Automatic Speech Recognition
NeMo
PyTorch
Bambara
speech
audio
CTC
QuartzNet
Bambara
NeMo
Eval Results (legacy)
Instructions to use RobotsMali/stt-bm-quartznet15x5-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use RobotsMali/stt-bm-quartznet15x5-v0 with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("RobotsMali/stt-bm-quartznet15x5-v0") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1ff0a2835393702f2d17feb13da2941c662616daf87c35870a7820cd9bc5671e
- Size of remote file:
- 76.4 MB
- SHA256:
- 6ed228132a92c5bf804011a9a443b23fcc8f751b859859ace501063b2aad8737
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