End of training

README.md

@@ -1,96 +1,85 @@
 ---
+library_name: transformers
+language:
+- hi
 license: apache-2.0
+base_model: openai/whisper-small
+tags:
+- generated_from_trainer
 datasets:
 - mozilla-foundation/common_voice_11_0
-language:
-- ur
 metrics:
 - wer
-base_model: openai/whisper-small
-pipeline_tag: automatic-speech-recognition
-library_name: transformers
-tags:
-- transformers
-- torch
-- tensor
+model-index:
+- name: WhisperLiveSubs
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice 11.0
+      type: mozilla-foundation/common_voice_11_0
+      config: ur
+      split: None
+      args: 'config: hi, split: test'
+    metrics:
+    - name: Wer
+      type: wer
+      value: 33.52296915515306
 ---
-# Model Card for WhisperLiveSubs
-This model is a fine-tuned version of OpenAI's Whisper model on the Common Voice dataset for Urdu speech recognition. It is optimized for transcribing Urdu language audio.
-
-### Model Description
-This model is a small variant of the Whisper model fine-tuned on the Common Voice dataset for the Urdu language. It is intended for automatic speech recognition (ASR) tasks and performs well in transcribing Urdu speech.
-- **Developed by:** codewithdark
-- **Model type:** Whisper-based model for ASR
-- **Language(s) (NLP):** Urdu (ur)
-- **License:** Apache 2.0
-- **Finetuned from model :** openai/whisper-small
-
-## Uses
-### Direct Use
-This model can be used directly for transcribing Urdu audio into text. It is suitable for applications such as:
-- Voice-to-text transcription services
-- Captioning Urdu language videos
-- Speech analytics in Urdu
-
-### Out-of-Scope Use
-The model may not perform well for:
-- Non-Urdu languages
-- Extremely noisy environments
-- Very long audio sequences without segmentation
 
-## How to Get Started with the Model
-Use the code below to get started with the model.
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-```python
-from transformers import WhisperProcessor, WhisperForConditionalGeneration
+# WhisperLiveSubs
 
-processor = WhisperProcessor.from_pretrained("codewithdark/WhisperLiveSubs")
-model = WhisperForConditionalGeneration.from_pretrained("codewithdark/WhisperLiveSubs")
+This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6872
+- Wer: 33.5230
 
-# Your transcription code here
-```
+## Model description
 
-### Training Data
-The model was fine-tuned on the Mozilla Common Voice dataset, specifically the Urdu subset. The dataset consists of approximately 141 hr of transcribed Urdu speech.
+More information needed
 
-#### Preprocessing
-The audio was resampled to 16kHz, and text was tokenized using the Whisper tokenizer configured for Urdu.
+## Intended uses & limitations
 
-#### Training Hyperparameters
-- **Training regime:** Mixed precision (fp16)
-- **Batch size:** 8
-- **Gradient accumulation steps:** 2
-- **Learning rate:** 1e-5
-- **Max steps:** 4000
+More information needed
 
-#### Metrics
-Word Error Rate (WER) was the primary metric used to evaluate the model's performance.
+## Training and evaluation data
 
-### Results
+More information needed
 
-- **Training Loss:** 0.2005
-- **Validation Loss:** 0.5342
-- **WER:** 51.06
+## Training procedure
 
-*This is my first time fine-tuning this model. Don't worry about the current performance;
-improvements can be made to enhance the model's accuracy and reduce the WER.*
+### Training hyperparameters
 
-- **Hardware Type:** P100 GPU
-- **Hours used:** 10 hr
-- **Cloud Provider:** Kaggle
-- **Compute Region:** PK
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 8
+- eval_batch_size: 4
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- training_steps: 4000
+- mixed_precision_training: Native AMP
 
-### Model Architecture and Objective
-The WhisperLiveSubs model is based on the Whisper architecture, designed for automatic speech recognition.
+### Training results
 
-#### Software
-- **Framework:** PyTorch
-- **Transformers Version:** 
+| Training Loss | Epoch  | Step | Validation Loss | Wer     |
+|:-------------:|:------:|:----:|:---------------:|:-------:|
+| 0.2002        | 2.1529 | 1000 | 0.5344          | 52.4633 |
+| 0.0573        | 4.3057 | 2000 | 0.5705          | 37.6640 |
+| 0.0109        | 6.4586 | 3000 | 0.6432          | 36.3443 |
+| 0.0044        | 8.6114 | 4000 | 0.6872          | 33.5230 |
 
-#### Summary
-The model demonstrates acceptable performance for Urdu transcription, but there is room for improvement in terms of WER, especially in noisy conditions or with diverse accents.
 
-## Model Card Contact
-For inquiries, please contact codewithdark90@gmail.com
+### Framework versions
 
-@Codewithdark. (2024). WhisperLiveSubs: An Urdu Automatic Speech Recognition Model. Retrieved from https://huggingface.co/codewithdark/WhisperLiveSubs
+- Transformers 4.44.2
+- Pytorch 2.4.0
+- Datasets 2.21.0
+- Tokenizers 0.19.1

generation_config.json

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+{
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+  "is_multilingual": true,
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+  "transformers_version": "4.44.2"
+}