Automatic Speech Recognition
Transformers
Safetensors
English
fela-asr-ctc
feature-extraction
fela
fourier-neural-operator
fno
cpu
on-device
streaming
ctc
constant-memory
custom_code
Instructions to use lowdown-labs/fela-streaming-asr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lowdown-labs/fela-streaming-asr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="lowdown-labs/fela-streaming-asr", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lowdown-labs/fela-streaming-asr", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "fela-asr-ctc", | |
| "architectures": [ | |
| "FelaAsrModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_asr.FelaAsrConfig", | |
| "AutoModel": "modeling_asr.FelaAsrModel" | |
| }, | |
| "architecture": "FELA", | |
| "edition": "streaming 64M", | |
| "n_layer": 16, | |
| "n_embd": 512, | |
| "n_head": 8, | |
| "fno_modes": 256, | |
| "gla_chunk": 64, | |
| "ffn_hidden": 2048, | |
| "layer_pattern": "FNO", | |
| "dropout": 0.0, | |
| "bias": false, | |
| "gla_delta": false, | |
| "n_mels": 80, | |
| "sample_rate": 16000, | |
| "n_fft": 400, | |
| "win_length": 400, | |
| "hop_length": 160, | |
| "subsample_factor": 4, | |
| "vocab": 257, | |
| "bpe_units": 256, | |
| "ctc_blank_id": 0, | |
| "decode": "greedy ctc", | |
| "language_model": "none", | |
| "tokenizer": "bpe256.model (SentencePiece, 256 units + 1 CTC blank)", | |
| "note": "The 64.4M streaming model (test clean WER 16.77 at 460h). The 206M byte CTC model (test clean 14.3 / test other 27.4 at 960h) shares the architecture with a larger width/depth." | |
| } | |