| # Autodiarize | |
| This repository provides a comprehensive set of tools for audio diarization, transcription, and dataset management. It leverages state-of-the-art models like Whisper, NeMo, and wav2vec2 to achieve accurate results. | |
| ## Table of Contents | |
| - [Installation](#installation) | |
| - [Usage](#usage) | |
| - [Diarization and Transcription](#diarization-and-transcription) | |
| - [Bulk Transcription](#bulk-transcription) | |
| - [Audio Cleaning](#audio-cleaning) | |
| - [Dataset Management](#dataset-management) | |
| - [YouTube to WAV Conversion](#youtube-to-wav-conversion) | |
| - [LJSpeech Dataset Structure](#ljspeech-dataset-structure) | |
| - [Contributing](#contributing) | |
| - [License](#license) | |
| ## Installation | |
| ### 1. Clone the repository: | |
| ```bash | |
| git clone https://github.com/your-username/whisper-diarization.git | |
| cd whisper-diarization | |
| ``` | |
| ### 2. Create a Python virtual environment and activate it: | |
| ```bash | |
| ./create-env.sh | |
| source autodiarize/bin/activate | |
| ``` | |
| or if you want to ruin your python env | |
| ### Install the required packages: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ## Usage | |
| ### Diarization and Transcription | |
| The `diarize.py` script performs audio diarization and transcription on a single audio file. It uses the Whisper model for transcription and the NeMo MSDD model for diarization. | |
| ```bash | |
| python diarize.py -a <audio_file> [--no-stem] [--suppress_numerals] [--whisper-model <model_name>] [--batch-size <batch_size>] [--language <language>] [--device <device>] | |
| ``` | |
| - `-a`, `--audio`: Path to the target audio file (required). | |
| - `--no-stem`: Disables source separation. This helps with long files that don't contain a lot of music. | |
| - `--suppress_numerals`: Suppresses numerical digits. This helps the diarization accuracy but converts all digits into written text. | |
| - `--whisper-model`: Name of the Whisper model to use (default: "medium.en"). | |
| - `--batch-size`: Batch size for batched inference. Reduce if you run out of memory. Set to 0 for non-batched inference (default: 8). | |
| - `--language`: Language spoken in the audio. Specify None to perform language detection (default: None). | |
| - `--device`: Device to use for inference. Use "cuda" if you have a GPU, otherwise "cpu" (default: "cuda" if available, else "cpu"). | |
| ### Bulk Transcription | |
| The `bulktranscript.py` script performs diarization and transcription on multiple audio files in a directory. | |
| ```bash | |
| python bulktranscript.py -d <directory> [--no-stem] [--suppress_numerals] [--whisper-model <model_name>] [--batch-size <batch_size>] [--language <language>] [--device <device>] | |
| ``` | |
| - `-d`, `--directory`: Path to the directory containing the target files (required). | |
| - Other arguments are the same as in `diarize.py`. | |
| ### Audio Cleaning | |
| The `audio_clean.py` script cleans an audio file by removing silence and applying EQ and compression. | |
| ```bash | |
| python audio_clean.py <audio_path> <output_path> | |
| ``` | |
| - `<audio_path>`: Path to the input audio file. | |
| - `<output_path>`: Path to save the cleaned audio file. | |
| ### Dataset Management | |
| The repository includes several scripts for managing datasets in the LJSpeech format. | |
| #### Merging Folders | |
| The `mergefolders.py` script allows you to merge two LJSpeech-like datasets into one. | |
| ```bash | |
| python mergefolders.py | |
| ``` | |
| Follow the interactive prompts to select the directories to merge and specify the output directory. | |
| #### Consolidating Datasets | |
| The `consolidate_datasets.py` script consolidates multiple LJSpeech-like datasets into a single dataset. | |
| ```bash | |
| python consolidate_datasets.py | |
| ``` | |
| Modify the `base_folder` and `output_base_folder` variables in the script to specify the input and output directories. | |
| #### Combining Sets | |
| The `combinesets.py` script combines multiple enumerated folders within an LJSpeech-like dataset into a chosen folder. | |
| ```bash | |
| python combinesets.py | |
| ``` | |
| Enter the name of the chosen folder when prompted. The script will merge the enumerated folders into the chosen folder. | |
| ### YouTube to WAV Conversion | |
| The `youtube_to_wav.py` script downloads a YouTube video and converts it to a WAV file. | |
| ```bash | |
| python youtube_to_wav.py [<youtube_url>] | |
| ``` | |
| - `<youtube_url>`: (Optional) URL of the YouTube video to download and convert. If not provided, the script will prompt for the URL. | |
| ## LJSpeech Dataset Structure | |
| The `autodiarize.py` script generates an LJSpeech-like dataset structure for each input audio file. Here's an example of how the dataset structure looks: | |
| ``` | |
| autodiarization/ | |
| ├── 0/ | |
| │ ├── speaker_0/ | |
| │ │ ├── speaker_0_001.wav | |
| │ │ ├── speaker_0_002.wav | |
| │ │ ├── ... | |
| │ │ └── metadata.csv | |
| │ ├── speaker_1/ | |
| │ │ ├── speaker_1_001.wav | |
| │ │ ├── speaker_1_002.wav | |
| │ │ ├── ... | |
| │ │ └── metadata.csv | |
| │ └── ... | |
| ├── 1/ | |
| │ ├── speaker_0/ | |
| │ │ ├── speaker_0_001.wav | |
| │ │ ├── speaker_0_002.wav | |
| │ │ ├── ... | |
| │ │ └── metadata.csv | |
| │ ├── speaker_1/ | |
| │ │ ├── speaker_1_001.wav | |
| │ │ ├── speaker_1_002.wav | |
| │ │ ├── ... | |
| │ │ └── metadata.csv | |
| │ └── ... | |
| └── ... | |
| ``` | |
| Each input audio file is processed and assigned an enumerated directory (e.g., `0/`, `1/`, etc.). Within each enumerated directory, there are subdirectories for each speaker (e.g., `speaker_0/`, `speaker_1/`, etc.). | |
| Inside each speaker's directory, the audio segments corresponding to that speaker are saved as individual WAV files (e.g., `speaker_0_001.wav`, `speaker_0_002.wav`, etc.). Additionally, a `metadata.csv` file is generated for each speaker, containing the metadata for each audio segment. | |
| The `metadata.csv` file has the following format: | |
| ``` | |
| filename|speaker|text | |
| speaker_0_001|Speaker 0|Transcribed text for speaker_0_001 | |
| speaker_0_002|Speaker 0|Transcribed text for speaker_0_002 | |
| ... | |
| ``` | |
| Each line in the `metadata.csv` file represents an audio segment, with the filename (without extension), speaker label, and transcribed text separated by a pipe character (`|`). | |