# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

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

<!-- Provide a longer summary of what this model is. -->

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

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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

<!-- 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. -->

 The model was trained using voidful/NMSQA train and dev set. 

## Evaluation

For evalaution WER metric is used on dev set. 

{"language": ["en"], "datasets": ["voidful/NMSQA"], "metrics": ["wer"], "pipeline_tag": "automatic-speech-recognition"}
transformers pytorch wav2vec2 feature-extraction automatic-speech-recognition en dataset:voidful/NMSQA endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #feature-extraction #automatic-speech-recognition #en #dataset-voidful/NMSQA #endpoints_compatible #region-us 

# Model Card for Model ID



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. 

# Model Card for Model ID



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.
TAGS
#transformers #pytorch #wav2vec2 #feature-extraction #automatic-speech-recognition #en #dataset-voidful/NMSQA #endpoints_compatible #region-us 
 # Model Card for Model ID



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.
TAGS
#transformers #pytorch #wav2vec2 #feature-extraction #automatic-speech-recognition #en #dataset-voidful/NMSQA #endpoints_compatible #region-us 
# Model Card for Model ID



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.