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
Quickstart
Transcribe a 16 kHz waveform through the streaming CTC path and watch the transcript build up. Memory stays flat over the stream regardless of length.
pip install -r requirements.txt
python run.py --audio sample.wav
What this needs
- A 16 kHz mono waveform. If the file is a different rate or stereo,
run.pyresamples and downmixes it. If no file is present, it runs the path on a short silent buffer and prints the expected input shape. - The streaming checkpoint and the SentencePiece BPE (
bpe256.model, 257 units including the CTC blank).
Status
This wires the current shippable streaming checkpoint (460h, 16.4 WER on LibriSpeech test clean).
The streaming loop feeds the audio in chunks and greedy decodes CTC per chunk (no language model). Long streams use a state reset every so many frames (VAD style) to stay in the training regime. Memory stays bounded either way.