Update README.md

README.md

@@ -6,199 +6,157 @@ tags:
 - asr
 - peft
 - lora
+license: apache-2.0
+datasets:
+- mozilla-foundation/common_voice_13_0
+language:
+- hi
+metrics:
+- wer
+base_model:
+- openai/whisper-small
+pipeline_tag: automatic-speech-recognition
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Whisper Small - Hindi Automatic Speech Recognition Model
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This is a fine-tuned Whisper Small model for Automatic Speech Recognition (ASR) in Hindi, developed using Parameter-Efficient Fine-Tuning (PEFT) with Low-Rank Adaptation (LoRA). The model is designed to transcribe Hindi speech with improved accuracy and efficiency.
 
-- **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]
+- **Developed by:** martin-mwiti
+- **Model type:** Automatic Speech Recognition (ASR)
+- **Language(s):** Hindi
+- **License:** Apache-2.0
+- **Finetuned from model:** openai/whisper-small
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [GitHub/martin-mwiti/AI-Model-Hub/ASR](https://github.com/MartinMwiti/AI-Model-Hub/ASR)
+- **HuggingFace Hub:** [martin-mwiti/whisper-small-hi-lora-r32-alpha64-20241231](https://huggingface.co/martin-mwiti/whisper-small-hi-lora-r32-alpha64-20241231)
 
 ## 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]
+This model can be used for transcribing Hindi speech audio files. It is optimized for automatic speech recognition tasks using the Whisper Small model as a base.
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The model can be further fine-tuned or used as a starting point for other Hindi speech recognition applications.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- Do not use for languages other than Hindi
+- Not suitable for real-time streaming audio transcription
+- Avoid using in high-stakes or safety-critical applications without additional validation
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- Performance may vary depending on audio quality, accent, and background noise
+- Trained on Common Voice dataset, which may not represent all Hindi dialects and speaking styles
+- May have biases present in the training data
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+- Validate model performance on your specific use case
+- Use in conjunction with human review for critical applications
+- Be aware of potential cultural or linguistic biases
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+```python
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+from peft import PeftModel, PeftConfig
 
-[More Information Needed]
+# Load the processor from the base model
+processor = WhisperProcessor.from_pretrained("openai/whisper-small")
 
-## Training Details
+# Load the base Whisper model
+base_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
 
-### Training Data
+# Load the adapter configuration and model
+adapter_config = PeftConfig.from_pretrained("martin-mwiti/whisper-small-hi-lora-r32-alpha64-20241231")
+model = PeftModel.from_pretrained(base_model, adapter_config)
 
-<!-- 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. -->
+# Use the model for inference
+audio_array = ...  # Replace with your audio array
+inputs = processor(audio_array, sampling_rate=16000, return_tensors="pt")
+predicted_ids = model.generate(inputs.input_features)
 
-[More Information Needed]
+# Decode the transcription
+transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
 
-### Training Procedure
+print("Transcription:", transcription)
 
-<!-- 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 Details
 
-#### Training Hyperparameters
+### Training Data
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- **Dataset:** Common Voice 13.0
+- **Language:** Hindi
+- **Splits:** Trained on combined train and validation sets, tested on test set
 
-#### Speeds, Sizes, Times [optional]
+### Training Procedure
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+#### Training Hyperparameters
 
-[More Information Needed]
+- **Base Model:** openai/whisper-small
+- **Fine-Tuning Method:** PEFT with LoRA
+- **LoRA Configuration:**
+  - Rank (r): 32
+  - Alpha: 64
+  - Target Modules: query and value projection matrices
+  - Dropout: 5%
+- **Training Regime:** Mixed precision (fp16)
+- **Batch Size:** 8 per device
+- **Learning Rate:** 1e-3
+- **Warmup Steps:** 25
+- **Total Training Steps:** 50
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### 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. -->
+### Metrics
 
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- **Primary Metric:** Word Error Rate (WER)
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
+| Metric        | Value   |
+|---------------|---------|
+| **Average WER** | 0.6938 |
+| **Best WER**    | 0.0000 |
+| **Worst WER**   | 1.6000 |
 
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+- **Evaluation Dataset:** Common Voice 13.0 Hindi Test Set
+- **Number of Evaluation Samples:** 50
 
 ## 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
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute).
 
-[More Information Needed]
+## Citation
 
-#### 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. -->
+If you use this model, please cite the original Whisper paper and acknowledge the fine-tuning work.
 
 **BibTeX:**
+```bibtex
+@misc{whisper2022,
+  title={Robust Speech Recognition via Large-Scale Weak Supervision},
+  author={Radford, Alec and Kim, Jong Wook and Xu, Tao and et al.},
+  publisher={arXiv},
+  year={2022}
+}
+```
 
-[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]
+## Model Card Authors
 
-[More Information Needed]
+- martin-mwiti
 
 ## Model Card Contact
 
-[More Information Needed]
+For questions or feedback, please open an issue on the GitHub repository or contact the model author.