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--- |
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dataset_info: |
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features: |
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- name: audio |
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dtype: |
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audio: |
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sampling_rate: 16000 |
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- name: text |
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dtype: string |
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- name: language |
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dtype: string |
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- name: prompt |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 2240165860.82 |
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num_examples: 24607 |
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download_size: 2213674221 |
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dataset_size: 2240165860.82 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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task_categories: |
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- automatic-speech-recognition |
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language: |
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- de |
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pretty_name: S |
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--- |
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# Dataset Card: Swiss Parliaments Corpus — Train v0.9 |
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## Summary |
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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. |
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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. |
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--- |
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## Dataset Details |
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### Generation Pipeline |
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The corpus was created with [`i4Ds/whisper‑prep`](https://github.com/i4Ds/whisper-prep) using the following configuration: |
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```yaml |
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# Generation configuration |
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maintain_speaker_chance: 0.50 # Probability of keeping the current speaker for consecutive utterances |
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n_samples_per_srt: 120 # Number of audio fragments merged into each SRT file |
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normalize_text: true # Clean text according to rules in whisper_prep/generation/text_normalizer.py |
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# Overlap settings |
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# Overlaps are inserted only in non‑speech regions identified by VAD. |
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overlap_chance: 0.80 # Probability of creating an overlap between consecutive clips |
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max_overlap_chance: 0.50 # If an overlap occurs, probability of using the maximum duration |
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max_overlap_duration: 0.30 # Maximum overlap length in seconds |
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``` |
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### Maintainer |
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* **Curated by:** [Vincenzo Timmel](mailto:vincenzo.timmel@fhnw.ch) (@vincenzo.timmel) |
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--- |
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## Intended Use & Scope |
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* **Primary use‑case:** Fine‑tuning multilingual ASR or speech‑translation models, particularly OpenAI Whisper. |
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* **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) |
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--- |
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## Dataset Sources |
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* **Related papers:** [“Swiss Parliaments Corpus”](https://arxiv.org/pdf/2010.02810), ["Fine-tuning Whisper on Low-Resource Languages"](https://arxiv.org/abs/2412.15726) |
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--- |
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## Citation |
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If you use this corpus, please cite the papers above and acknowledge **I4DS FHNW** for data preparation. |
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