Add comprehensive model card

README.md

@@ -1,199 +1,382 @@
 ---
+language:
+- bo  # Tibetan ISO 639-1 code
+tags:
+- whisper
+- audio
+- automatic-speech-recognition
+- speech-to-text
+- tibetan
+- translation
+- low-resource
+license: apache-2.0
+base_model: openai/whisper-small
+datasets:
+- lilgoose777/merged-tibetan-titung-goose
 library_name: transformers
-tags: []
+pipeline_tag: automatic-speech-recognition
+metrics:
+- wer
+model-index:
+- name: Whisper Small Tibetan
+  results:
+  - task:
+      type: automatic-speech-recognition
+      name: Automatic Speech Recognition
+    dataset:
+      type: lilgoose777/merged-tibetan-titung-goose
+      name: Merged Tibetan Titung Goose
+    metrics:
+    - type: wer
+      value: XX.XX
+      name: Word Error Rate
 ---
 
-# Model Card for Model ID
+# Whisper Small - Tibetan Speech Translation
 
-<!-- Provide a quick summary of what the model is/does. -->
+This model is a fine-tuned version of **[openai/whisper-small](https://huggingface.co/openai/whisper-small)** for Tibetan speech-to-text translation.
 
+## Model Description
 
+- **Model**: Whisper Small (244M parameters)
+- **Language**: Tibetan (བོད་སྐད།)
+- **Task**: Speech Translation (Tibetan audio → Text transcription)
+- **Base Model**: openai/whisper-small
+- **Dataset**: [lilgoose777/merged-tibetan-titung-goose](https://huggingface.co/datasets/lilgoose777/merged-tibetan-titung-goose)
+- **Checkpoint**: checkpoint-3000
 
-## Model Details
+## Intended Uses
 
-### Model Description
+This model is designed to transcribe Tibetan speech into written text. It can be used for:
 
-<!-- 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.
-
-- **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]
-
-## 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]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### 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. -->
-
-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]
+- 📝 Transcribing Tibetan audio recordings
+- 🎙️ Building Tibetan speech recognition applications
+- 📚 Creating subtitles for Tibetan audio/video content
+- 🔬 Research in low-resource language ASR
+- 📖 Preserving and digitizing Tibetan oral traditions
 
 ## 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
+### Dataset
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+The model was trained on the **Merged Tibetan Titung Goose** dataset, which combines multiple Tibetan audio sources for improved coverage.
 
-#### Preprocessing [optional]
+- **Dataset**: `lilgoose777/merged-tibetan-titung-goose`
+- **Train/Test Split**: 90/10
+- **Sampling Rate**: 16000 Hz
 
-[More Information Needed]
+### Training Hyperparameters
 
+The model was fine-tuned with the following configuration:
 
-#### Training Hyperparameters
+| Hyperparameter | Value |
+|----------------|-------|
+| Base Model | `openai/whisper-small` |
+| Training Steps | 3,000 |
+| Batch Size (Train) | 16 |
+| Batch Size (Eval) | 8 |
+| Learning Rate | 1.25e-05 |
+| Warmup Steps | 300 |
+| Gradient Accumulation | 1 |
+| Gradient Checkpointing | True |
+| Mixed Precision (FP16) | False |
+| Max Generation Length | 225 |
+| Evaluation Strategy | Every 100 steps |
+| Save Strategy | Every 100 steps |
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+### Training Infrastructure
 
-#### Speeds, Sizes, Times [optional]
+- **Framework**: HuggingFace Transformers 4.56.2
+- **Training Framework**: Seq2SeqTrainer
+- **Optimizer**: AdamW
+- **Metric**: Word Error Rate (WER)
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+### Results
 
-## Evaluation
+| Metric | Value |
+|--------|-------|
+| **Best WER** | **XX.XX%** |
+| Final WER | XX.XX% |
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+*Lower WER is better. WER (Word Error Rate) measures the percentage of words that are incorrectly transcribed.*
 
-### Testing Data, Factors & Metrics
+## Usage
 
-#### Testing Data
+### Quick Start with Pipeline
+
+```python
+from transformers import pipeline
 
-<!-- This should link to a Dataset Card if possible. -->
+# Create transcription pipeline
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model="milanakdj/whisper-small-full-tibetan",
+    generate_kwargs={"language": "tibetan", "task": "translate"}
+)
 
