stt-bm-quartznet15x5-v0 is a fine-tuned version of NVIDIA’s stt_fr_quartznet15x5 trained for Automatic Speech Recognition of Bambara speech. This model cannot write Punctuations and Capitalizations, it utilizes a character encoding scheme, and transcribes text in the standard character set that is provided in the training set of bam-asr-early dataset.
The model was fine-tuned using NVIDIA NeMo and is trained with CTC (Connectionist Temporal Classification) Loss.
🚨 Important Note
This model, along with its associated resources, is part of an ongoing research effort, improvements and refinements are expected in future versions. Users should be aware that:
The model may not generalize very well accross all speaking conditions and dialects.
Community feedback is welcome, and contributions are encouraged to refine the model further.
NVIDIA NeMo: Training
To fine-tune or use the model, install NVIDIA NeMo. We recommend installing it after setting up the latest PyTorch version.
pip install nemo-toolkit['asr']
How to Use This Model
Load Model with NeMo
python
1import nemo.collections.asr as nemo_asr
2asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name="RobotsMali/stt-bm-quartznet15x5-v0")
Transcribe Audio
python
1# Assuming you have a test audio file named sample_audio.wav2asr_model.transcribe(['sample_audio.wav'])
Input
This model accepts 16 kHz mono-channel audio (wav files) as input. But it is equipped with its own preprocessor doing the resampling so you may input audios at higher sampling rates.
Output
This model provides transcribed speech as an hypothesis object with a text attribute containing the transcription string for a given speech sample.
Model Architecture
QuartzNet is a convolutional architecture, which consists of 1D time-channel separable convolutions optimized for speech recognition. More information on QuartzNet can be found here: QuartzNet Model.
Training
The NeMo toolkit was used to fine-tune this model for 25939 steps over the stt_fr_quartznet15x5 model. The finetuning codes and configurations can be found at RobotsMali-AI/bambara-asr.
Dataset
This model was fine-tuned on the bam-asr-early dataset, which consists of 37 hours of transcribed Bambara speech data. The dataset is primarily derived from Jeli-ASR dataset (~87%).
Performance
The performance of Automatic Speech Recognition models is measured using Word Error Rate (WER%) and Character Error Rate (CER), two edit distance metrics .
Benchmark
Decoding
WER (%) ↓
CER (%) ↓
Bam ASR Early
CTC
46.66
21.65
Nyana Eval
CTC
65.42
30.66
These are greedy WER numbers without external LM.
License
This model is released under the CC-BY-4.0 license. By using this model, you agree to the terms of the license.
Feel free to open a discussion on Hugging Face or file an issue on GitHub for help or contributions.
