Datasets:
File size: 2,671 Bytes
060e815 babc033 060e815 babc033 6085ca5 060e815 6085ca5 7370584 6085ca5 7370584 6085ca5 6788366 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
---
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.
|