| ```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 | |
| ## Usage | |
| You can use the model with the following Python code: | |
| ```python | |
| from transformers import pipeline, WhisperForConditionalGeneration, WhisperProcessor | |
| import torch | |
| # 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", | |
| ) | |
| # 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: | |
| ``` | |
| python transcribe.py | |
| ``` | |
| 5. The transcribed text will be displayed on the screen. | |
| ## 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 [totetec.dev@gmail.com]. | |
| ## Acknowledgements | |
| - OpenAI for the original Whisper model | |
| ``` | |