_id` | Unique WAV filename for the speech segment |
| `duration` | Audio duration in seconds |
| `length` | Original segment length in seconds |
| `audio_format` | Audio format (WAV) |
| `sampling_rate` | Audio sample rate (16 kHz mono) |

---

## Transcript Fields

| Field | Description |
|---|---|
| `text` | Final machine-generated transcript selected through ROVER voting |
| `normalized_text` | Normalized version of the transcript for ASR training |
| `winner_text` | Raw winning transcript from the ROVER voting process |
| `language` | Spoken language of the segment (Faroese) |

---

## Speaker Metadata

| Field | Description |
|---|---|
| `speaker_id` | Internal speaker identifier |
| `mp_id` | Parliament member identifier |
| `name` | Speaker name |
| `gender` | Speaker gender |
| `age` | Speaker age |
| `age_group` | Speaker age group |
| `date_of_birth` | Speaker date of birth |
| `city` | Speaker home city |
| `dialect` | Dialect region |
| `political_party_affiliation` | Political party affiliation |
| `mp_url` | URL to parliament member profile |

---

## Parliamentary Metadata

| Field | Description |
|---|---|
| `id` | Unique speech segment ID |
| `meeting_id` | Parliamentary meeting identifier |
| `url` | URL to the original parliamentary meeting |
| `date` | Meeting date |
| `time` | Speech start time |
| `second` | Start offset in seconds within the meeting |
| `topic` | Parliamentary agenda topic |
| `contribution_type` | Type of contribution (speech, remark, chair, etc.) |
| `location` | Recording location |

---

## ROVER Voting Metadata

The final transcripts were generated using an ensemble of four Faroese-adapted ASR systems combined through ROVER voting.

| Field | Description |
|---|---|
| `rover_vote_type` | Type of ROVER voting decision |
| `confidence` | Confidence score assigned by the voting system |
| `winner_model_id` | Full Hugging Face model ID of the winning ASR model |
| `winner_model_short` | Short name of the winning ASR model |
| `costs` | Full ROVER voting cost dictionary stored as JSON |
| `cost_wav2vec2_fo_cpt` | Voting cost for Wav2Vec2-FO-CPT |
| `cost_whisper_fo` | Voting cost for Whisper-FO |
| `cost_wav2vec2_fo` | Voting cost for Wav2Vec2-FO |
| `cost_whisper_no_is_fo` | Voting cost for Whisper-NO/IS/FO |

---

# ASR Models Used for Weak Transcription

The corpus transcripts were generated using four Faroese-adapted ASR systems:

| Model | Description |
|---|---|
| `Wav2Vec2-FO-CPT` | Continually pretrained Wav2Vec2 XLS-R model adapted on Faroese parliamentary speech |
| `Wav2Vec2-FO` | Faroese fine-tuned Wav2Vec2 XLS-R model |
| `Whisper-FO` | Whisper Large model fine-tuned on Faroese speech |
| `Whisper-NO/IS/FO` | Multilingual Whisper model fine-tuned on Norwegian, Icelandic, and Faroese |

The final transcript for each segment was selected using weighted ROVER voting based on ASR model performance.

---

# Transcript Quality Notice

The transcripts in FPSC are automatically generated and should be treated as **weakly supervised labels**, not manually verified gold-standard transcriptions.

Although the corpus was generated using multiple ASR systems and ROVER consensus voting, transcription errors remain present, especially for:

- overlapping speech
- dialect variation
- named entities
- interruptions
- spontaneous parliamentary speech

The dataset is therefore most suitable for:

- weakly supervised ASR training
- continual pretraining
- large-scale speech modeling
- speech representation learning
- sociolinguistic analysis

---

# Loading the Dataset

```python

from datasets import load_dataset, Audio



ds = load_dataset("davidilag/FPSC")

ds = ds.cast_column("audio", Audio(sampling_rate=16000))



print(ds)

print(ds["train"][0])

```

---

# Citation

If you use FPSC in your research, please cite:

```bibtex

@inproceedings{iLag2026FPSC,

  author = {Dávid í Lág and Barbara Scalvini and Carlos Mena and Jón Guðnason},

  title = {{FPSC}: A Sustainable Pipeline for Building a Faroese Parliamentary Speech Corpus},

  booktitle = {Proceedings of the Language Resources and Evaluation Conference (LREC 2026)},

  year = {2026},

  address = {Palma de Mallorca, Spain},

  publisher = {European Language Resources Association (ELRA)},

  institution = {University of the Faroe Islands},

  keywords = {Faroese, Parliamentary Speech, Automatic Speech Recognition, Weakly-Supervised Transcription, Whisper, Wav2Vec2},

  abstract = {We present FPSC, a 1,600-hour Faroese parliamentary speech corpus comprising approximately 89,000 speeches enriched with detailed speaker and linguistic metadata. The corpus was constructed using a sustainable ASR-assisted pipeline combining speech segmentation, multiple Faroese-adapted ASR systems, and ROVER-based consensus voting for weakly supervised transcription. FPSC represents the first large-scale corpus of natural spoken Faroese and provides an open resource for future research in automatic speech recognition and low-resource language technology.}

}

```

---

# Repository and Scripts

Processing scripts and pipeline implementation:

- GitHub repository: https://github.com/davidilag/LREC2026

---

# Original Data Source

The original parliamentary recordings and metadata were collected from the official website of *Løgtingið*, the Parliament of the Faroe Islands:

https://www.logting.fo/mal/yvirlit/gerdabokur/

The website provides publicly accessible:

- parliamentary meeting recordings
- agendas and meeting protocols
- speaker order and timestamps
- meeting dates and metadata
- parliament member information

FPSC was constructed by automatically downloading, processing, segmenting, and transcribing these publicly available parliamentary sessions into a structured speech corpus suitable for ASR and language technology research.

---

# License

This dataset is released under the CC BY 4.0 license.

The recordings originate from publicly available parliamentary broadcasts from *Løgtingið*, the Parliament of the Faroe Islands.

---

# Contact

**Dávid í Lág**  
University of the Faroe Islands  
MSc. Computer Science, Ph.D. student in Computer Science (2024-2028)  
Research area: Automatic Speech Recognition for Low-Resource Languages