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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All Bambara ASR Dataset

This is the dataset that fueled our early ASR experiments that gave as results the V0 models. It is primarily composed of the Jeli-ASR dataset (available at RobotsMali/jeli-asr), along with the Mali-Pense data curated and published by Aboubacar Ouattara (available at oza75/bambara-tts). Additionally, it includes 1 hour of audio recently collected by the RobotsMali AI4D Lab, featuring children's voices reading some of RobotsMali GAIFE books. This dataset is designed for automatic speech recognition (ASR) task primarily.

Important Notes

Please note that this dataset is currently in development and is therefore not fixed. The structure, content, and availability of the dataset may change as improvements and updates are made.

Key Changes in Version 1.0.1 (December 17th)

This version extends the same updates as Jeli-ASR 1.0.1 at the transcription level. The transcription were normalized using the Bambara Normalizer, a python package designed to normalize Bambara text for different NLP applications.

Please, let us know if you have feedback or additional use suggestions for the dataset by opening a discussion or a pull request. You can find a record or updates of the dataset in VERSIONING.md

Dataset Details

Total Duration: 37.41 hours
Number of Samples: 38,769
- Training Set: 37,306 samples
- Testing Set: 1,463 samples

Subsets:

Oza's Bambara-ASR: ~29 hours (clean subset).
Jeli-ASR-RMAI: ~3.5 hours (filtered subset).
oza-tts-mali-pense: ~4 hours
reading-tutor-data-collection: ~1 hour

Usage

The data in the main branch are in .arrow format for compatibility with HF's Datasets Library. So you don't need any ajustement to load the dataset directly with datasets:

from datasets import load_dataset

# Load the dataset into Hugging Face Dataset object
dataset = load_dataset("RobotsMali/bam-asr-all")

However, an "archives" branch has been added for improved versioning of the dataset and to facilitate usage for those working outside the typical Hugging Face workflow. Precisely the archives are created from the directory of version 1.0.0 tailored for usage with NVIDIA's NEMO. If you prefer to reconstrcut the dataset from archives you can follow the instructions below.

Downloading the Dataset:

You could download the dataset by git cloning this branch:

# Clone dataset repository maintaining directory structure for quick setup with Nemo
git clone --depth 1 -b archives https://huggingface.co/datasets/RobotsMali/bam-asr-all

Or you could download the individual archives that you are interested in, thus avoiding the git overload

# Download the audios with wget
wget https://huggingface.co/datasets/RobotsMali/bam-asr-all/resolve/archives/audio-archives/bam-asr-all-1.0.0-audios.tar.gz
# Download the manifests in the same way
wget https://huggingface.co/datasets/RobotsMali/bam-asr-all/resolve/archives/manifests-archives/bam-asr-all-1.0.1-manifests.tar.gz

Finally, untar those files to reconstruct the default Directory structure of jeli-asr 1.0.0:

# untar the audios
tar -xvzf bam-asr-all-1.0.0-audios.tar.gz
# untar the manifests
tar -xvzf bam-asr-all-1.0.1-manifests.tar.gz

This approach allow you to combine the data from different versions and restructure your working directory as you with, with more ease and without necessarily having to write code.

Known Issues

This dataset also has most of the issues of Jeli-ASR, including a few misaligned samples. Additionally a few samples from the mali pense subset and all the data from the rt-data-collection subset don't currently have french translations

Citation

If you use this dataset in your research or project, please credit the creators of these datasets.

Jeli-ASR dataset: Jeli-ASR Dataset.
Oza's Bambara-ASR dataset: oza75/bambara-asr
Oza's Bambara-TTS dataset: oza75/bambara-tts

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