Ayoub-Laachir
/

MaghrebVoice_OnlyLoRaLayers

Automatic Speech Recognition

Model card Files Files and versions Community

Ayoub-Laachir commited on Oct 2

Commit

1744f1a

•

1 Parent(s): 7c9f25c

Update README.md

Browse files

Files changed (1) hide show

README.md +71 -197

README.md CHANGED Viewed

@@ -1,199 +1,73 @@
 ---
-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]

 ---
+license: apache-2.0
+datasets:
+- Ayoub-Laachir/Darija_Dataset
+language:
+- dj
+metrics:
+- wer
+- cer
+base_model:
+- openai/whisper-large-v3
+pipeline_tag: automatic-speech-recognition
 ---
+# Model Card for Fine-tuned Whisper Large V3 (Moroccan Darija)
+## Model Overview
+**Model Name:** Whisper Large V3 (Fine-tuned for Moroccan Darija)
+**Author:** Ayoub Laachir
+**License:** apache-2.0
+**Repository:** [Ayoub-Laachir/MaghrebVoice](https://huggingface.co/Ayoub-Laachir/MaghrebVoice)
+**Dataset:** [Ayoub-Laachir/Darija_Dataset](https://huggingface.co/datasets/Ayoub-Laachir/Darija_Dataset)
+## Description
+This model is a fine-tuned version of OpenAI’s Whisper Large V3, specifically adapted for recognizing and transcribing Moroccan Darija, a dialect influenced by Arabic, Berber, French, and Spanish. The project aims to improve technological accessibility for millions of Moroccans and serve as a blueprint for similar advancements in underrepresented languages.
+## Technologies Used
+- **Whisper Large V3:** OpenAI’s state-of-the-art speech recognition model
+- **PEFT (Parameter-Efficient Fine-Tuning) with LoRA (Low-Rank Adaptation):** An efficient fine-tuning technique
+- **Google Colab:** Cloud environment for training the model
+- **Hugging Face:** Hosting the dataset and final model
+## Dataset Preparation
+The dataset preparation involved several steps:
+1. **Cleaning:** Correcting bad transcriptions and standardizing word spellings.
+2. **Audio Processing:** Converting all samples to a 16 kHz sample rate.
+3. **Dataset Split:** Creating a training set of 3,312 samples and a test set of 150 samples.
+4. **Format Conversion:** Transforming the dataset into the parquet file format.
+5. **Uploading:** The prepared dataset was uploaded to the Hugging Face Hub.
+## Training Process
+The model was fine-tuned using the following parameters:
+- **Per device train batch size:** 8
+- **Gradient accumulation steps:** 1
+- **Learning rate:** 1e-4 (0.0001)
+- **Warmup steps:** 200
+- **Number of train epochs:** 2
+- **Logging and evaluation:** every 50 steps
+- **Weight decay:** 0.01
+Training progress showed a steady decrease in both training and validation loss over 8000 steps.
+## Testing and Evaluation
+The model was evaluated using:
+- **Word Error Rate (WER):** 3.1467%
+- **Character Error Rate (CER):** 2.3893%
+These metrics demonstrate the model's ability to accurately transcribe Moroccan Darija speech.
+The fine-tuned model shows improved handling of Darija-specific words, sentence structure, and overall accuracy.
+## Challenges and Future Improvements
+### Challenges Encountered
+- Diverse spellings of words in Moroccan Darija
+- Cleaning and standardizing the dataset
+### Future Improvements
+- Expand the dataset to include more Darija accents and expressions
+- Further fine-tune the model for specific Moroccan regional dialects
+- Explore integration into practical applications like voice assistants and transcription services
+## Conclusion
+This project marks a significant step towards making AI more accessible for Moroccan Arabic speakers. The success of this fine-tuned model highlights the potential for adapting advanced AI technologies to underrepresented languages, serving as a model for similar initiatives in North Africa.