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Datasets:
davidilag
/
FPSC

Tasks:
Automatic Speech Recognition
Modalities:
Audio
Text
Formats:
soundfolder
Languages:
Faroese
Size:
10K - 100K
Libraries:
Datasets
License:
Dataset card Data Studio Files
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  1. README.md +61 -0
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README.md ADDED
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+ ---
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+ license: cc-by-4.0
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+ task_categories:
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+ - automatic-speech-recognition
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+ language:
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+ - fo
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+ pretty_name: FPSC-ASR
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+ size_categories:
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+ - 10K<n<100K
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+ ---
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+
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+ # FPSC-ASR
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+
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+ FPSC-ASR is a Faroese parliamentary ASR dataset based on speech recordings from Løgtingið, the Faroese Parliament.
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+
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+ The dataset contains WAV audio files, parliamentary metadata, and machine-generated transcripts selected by ROVER-style system voting.
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+
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+ ## Format
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+
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+ The dataset follows a standard Hugging Face audio dataset format using `AudioFolder`.
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+
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+ Main ASR columns:
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+
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+ - `audio_id`: original WAV filename
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+ - `audio`: audio column created by Hugging Face when loading the dataset
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+ - `speaker_id`: speaker identifier, mapped from `mp_id`
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+ - `gender`: speaker gender metadata
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+ - `age`: age group, mapped from `age_group`
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+ - `duration`: audio duration in seconds, mapped from `length`
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+ - `normalized_text`: machine-generated transcript, mapped from `winner_text`
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+ - `dialect`: speaker dialect metadata
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+ - `winner_text`: original winning ROVER transcript
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+ - `text`: same as `winner_text`, included for ASR training convenience
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+
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+ ROVER voting columns:
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+
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+ - `confidence`: ROVER voting confidence
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+ - `winner_model_id`: full Hugging Face model id of the winning model
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+ - `winner_model_short`: short model name of the winning model
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+ - `costs`: full ROVER costs dictionary stored as a JSON string
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+ - `cost_wav2vec2_fo_cpt`: flattened cost for Wav2Vec2-FO-CPT
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+ - `cost_whisper_fo`: flattened cost for Whisper-FO
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+ - `cost_wav2vec2_fo`: flattened cost for Wav2Vec2-FO
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+ - `cost_whisper_no_is_fo`: flattened cost for Whisper-NO/IS/FO
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+
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+ All original parliamentary metadata fields are also included.
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+
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+ ## Transcript note
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+
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+ The transcripts are machine-generated and should be treated as weak labels, not manually verified gold-standard transcripts.
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+
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+ ## Loading
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+
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+ ```python
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+ from datasets import load_dataset, Audio
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+
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+ ds = load_dataset("davidilag/FPSC-ASR")
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+ ds = ds.cast_column("audio", Audio(sampling_rate=16000))
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+
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+ print(ds)
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+ print(ds["train"][0])
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