Update README.md

https://github.com/ThatJeffGuy/speaker-identification-toolkit

README.md

@@ -15,75 +15,4 @@ pretty_name: Speaker Identification Toolkit
 
 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
-- Run `dataset-creation.py` to extract English audio tracks from video files.
-
-### 2. Generate Speaker Diarization Data
-- Run `diarize-dataset.py` to process the extracted WAV files and produce JSON files containing diarization data.
-
-### 3. Identify the Target Speaker
-- Run `identify-speaker.py` to play audio segments from diarization files and interactively map the target speaker.
-
-### 4. Isolate and clean-up the Audio
-- Run `isolate-trim.py` to extract and trim the target speaker's audio segments, preparing them for dataset creation.
-
-## Dependencies
-
-Ensure the following are installed:
-
-- **Python 3.8+**
-- **ffmpeg**: Install via your system's package manager or from the [official site](https://ffmpeg.org/).
-
-## Configuration
-
-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/speaker_mapping.csv`
-- **Processed Speaker Output Directory**: `base-folder/targeted`
-
-### 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`
-  
+SEE GITHUB FOR UPDATES - I DON'T UPDATE THE FILES HERE ANYMORE --- https://github.com/ThatJeffGuy/speaker-identification-toolkit