# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("birgermoell/psst-fairseq-rir")
model = AutoModelForCTC.from_pretrained("birgermoell/psst-fairseq-rir", device_map="auto")Quick Links
This model is trained on the PSST Challenge data, with a subset of TIMIT that was augmented using Room Impulse Response (RIR). A file containing the list of TIMIT IDs is in the repository (timit-ids.txt)
The model was finetuned on Wav2vec 2.0 Base, No finetuning, and the results on the validation set were PER: 21.8%, FER: 9.6%.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="birgermoell/psst-fairseq-rir")