Update README.md

README.md

@@ -11,44 +11,78 @@ language:
 - en
 pretty_name: Speaker Identification Toolkit
 ---
-# Speaker Identification Toolkit (Speaker Diarization)
+# Speaker Identification Toolkit
 
-This repository contains Python scripts for processing audio and video files, focusing on speaker diarization, speaker identification, audio extraction, and dataset creation. These tools leverage `ffmpeg`, `pyannote.audio`, and other Python libraries for efficient and accurate processing.
+This repository provides a comprehensive toolkit for processing audio and video files, with a focus on speaker diarization, speaker identification, audio extraction, and dataset creation. By leveraging tools like `ffmpeg`, `pyannote.audio`, and other Python libraries, the scripts enable efficient and accurate workflows for handling audio data.
 
 ## Workflow
 
-1. **Extract Audio from Videos**:
-   Use `dataset-creation.py` to extract English audio tracks from video files.
+### 1. Extract Audio from Videos
+Use `dataset-creation.py` to extract English audio tracks from video files.
 
-2. **Generate Speaker Diarization Data**:
-   Use `diarize-dataset.py` to process the extracted WAV files and generate JSON files containing diarization data.
+### 2. Generate Speaker Diarization Data
+Run `diarize-dataset.py` to process the extracted WAV files and produce JSON files containing diarization data.
 
-3. **Identify Target Speaker**:
-   Use `identify-speaker.py` to play audio segments from diarization files and map the target speaker interactively.
+### 3. Identify Target Speaker
+Execute `identify-speaker.py` to play audio segments from diarization files and interactively map the target speaker.
 
-4. **Isolate and Trim Target Speaker Audio**:
-   Run `isolate-trim.py` to extract and trim the target speaker's audio segments for dataset creation.
+### 4. Isolate and Trim Target Speaker Audio
+Use `isolate-trim.py` to extract and trim the target speaker's audio segments, preparing them for dataset creation.
 
 ## Dependencies
 
-Ensure you have the following installed:
+Ensure the following are installed:
 
-- Python 3.8+
-
-- `ffmpeg`:
-  Install `ffmpeg` via your system's package manager or [official site](https://ffmpeg.org/).
+- **Python 3.8+**
+- **ffmpeg**: Install via your system's package manager or from the [official site](https://ffmpeg.org/).
 
 ## Configuration
 
-The script will automatically create the folders it needs, pause for users to populate them with data, or proceed without prompt if they're already populated.
+The scripts automatically create necessary directories and pause execution for users to populate them with required data. Ensure the following directory structure is in place:
 
 - **Video Input Directory**: `base-folder/videos`
 - **WAV Output Directory**: `base-folder/wavs`
 - **JSON Output Directory**: `base-folder/jsons`
-- **Speaker Mapping File**: `base-folder/scripts/mappings.csv`
+- **Speaker Mapping File**: `base-folder/scripts/speaker_mapping.csv`
 - **Processed Speaker Output Directory**: `base-folder/targeted`
 
-**Additionally:**
-
-- For `diarize-dataset.py`, set the Hugging Face API token in the script manually for faster, prompt-free processing. Insert it between the final set of quotes on line 29.
-
+### Notes
+
+- For `diarize-dataset.py`, you can set the Hugging Face API token directly in the script for prompt-free operation. Replace the placeholder value in the following line:
+  ```python
+  default="your_hugging_face_token"
+  ```
+- Verify directory paths and ensure all dependencies are installed to avoid runtime issues.
+
+## Extended Description
+
+### 1. `dataset-creation.py`
+- **Description**: Extracts English audio tracks from video files and converts them to mono 16-bit PCM WAV format.
+- **Key Features**:
+  - Processes video files in `.mp4` and `.mkv` formats.
+  - Automatically names output files using a sequential naming convention.
+- **Dependencies**: `rich`, `ffmpeg`
+
+### 2. `diarize-dataset.py`
+- **Description**: Processes WAV files to generate JSON files containing speaker diarization data.
+- **Key Features**:
+  - Leverages Hugging Face's `pyannote.audio` for diarization.
+  - Outputs JSON files with timestamps and speaker labels.
+- **Dependencies**: `pyannote.audio`, `rich`
+
+### 3. `identify-speaker.py`
+- **Description**: Plays audio segments from diarization files to help users identify and map the target speaker for isolation.
+- **Key Features**:
+  - Interactive selection of the target speaker.
+  - Maintains a CSV file to map diarization files to speakers.
+- **Recommendations**:
+  - Listen to multiple segments to confirm the target speaker, as segments may contain mixed audio.
+  - If a mistake occurs, update the CSV file to correct mappings.
+- **Dependencies**: `playsound`, `rich`, `pandas`, `ffmpeg`
+
+### 4. `isolate-trim.py`
+- **Description**: Extracts and trims audio segments of the target speaker, splitting them into smaller clips if necessary.
+- **Key Features**:
+  - Isolates diarized segments for the target speaker.
+  - Trims silence and ensures clips are 30 seconds or shorter.
+- **Dependencies**: `rich`, `pandas`, `ffmpeg`