visalkao
/

whisper-small-french-finetuning

@@ -1,7 +1,10 @@
----
-base_model: openai/whisper-small
-library_name: peft
----
 # Model Card for Model ID
@@ -17,186 +20,73 @@ library_name: peft
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-### Framework versions
-- PEFT 0.15.1

+---
+base_model: openai/whisper-small
+library_name: peft
+license: mit
+language:
+- fr
+---
 # Model Card for Model ID
+- **Developed by:** Visal KAO
+- **Model type:** Speech Recognition
+- **Language(s) (NLP):** French
+- **License:** MIT
+- **Finetuned from model :** Whisper-small
 ### Model Sources [optional]
 <!-- Provide the basic links for the model. -->
+- **Repository:** openai/whisper-small
+## Dataset
+This model is finetuned on 50% of French Single Speaker Speech Dataset on kaggle (Only lesmis).
+- **Link to dataset :** (https://www.kaggle.com/datasets/bryanpark/french-single-speaker-speech-dataset)
 ## 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 goal of this project is to finetune whisper-small model to improve its accuracy for french transcription.
+The reason why I chose Whisper-small is due to its size and versatility. My primary objective is to build/finetune a small model to get acceptable results.
 ### Direct Use
+**Live Demo :** https://huggingface.co/spaces/visalkao/whisper-small-french-finetuned
 ## Bias, Risks, and Limitations
+As this model has less than 250 millions parameters, which is quite small considering its objective is to transcribe speech, it also has its own limitation.
+The Word Error Rate (WER) of this finetuned model is approximately 0.17 (17%).
+For reference, the original Whisper-small's WER is around 0.27 (27%) on the same dataset.
+## Training Hyperparameters
+This model is trained using LoRa with these hyperparamters:
+* per_device_train_batch_size=3,
+* gradient_accumulation_steps=1,
+* learning_rate=1e-3,
+* num_train_epochs=7,
+* evaluation_strategy="epoch",
+* fp16=True,
+* per_device_eval_batch_size=1,
+* generation_max_length=225,
+* logging_steps=10,
+* remove_unused_columns=False,
+* label_names=["labels"],
+* predict_with_generate=True,
+## Results
+Before finetuning, The Word Error Rate on this dataset is approximately 0.27.
+After finetuning, it drops down 0.1 to 0.17 or 17% wer (On testing data).
+Here is the training log:
+| Epoch | Training Loss | Validation Loss | WER        |
+|-------|--------------|----------------|------------|
+| 1     | 0.369600     | 0.404414       | 26.665379  |
+| 2     | 0.273200     | 0.361762       | 22.793976  |
+| 3     | 0.308800     | 0.344289       | 24.454528  |
+| 4     | 0.131600     | 0.318023       | 21.847847  |
+| 5     | 0.117400     | 0.311023       | 19.134968  |
+| 6     | 0.035700     | 0.301410       | 18.922572  |
+| 7     | 0.013900     | 0.315151       | 16.972388  |