model card

README.md

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 ---
 library_name: transformers
-tags: []
+tags:
+- child_speech
+- classroom_speech
+- asr
+base_model:
+- openai/whisper-large-v2
+pipeline_tag: automatic-speech-recognition
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for whisat
+ASR model tuned for child speech in the classroom on public corpora of children's speech
+Research conducted as part of NSF-ISAT
 
 ## Model Details
-
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+K-12 school classrooms have proven to be a challenging environment for Automatic Speech Recognition (ASR) systems, both due to background noise and conversation, and differences in linguistic and acoustic properties from adult speech, on which the majority of ASR systems are trained and evaluated. We report on experiments to improve ASR for child speech in the classroom by training and fine-tuning transformer models on public corpora of adult and child speech augmented with classroom background noise. By tuning OpenAI’s Whisper model we achieve a 38% relative reduction in word error rate (WER) to 9.2% on the public MyST dataset of child speech – the lowest yet reported – and a 7% relative reduction to reach 54% WER on a more challenging classroom speech dataset (ISAT). We also introduce a novel beam hypothesis rescoring method that incorporates a speed-aware term to capture prior knowledge of human speaking rates, as well as a Large Language Model, to select among hypotheses. We demonstrate the effectiveness of this technique on both publicly-available datasets and a classroom speech dataset.
 
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Finetuned from model [optional]:** openai/whisper-large-v2
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## 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. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-[More Information Needed]
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-[More Information Needed]
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-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
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-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+- **Paper:** [Automatic Speech Recognition Tuned for Child Speech in the Classroom](https://ieeexplore.ieee.org/document/10447428)
 
 ## Training Details
 
 ### Training Data
+See the paper and supplied data_manifest.csv for details.
 
-<!-- This should link to a Dataset 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. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
-
-[More Information Needed]
-
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
+Utterances sourced from:
+MyST
+CuKids
+CSLU
 
-<!-- This should link to a Dataset Card if possible. -->
 
-[More Information Needed]
 
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
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-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Citation
 
+R. Southwell et al., "Automatic Speech Recognition Tuned for Child Speech in the Classroom," ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Korea, Republic of, 2024, pp. 12291-12295, doi: 10.1109/ICASSP48485.2024.10447428.
 **BibTeX:**
 
-[More Information Needed]
-
-**APA:**
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-[More Information Needed]
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-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
-
-## Model Card Contact
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-[More Information Needed]
+@INPROCEEDINGS{10447428,
+  author={Southwell, Rosy and Ward, Wayne and Trinh, Viet Anh and Clevenger, Charis and Clevenger, Clay and Watts, Emily and Reitman, Jason and D’Mello, Sidney and Whitehill, Jacob},
+  booktitle={ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
+  title={Automatic Speech Recognition Tuned for Child Speech in the Classroom}, 
+  year={2024},
+  volume={},
+  number={},
+  pages={12291-12295},
+  keywords={Training;Oral communication;Signal processing;Linguistics;Transformers;Acoustics;Background noise;Automatic Speech Recognition;Child Speech;Language Modeling;Transfer Learning;Transformers},
+  doi={10.1109/ICASSP48485.2024.10447428}}