Update README.md

README.md

@@ -1,31 +1,89 @@
-```
+```markdown
+# Whisper Large v2 Uzbek Speech Recognition Model
+
+This project contains a fine-tuned version of the Faster Whisper Large v2 model for Uzbek speech recognition. The model can be used to transcribe Uzbek audio files into text.
+
+## Installation
+
+1. Ensure you have Python 3.7 or higher installed.
+
+2. Install the required libraries:
+
+   ```
+   pip install transformers datasets accelerate soundfile librosa torch
+   ```
 
-from peft import PeftModel, PeftConfig
-from transformers import WhisperForConditionalGeneration, WhisperProcessor, pipeline
+## Usage
 
-# PEFT model yolunu tanımlayın
-peft_model_id = "totetecdev/whisper-large-v2-uzbek-100steps"
+You can use the model with the following Python code:
 
-# PEFT konfigürasyonunu yükleyin
-peft_config = PeftConfig.from_pretrained(peft_model_id)
+```python
+from transformers import pipeline, WhisperForConditionalGeneration, WhisperProcessor
+import torch
 
-# Temel modeli yükleyin
-model = WhisperForConditionalGeneration.from_pretrained(
-    peft_config.base_model_name_or_path, 
-    load_in_8bit=True, 
-    device_map="auto"
+# Load the model and processor
+model_name = "totetecdev/whisper-large-v2-uzbek-100steps" 
+model = WhisperForConditionalGeneration.from_pretrained(model_name)
+processor = WhisperProcessor.from_pretrained(model_name)
+
+# Create the speech recognition pipeline
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    torch_dtype=torch.float16,
+    device_map="auto",
 )
 
-# PEFT modelini yükleyin
-model = PeftModel.from_pretrained(model, peft_model_id)
+# Transcribe an audio file
+audio_file = "path/to/your/audio/file.wav"  # Replace with the path to your audio file
+result = pipe(audio_file)
+
+print(result["text"])
+```
+
+## Example Usage
+
+1. Prepare your audio file (it should be in WAV format).
+2. Save the above code in a Python file (e.g., `transcribe.py`).
+3. Update the `model_name` and `audio_file` variables in the code with your values.
+4. Run the following command in your terminal or command prompt:
 
-# Önbelleği etkinleştirin
-model.config.use_cache = True
+   ```
+   python transcribe.py
+   ```
 
-# Whisper işlemcisini yükleyin (tokenizer yerine)
-processor = WhisperProcessor.from_pretrained(peft_config.base_model_name_or_path)
+5. The transcribed text will be displayed on the screen.
 
-# Pipeline'ı oluşturun
-pipe = pipeline("automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor)
+## Notes
 
-```
+- This model will perform best with Uzbek audio files.
+- Longer audio files may require more processing time.
+- GPU usage is recommended, but the model can also run on CPU.
+- If you're using Google Colab, you can upload your audio file using:
+
+  ```python
+  from google.colab import files
+  uploaded = files.upload()
+  audio_file = next(iter(uploaded))
+  ```
+
+## Model Details
+
+- Base Model: Faster Whisper Large v2
+- Fine-tuned for: Uzbek Speech Recognition
+
+## License
+
+This project is licensed under [LICENSE]. See the LICENSE file for details.
+
+## Contact
+
+For questions or feedback, please contact [KHABIB SALIMOV] at [totete.dev@gmail.com].
+
+## Acknowledgements
+
+- OpenAI for the original Whisper model
+
+```