Datasets:

i4ds
/

SPC

dataset_info:
  features:
    - name: audio
      dtype:
        audio:
          sampling_rate: 16000
    - name: text
      dtype: string
    - name: language
      dtype: string
    - name: prompt
      dtype: string
  splits:
    - name: train
      num_bytes: 2240165860.82
      num_examples: 24607
  download_size: 2213674221
  dataset_size: 2240165860.82
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
task_categories:
  - automatic-speech-recognition
language:
  - de
pretty_name: S

Dataset Card: Swiss Parliaments Corpus — Train v0.9

Summary

The SPC Train v0.9 release pairs Swiss German speech with Standard German transcriptions, providing a high‑quality resource for training and evaluating automatic speech‑recognition (ASR) or speech‑translation systems. If you intend to fine‑tune Whisper, we recommend the companion project i4Ds/whisper‑finetune, which is fully compatible with the data structure produced here.

Dataset Details

Generation Pipeline

The corpus was created with i4Ds/whisper‑prep using the following configuration:

# Generation configuration
maintain_speaker_chance: 0.50  # Probability of keeping the current speaker for consecutive utterances
n_samples_per_srt: 120         # Number of audio fragments merged into each SRT file
normalize_text: true           # Clean text according to rules in whisper_prep/generation/text_normalizer.py

# Overlap settings
# Overlaps are inserted only in non‑speech regions identified by VAD.
overlap_chance: 0.80           # Probability of creating an overlap between consecutive clips
max_overlap_chance: 0.50       # If an overlap occurs, probability of using the maximum duration
max_overlap_duration: 0.30     # Maximum overlap length in seconds

Maintainer

Curated by: Vincenzo Timmel (@vincenzo.timmel)

Intended Use & Scope

Primary use‑case: Fine‑tuning multilingual ASR or speech‑translation models, particularly OpenAI Whisper.
Not suitable for: Language‑identification or emotion‑recognition tasks without additional annotation. For evaluation, please see "SPC_Test"

Dataset Sources

Related papers: “Swiss Parliaments Corpus”, "Fine-tuning Whisper on Low-Resource Languages"

Citation

If you use this corpus, please cite the papers above and acknowledge I4DS FHNW for data preparation.