-[More Information Needed]
+# Transcribe audio file
+result = pipe("path/to/tibetan_audio.wav")
+print(result["text"])
+```
 
-#### Factors
+### Using Processor and Model
+
+```python
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+import torch
+
+# Load model and processor
+processor = WhisperProcessor.from_pretrained("milanakdj/whisper-small-full-tibetan")
+model = WhisperForConditionalGeneration.from_pretrained("milanakdj/whisper-small-full-tibetan")
+
+# Load your audio (16kHz sampling rate)
+# audio_array = ... your audio array ...
+
+# Process audio
+input_features = processor(
+    audio_array,
+    sampling_rate=16000,
+    return_tensors="pt"
+).input_features
+
+# Generate transcription
+forced_decoder_ids = processor.get_decoder_prompt_ids(
+    language="tibetan",
+    task="translate"
+)
+
+with torch.no_grad():
+    predicted_ids = model.generate(
+        input_features,
+        forced_decoder_ids=forced_decoder_ids
+    )
+
+# Decode
+transcription = processor.batch_decode(
+    predicted_ids,
+    skip_special_tokens=True
+)[0]
+
+print(transcription)
+```
+
+### Using with Librosa
+
+```python
+import librosa
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+
+# Load model
+processor = WhisperProcessor.from_pretrained("milanakdj/whisper-small-full-tibetan")
+model = WhisperForConditionalGeneration.from_pretrained("milanakdj/whisper-small-full-tibetan")
+
+# Load audio file (automatically resamples to 16kHz)
+audio, sr = librosa.load("tibetan_audio.mp3", sr=16000)
+
+# Process and transcribe
+input_features = processor(
+    audio,
+    sampling_rate=16000,
+    return_tensors="pt"
+).input_features
+
+forced_decoder_ids = processor.get_decoder_prompt_ids(
+    language="tibetan",
+    task="translate"
+)
+
+predicted_ids = model.generate(
+    input_features,
+    forced_decoder_ids=forced_decoder_ids
+)
+
+transcription = processor.batch_decode(
+    predicted_ids,
+    skip_special_tokens=True
+)[0]
+
+print(transcription)
+```
+
+### Batch Processing Multiple Files
+
+```python
+import torch
+import librosa
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+
+# Load model
+processor = WhisperProcessor.from_pretrained("milanakdj/whisper-small-full-tibetan")
+model = WhisperForConditionalGeneration.from_pretrained("milanakdj/whisper-small-full-tibetan")
+
+# Set device
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+# Process multiple files
+audio_files = ["file1.wav", "file2.wav", "file3.wav"]
+
+for audio_file in audio_files:
+    # Load audio
+    audio, _ = librosa.load(audio_file, sr=16000)
+    
+    # Process
+    input_features = processor(
+        audio,
+        sampling_rate=16000,
+        return_tensors="pt"
+    ).input_features.to(device)
+    
+    # Generate
+    forced_decoder_ids = processor.get_decoder_prompt_ids(
+        language="tibetan",
+        task="translate"
+    )
+    
+    with torch.no_grad():
+        predicted_ids = model.generate(
+            input_features,
+            forced_decoder_ids=forced_decoder_ids
+        )
+    
+    # Decode
+    transcription = processor.batch_decode(
+        predicted_ids,
+        skip_special_tokens=True
+    )[0]
+    
+    print(f"{audio_file}: {transcription}")
+```
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+## Evaluation
 
-[More Information Needed]
+To evaluate the model on your own Tibetan audio dataset:
 
-#### Metrics
+```python
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+from datasets import load_dataset
+import evaluate
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+# Load model
+processor = WhisperProcessor.from_pretrained("milanakdj/whisper-small-full-tibetan")
+model = WhisperForConditionalGeneration.from_pretrained("milanakdj/whisper-small-full-tibetan")
 
-[More Information Needed]
+# Load your dataset
+dataset = load_dataset("your_tibetan_dataset")
 
-### Results
+# Initialize WER metric
+wer_metric = evaluate.load("wer")
 
-[More Information Needed]
+# Process and evaluate
+# ... (see full evaluation code in documentation)
+```
 
