AfroLogicInsect
/

whisper-finetuned-float32

@@ -1,199 +1,89 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## 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.
-- **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]
 ## 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. -->
-### 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]
-#### 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]

 ---
+language: en
+license: apache-2.0
+tags:
+- whisper
+- automatic-speech-recognition
+- speech
+- audio
+datasets:
+- your-dataset-name  # Replace with your actual dataset
+metrics:
+- wer
+- cer
+model-index:
+- name: AfroLogicInsect/whisper-finetuned-float32
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Your Dataset Name  # Replace
+      type: your-dataset-type  # Replace
+    metrics:
+    - name: WER
+      type: wer
+      value: "your-wer-score"  # Replace with actual score
 ---
+# AfroLogicInsect/whisper-finetuned-float32
+Fine-tuned Whisper model (float32 version) for speech recognition
 ## Model Details
+- **Model Type**: Whisper (Fine-tuned)
+- **Language**: English
+- **Data Type**: float32
+- **Use Cases**: Speech-to-text transcription
+## Usage
+```python
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+import librosa
+# Load model and processor
+processor = WhisperProcessor.from_pretrained("AfroLogicInsect/whisper-finetuned-float32")
+model = WhisperForConditionalGeneration.from_pretrained("AfroLogicInsect/whisper-finetuned-float32")
+# Load audio
+audio, sr = librosa.load("path/to/audio.wav", sr=16000)
+# Process
+input_features = processor(audio, sampling_rate=16000, return_tensors="pt").input_features
+# Generate transcription
+with torch.no_grad():
+    predicted_ids = model.generate(input_features)
+    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+print(transcription)
+```
 ## Training Details
+- **Base Model**: OpenAI Whisper
+- **Training Dataset**: [Add your dataset details]
+- **Training Parameters**: [Add your training parameters]
+- **Evaluation Metrics**: [Add your evaluation results]
+## Limitations and Biases
+- This model may have biases present in the training data
+- Performance may vary on different accents or audio qualities
+- Recommended for English speech recognition tasks
+## Citation
+If you use this model, please cite:
+```bibtex
+@misc{whisper-finetuned,
+  author = {Daniel AMAH},
+  title = {Fine-tuned Whisper Model},
+  year = {2024},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/AfroLogicInsect/whisper-finetuned-float32}
+}
+```