fix: README.md

README.md

@@ -1,159 +1,117 @@
 ---
+language:
+- en
 license: mit
+base_model: openai/whisper-small
+tags:
+- generated_from_trainer
+metrics:
+- wer
+model-index:
+- name: whisper-small-singlish-122k
+  result:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    metrics:
+    - name: Wer
+      type: wer
+      value: 9.69
 ---
 
-# Model Card for Model ID
+# Whisper-small-singlish-122k.
 
-<!-- Provide a quick summary of what the model is/does. -->
+This model is a [openai/whisper-small](https://huggingface.co/openai/whisper-small), fine-tuned on a subset (122k samples) of the [National Speech Corpus](https://www.imda.gov.sg/how-we-can-help/national-speech-corpus).
 
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+The following results on the evaluation set (43,788k samples) are reported:
+
+- Loss: 0.171377
+- WER: 9.69
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Developed by:** [jensenlwt](https://huggingface.co/jensenlwt)
+- **Model type:** automatic-speech-recognition
+- **License:** MIT
+- **Finetuned from model:** [openai/whisper-small](https://huggingface.co/openai/whisper-small)
 
 ## 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 is intended as exploration exercise to develop better ASR model for Singapore English (singlish). 
 
-### Direct Use
+The recommended audio usage for testing should be:
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+1. Involves local Singapore slang, dialect, names, and terms etc.
+2. Involves Singaporean accent.
 
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Direct Use
 
-[More Information Needed]
+To use the model in an application, you can make use of `transformers`:
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+- Long form audio
+- Broken Singlish (typically from older generation)
+- Poor quality audio (audio samples are recorded in a controlled environment)
+- Conversation (as the model is not trained on conversation)
 
-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]
 
 ## Training Details
 
 ### Training Data
 
-<!-- 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. -->
-
-#### 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. -->
+The following hyperparameters are used:
+
+- **batch_size**: 128
+- **gradient_accumulation_steps**: 1
+- **learning_rate**: 1e-5
+- **warmup_steps**: 500
+- **max_steps**: 5000
+- **fp16**: true
+- **eval_batch_size**: 32
+- **eval_step**: 500
+- **max_grad_norm**: 1.0
+- **generation_max_length**: 225
+
+#### Training Results
+
+| Steps | Epoch    | Train Loss | Eval Loss | WER                |
+|:-----:|:--------:|:----------:|:---------:|:------------------:|
+| 500   | 0.654450 | 0.7418     | 0.3889    | 17.968250          |
+| 1000  | 1.308901 | 0.2831     | 0.2519    | 11.880948          |
+| 1500  | 1.963351 | 0.1960     | 0.2038    | 9.948440           |
+| 2000  | 2.617801 | 0.1236     | 0.1872    | 9.420248           |
+| 2500  | 3.272251 | 0.0970     | 0.1791    | 8.539280           |
+| 3000  | 3.926702 | 0.0728     | 0.1714    | 8.207827           |
+| 3500  | 4.581152 | 0.0484     | 0.1741    | 8.145801           |
+| 4000  | 5.235602 | 0.0401     | 0.1773    | 8.138047           |
 
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
 
-<!-- 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. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
 ### Results
 
-[More Information Needed]
+| Model                        | WER   |
+|------------------------------|-------|
+| fine-tuned-122k-whisper-small| 9.69% |
 
 #### 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]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
 
 ### Compute Infrastructure
 
@@ -161,30 +119,6 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 #### 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]