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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YouTube ASR Caption Dataset (Cantonese)

This dataset was built from YouTube videos with manually provided captions in Cantonese. We used SenseVoice to re-transcribe the audio and filtered segments to build a high-quality collection of audio-caption pairs.

What’s included

Segments where the ASR output is identical to the original caption — likely clean.
Segments where differences are only homophones (同音字) or English words — likely ASR mistakes.

This combination supports both:

ASR error analysis and correction
Training clean speech-to-text models

Dataset Info

Total duration: ~35 hours
Sampling rate: 16 kHz
Audio format: .mp3
Unfiltered data: available under creator/{video_id}/*.mp3

Features

Name	Type
`id`	`string`
`caption`	`string`
`start`, `end`	`float64` (seconds in original audio)
`sensevoice_caption`	`string`
`sensevoice_words`	list of { `word`, `start`, `duration` }
`audio`	`Audio(sampling_rate=16000)`
`uploader_id`	`string`
`video_id`	`string`

Splits

Split	# Examples	Size
Train	56,484	~390 MB
Test	1,000	~7 MB

Usage

Install the 🤗 Datasets library:

pip install datasets

Load the dataset:

from datasets import load_dataset

dataset = load_dataset("ming030890/youtube_caption_yue")
train_data = dataset["train"]
print(train_data[0])

Play audio (in notebooks):

from IPython.display import Audio
example = train_data[0]
Audio(example["audio"]["array"], rate=example["audio"]["sampling_rate"])

License

Free for research use. Check original YouTube licenses before reuse or redistribution.

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Total file size:

146 GB