pafst 1.0.0

Library That Preprocessing Audio For TTS/STT.

PAFST

---

Library That Preprocessing Audio For TTS.

This library enables easy processing of audio files into a format suitable for TTS training data with a simple execution.

Description

PAFST have three features.

1. Separator and Denoiser
2. VAD
3. Diarization
4. STT

• Separator or Denoiser : Removes background music (MR) and noise from each audio file to isolate clean voice tracks.
• VAD : Detects whether the audio is present or absent.
• Diarization : Separates speakers within each audio file, identifying distinct voices.
• STT : Extract text from audio.

# before run()

      path
        ├── TEST-1.wav # have mr or noise
        └── TEST-2.wav
        


# after run()
    
       path
        ├── speaker_SPEAKER_00
        │   ├── SPEAKER_00_1.wav # removed mr and noise
        │   ├── SPEAKER_00_2.wav
        │   └── SPEAKER_00_3.wav
        ├── speaker_SPEAKER_01
        │   ├── SPEAKER_01_1.wav
        │   └── SPEAKER_01_2.wav
        ├── speaker_SPEAKER_02
        │   ├── SPEAKER_02_1.wav
        │   └── SPEAKER_02_2.wav
        ├── asr.json
        └── diarization.json
        
        # diarization.json
        [
              {
                "speaker_path": "/processed_audio/speaker_SPEAKER_00/SPEAKER_00_0.wav",
                "audio_filepath": "processed_audio//TEST-1.wav", # this is audio separated
                "start_time": 0.03,
                "end_time": 3.81
              },
            ...
      ]

      # asr.json
      [
            {
              "asr_text": " Let's talk about music. I often do you listen to music.",
              "audio_filepath": "/processed_audio/speaker_SPEAKER_00/SPEAKER_00_0.wav",
              "language": "en"
            } 
      ]

Features

• Separator : Using the UVR project’s model and code for music source separation.
• Denoiser : DFNet3 and Facebook's denoiser
• VAD : Using webrtcvad
• Diarization : Using speaker diarization from pyannote-audio
• STT : Using STT model whisper from OpenAI and faster-whisper

Setup

This library was developed using Python 3.10, and we recommend using Python versions 3.8 to 3.10 for compatibility.

While the library is compatible with both Linux and Windows, all testing was conducted on Linux. For any issues or errors encountered while running on Linux, please feel free to open an issue.

Before running the library, please ensure the following are installed:

PyTorch

We highly recommend using a GPU to optimize performance. For PyTorch installation, please follow the commands below to ensure compatibility with your GPU

# Example for installing PyTorch with CUDA 11.8
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

ffmpeg

ffmpeg is required for audio processing tasks within this library. Please ensure it is installed and accessible from your system’s PATH. To install ffmpeg:

Windows

Download the latest FFmpeg release from FFmpeg’s official website, and add the bin folder to your system’s PATH.

Linux

Use the following command to install FFmpeg:

sudo apt update
sudo apt install ffmpeg

After installation, you can verify by running

ffmpeg -version

HuggingFace Access Token (required for diarization)

To enable diarization functionality, please complete the following steps

1. Accept pyannote/segmentation-3.0 user conditions
2. Accept pyannote/speaker-diarization-3.1 user conditions
3. Create access token at hf.co/settings/tokens.

from pafst.pafts import PAFST

p = PAFST(
    path = 'your_audio_directory_path',
    output_path = 'output_path',
    hf_token="HUGGINGFACE_ACCESS_TOKEN_GOES_HERE"
)

After completing the setup steps above, you can install this library by running

pip install pafst

Usage

from pafst import PAFST

p = PAFST(
    path = 'your_audio_directory_path',
    output_path = 'output_path',
    hf_token="HUGGINGFACE_ACCESS_TOKEN_GOES_HERE" # if you use diarization
    
)

# Separator
p.separator() # or

p.denoiser(processor="dfn") # use "den" for facebook's denoiser

p.vad() # voice-activity-detection using webrtcvad

# Diarization
p.diarization()

# STT
p.stt(model_size='small')

# One-Click Process
p.run()

TODO

• Command line
• Clean logging
• Separator with Model Selection

References:

• PAFTS for base code
• Paper for DFNet3 use case

License

The code of PAFST is MIT-licensed