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--- |
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datasets: |
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- voidful/NMSQA |
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language: |
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- en |
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metrics: |
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- wer |
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pipeline_tag: automatic-speech-recognition |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model was pretrained using facebook/hubert-base-ls960 model on NMSQA dataset. The task is Automatic Speech Recognition (ASR) in which the questions and context sentences are used. |
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This is a checkpoint with WER 14.36 on dev set. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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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. |
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The input audios are both from context and questions. This ASR model was trained on using training and dev set of NMSQA. |
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- **Developed by:** Merve Menevse |
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- **Model type:** Supervised ML |
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- **Language(s) (NLP):** English |
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- **Finetuned from model [optional]:** facebook/wav2vec2-base-960h |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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The model should be used as fine-tuned model for wav2vec2. |
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## How to Get Started with the Model |
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from transformers import AutoModel |
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model = AutoModel.from_pretrained("menevsem/hubert-base-ls960-nmsqa-asr") |
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## Training Details |
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### Training Data |
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<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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The model was trained using voidful/NMSQA train and dev set. |
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## Evaluation |
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For evalaution WER metric is used on dev set. |
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