Datasets:
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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 failedNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Speech DAC Tokens 16kHz (2 Codebooks)
Pre-tokenized speech dataset using DAC at 16kHz with 2 codebooks. Optimized for speech TTS training — 16kHz captures the full speech frequency range without wasting capacity on inaudible frequencies.
Why 16kHz?
- Speech lives below 8kHz — 16kHz sample rate is sufficient (Nyquist)
- 50 tokens/sec per codebook vs 87 at 44kHz — shorter sequences, faster training
- 2 codebooks at 16kHz produce intelligible speech — verified by listening tests
- No resampling needed — LibriSpeech is natively 16kHz
Dataset Summary
| Stat | Value |
|---|---|
| Total samples | 132,479 |
| Total audio | ~464 hours |
| Source | LibriSpeech clean-100 + clean-360 |
| Language | English |
| DAC model | 16kHz, 2 of 12 codebooks |
| Codebook size | 1,024 entries each |
| Tokens per second | 100 (50/codebook x 2) |
| Token sequence length | 149-2,047 (mean: 1,327) |
Format
| Column | Type | Description |
|---|---|---|
text |
string | Original text transcription |
prompt |
string | {text}<|audio_start|><|c1_X|><|c2_Y|>...<|audio_end|> |
input_ids |
list[int] | Pre-tokenized with Qwen3-0.6B + 2cb DAC tokens |
attention_mask |
list[int] | All 1s |
labels |
list[int] | Copy of input_ids |
n_audio_frames |
int | Number of DAC time frames |
n_tokens |
int | Total token count |
Audio tokens interleaved: c1, c2, c1, c2, ... per frame.
Related
- Training code: treadon/ri-tts on GitHub
- 44kHz dataset (3cb): treadon/speech-dac-tokens-3cb (241K samples, kept for reference)
Citation
@inproceedings{panayotov2015librispeech,
title={Librispeech: an ASR corpus based on public domain audio books},
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={ICASSP},
year={2015}
}
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