Updated the file

README.md

@@ -12,8 +12,8 @@ to perform accurate and robust speaker identification.
 ## Installation
 1. **Clone the Repository**
    ```bash
-   git clone https://github.com/your_username/your_repository_name.git
-   cd your_repository_name
+   git clone git@github.com:GenaNiv/voice-recognition-engine.git
+   cd voice-recognition-engine
 
 2. **Install Dependencies**
    ```bash
@@ -32,21 +32,21 @@ The Speaker Recognition Engine supports several commands for managing speaker au
 3. **List Enrolled Speakers**: Display a list of all enrolled speakers.
 4. **Delete a Speaker**: Remove a speaker's data from the system.
 
-Each command can be executed from the command line with the appropriate arguments. The general syntax for using the tool is:
-
+Each command can be executed from the command line with the appropriate arguments. 
+The general syntax for using the tool is:
 ```bash
 python cli.py <command> [arguments]
+```
 
-### Enroll a Speaker
+## Enroll a Speaker
 
 To enroll a new speaker, use the `enroll` command followed by the speaker's name and the path to the audio file. Optionally, you can specify parameters like sample rate, number of filters, and number of MFCC coefficients.
 
 **Syntax:**
 ```bash
 python cli.py enroll <speaker_name> <audio_file_path> [optional parameters]
-
+```
 Optional Parameters:
-
 --sample_rate: Sampling rate of the audio file (default: 16000)
 --num_filters: Number of Mel filters (default: 26)
 --num_ceps: Number of MFCC coefficients (default: 13)
@@ -55,5 +55,5 @@ Optional Parameters:
 --frame_step: Frame step (overlap) in seconds (default: 0.01)
 --n_mixtures: Number of Gaussian mixtures in GMM (default: 8)
 
-Example:
+**Example:**
 python cli.py enroll gena /home/gena/audio_files/gena.wav --sample_rate 16000 --num_filters 40 --num_ceps 13 --n_fft 512 --frame_size 0.025 --frame_step 0.01 --n_mixtures 8