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Datasets:
i4ds
/
SPC

Tasks:
Automatic Speech Recognition
Modalities:
Audio
Text
Formats:
parquet
Languages:
German
Size:
10K - 100K
ArXiv:
Libraries:
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Dask
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The dataset viewer is not available for this dataset.
The JWT signature verification failed. Check the signing key and the algorithm.
Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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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

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

Citation

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