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This model was pretrained using Facebook-base-960h model on NMSQA dataset. The task is Automatic Speech Recognition (ASR) in which the questions and context sentences are used.
This is a checkpoint with WER 10.58 on dev set.
Model Details
Model Description
The input of the models are from NMSQA dataset. The task of the dataset is Spoken QA, but in this model I used the sentences for ASR. The input audios are both from context and questions. This ASR model was trained on using training and dev set of NMSQA.
- Developed by: Merve Menevse
- Model type: Supervised ML
- Language(s) (NLP): English
- Finetuned from model [optional]: facebook/wav2vec2-base-960h
Uses
The model should be used as fine-tuned model for wav2vec2.
How to Get Started with the Model
from transformers import AutoModel
model = AutoModel.from_pretrained("menevsem/wav2vec2-base-960h-nmsqa-asr")
Training Details
Training Data
The model was trained using voidful/NMSQA train and dev set.
Evaluation
For evalaution WER metric is used on dev set.
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