---
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`](https://github.com/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`](https://github.com/i4Ds/whisper-prep) using the following configuration:

```yaml
# 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](mailto:vincenzo.timmel@fhnw.ch) (@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"](https://huggingface.co/datasets/i4ds/SPC_test)

---

## Dataset Sources

* **Related papers:** [“Swiss Parliaments Corpus”](https://arxiv.org/pdf/2010.02810), ["Fine-tuning Whisper on Low-Resource Languages"](https://arxiv.org/abs/2412.15726)

---

## Citation

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