Update README.md

README.md

@@ -17,198 +17,89 @@ model-index:
       name: WER
 ---
 
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+## ASR+NL Cache-aware Model Overview
 
+Recoganize begin and end of digit sequences and also transcribe
 
+## NVIDIA NeMo: Training
 
-## Model Details
+To train, fine-tune or play with the model you will need to install [NVIDIA NeMo](https://github.com/NVIDIA/NeMo). We recommend you install it after you've installed latest Pytorch version.
+```
+pip install nemo_toolkit['all']
+``` 
 
-### Model Description
+## How to Use this Model
 
-<!-- Provide a longer summary of what this model is. -->
+The model is available for use in the NeMo toolkit [3], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.
 
+### Automatically instantiate the model
 
+```python
+import nemo.collections.asr as nemo_asr
+asr_model = nemo_asr.models.ASRModel.from_pretrained("ksingla025/stt_en_conformer_ctc_caware")
+```
 
-- **Developed by:** [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]
+### Transcribe and tag using Python
+First, let's get a sample
+```
+wget https://www.dropbox.com/s/fmre0xkl3ism62e/audio.zip?dl=0
+unzip audio.zip
+```
+Then simply do:
+```
+asr_model.transcribe(['audio/digits1.wav'])
+```
 
-### Model Sources [optional]
+### Transcribing many audio files
 
-<!-- Provide the basic links for the model. -->
+```shell
+python [NEMO_GIT_FOLDER]/examples/asr/transcribe_speech.py  pretrained_name="ksingla025/stt_en_conformer_ctc_caware"  audio_dir="<DIRECTORY CONTAINING AUDIO FILES>"
+```
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Input
 
-## Uses
+This model accepts 16000 KHz Mono-channel Audio (wav files) as input.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+### Output
 
-### Direct Use
+This model provides transcribed speech as a string for a given audio sample.
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+## Model Architecture
 
-[More Information Needed]
+<ADD SOME INFORMATION ABOUT THE ARCHITECTURE>
 
-### Downstream Use [optional]
+## Training
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+<ADD INFORMATION ABOUT HOW THE MODEL WAS TRAINED - HOW MANY EPOCHS, AMOUNT OF COMPUTE ETC>
 
-[More Information Needed]
+### Datasets
 
-### Out-of-Scope Use
+<LIST THE NAME AND SPLITS OF DATASETS USED TO TRAIN THIS MODEL (ALONG WITH LANGUAGE AND ANY ADDITIONAL INFORMATION)>
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+## Performance
 
-[More Information Needed]
+<LIST THE SCORES OF THE MODEL - 
+      OR
+USE THE Hugging Face Evaluate LiBRARY TO UPLOAD METRICS>
 
-## Bias, Risks, and Limitations
+## Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+<DECLARE ANY POTENTIAL LIMITATIONS OF THE MODEL>
 
-[More Information Needed]
+Eg: 
+Since this model was trained on publically available speech datasets, the performance of this model might degrade for speech which includes technical terms, or vernacular that the model has not been trained on. The model might also perform worse for accented speech.
 
-### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+## References
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+<ADD ANY REFERENCES HERE AS NEEDED>
 
-## How to Get Started with the Model
+[1] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)
 
-Use the code below to get started with the model.
 
-[More Information Needed]
 
-## 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. -->
-
-[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. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **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]
-
-<!-- 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
-
-<!-- This should link to a Data Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## 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]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## 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. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]