Instructions to use HuggingAnalist/mms-1b-asr-lin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingAnalist/mms-1b-asr-lin with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="HuggingAnalist/mms-1b-asr-lin")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("HuggingAnalist/mms-1b-asr-lin") model = AutoModelForCTC.from_pretrained("HuggingAnalist/mms-1b-asr-lin") - Notebooks
- Google Colab
- Kaggle
| language: lin | |
| library_name: transformers | |
| pipeline_tag: automatic-speech-recognition | |
| tags: | |
| - mms | |
| - wav2vec2 | |
| - ctc | |
| - asr | |
| - waxal | |
| - lingala | |
| # MMS-300M — Lingala ASR | |
| `facebook/mms-300m` fine-tuned (character CTC head) on WaxalNLP `lin_asr` for the Waxal ASR challenge. Trained in bf16/fp32 with `ctc_zero_infinity=True`. | |
| `language_model/lm_lin.arpa` is a KenLM 4-gram built from the training transcriptions, for beam-search decoding with pyctcdecode (tune alpha/beta on validation). | |