-#### Summary
+## Limitations and Considerations
 
+### Known Limitations
 
+1. **Domain Specificity**: The model is trained on specific Tibetan dialects and domains present in the training data
+2. **Audio Quality**: Performance degrades with:
+   - Background noise
+   - Poor recording quality
+   - Multiple speakers
+   - Non-standard dialects
+3. **Low-Resource Language**: As Tibetan is a low-resource language, the model may have limited generalization compared to high-resource language models
+4. **Code-Switching**: May struggle with Tibetan-English or Tibetan-Chinese code-switching
 
-## Model Examination [optional]
+### Best Practices
 
-<!-- Relevant interpretability work for the model goes here -->
+- **Audio Format**: Use 16kHz mono audio for best results
+- **Clean Audio**: Minimize background noise and ensure clear speech
+- **Standard Dialect**: Model performs best on dialects similar to training data
+- **Audio Length**: Optimal performance on audio clips under 30 seconds
 
-[More Information Needed]
+## Ethical Considerations
 
-## Environmental Impact
+### Intended Use
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+This model is intended for:
+- ✅ Transcription of Tibetan speech
+- ✅ Educational purposes
+- ✅ Research in speech recognition
+- ✅ Preservation of Tibetan language
 
-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]
+### Out-of-Scope Use
 
-## Technical Specifications [optional]
+- ❌ Surveillance or monitoring without consent
+- ❌ Generating misleading transcriptions
+- ❌ Any use that violates privacy or human rights
 
-### Model Architecture and Objective
+### Bias and Fairness
 
-[More Information Needed]
+- The model's performance may vary across different Tibetan dialects
+- Training data may not represent all Tibetan-speaking communities equally
+- Users should evaluate the model on their specific use case before deployment
 
-### Compute Infrastructure
+## Citation
 
-[More Information Needed]
+If you use this model in your research or application, please cite:
 
-#### Hardware
+```bibtex
+@misc{whisper-small-tibetan-2024,
+  author = {Milan Akdj},
+  title = {Whisper Small - Tibetan Speech Translation},
+  year = {2024},
+  publisher = {HuggingFace},
+  journal = {HuggingFace Model Hub},
+  howpublished = {\url{https://huggingface.co/milanakdj/whisper-small-full-tibetan}}
+}
+```
 
-[More Information Needed]
+Also cite the original Whisper paper:
 
-#### Software
+```bibtex
+@article{radford2022whisper,
+  title={Robust Speech Recognition via Large-Scale Weak Supervision},
+  author={Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
+  journal={arXiv preprint arXiv:2212.04356},
+  year={2022}
+}
+```
 
-[More Information Needed]
+## Acknowledgements
 
-## Citation [optional]
+- **Base Model**: [OpenAI Whisper Small](openai/whisper-small)
+- **Dataset**: [Merged Tibetan Titung Goose](lilgoose777/merged-tibetan-titung-goose)
+- **Framework**: [HuggingFace Transformers](https://github.com/huggingface/transformers)
+- **Training**: Fine-tuned using HuggingFace Seq2SeqTrainer
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Model Card Authors
 
-**BibTeX:**
+Milan Akdj
 
-[More Information Needed]
+## Contact
 
-**APA:**
+For questions or issues with this model, please open an issue on the [model repository](https://huggingface.co/milanakdj/whisper-small-full-tibetan).
 
-[More Information Needed]
+---
 
-## Glossary [optional]
+## Additional Information
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+### Model Architecture
 
-[More Information Needed]
+Whisper Small uses a Transformer encoder-decoder architecture:
+- **Encoder**: Processes audio features
+- **Decoder**: Generates text transcription
+- **Parameters**: ~244M total parameters
 
-## More Information [optional]
+### Training Environment
 
-[More Information Needed]
+- **Platform**: RunPod GPU Instance
+- **Monitoring**: TensorBoard logging enabled
+- **Evaluation**: WER metric on held-out test set
 
-## Model Card Authors [optional]
+### Version History
 
-[More Information Needed]
+- **v1.0**: Initial release with checkpoint-3000
 
-## Model Card Contact
+---
 
-[More Information Needed]
+**Language**: Tibetan (བོད་སྐད།)  
+**License**: Apache 2.0  
+**Model Size**: ~244M parameters