iimbryk/therapist

Therapist

Table of contents

• Structure
• Installation
• Start

Structure

scripts:

• train.py - train segmentation model. The resulted model writes in folder \models.

• eval.py - model evaluation. The result writes in \output and metrics_result.txt (Set trained_model and read_hyperparameters if you want to use trained model and new hyperparameters)

• infer.py - processing data. The result writes in folder \output. (Set trained_model and read_hyperparameters if you want to use trained model and new hyperparameters)

• tune_hyperparameters.py - the pipeline hyper-parameters optimizing -- segmentation.threshold and clustering.threshold

directoires:

• hyperparameters - hyperparameters (result of tune_hyperparameters.py, using in infer.py)
• models - trained model (result of train.py)
• data_train - put .wav and .csv for models training
• data_test - put .wav and .csv for models testing
• reference_audio - put .wav of the therapist's speech examples
• output - results

Installation

1. Install Python 3.8+ (though it might work with Python 3.7)

2. Install libraries. There's two ways installation liberies:

• use requirements.txt:

Download code, open terminal in \therapist directory and type:

pip install -r requirements.txt

• manually:

pip install torch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0
pip install pyannote.audio
pip install huggingface_hub

Primarily requirements are driven by the pyannote library.

Authorization

Official pyannote.audio pipelines are open-source, but gated. It means that you have to first accept users conditions on their respective Huggingface page to access the pretrained weights and hyper-parameters.

1. Register on HuggingFace
2. Visit speaker-diarization page and accept the terms
3. Visit segmentation page and accept the terms
4. Go to Seetings (User Access Tokens), generate token and copy it
5. Insert token in token.txt file

You can use the same token on different PC.

Start

To get a result on a new data type in terminal:

$ python infer.py --trained_model --read_hyperparameters --no_merge --input_path <DATA_PATH> --path_reference_audio <REFERENCE_PATH>

Meaning of the flags:

• --trained_model -- use the trained model
• --read_hyperparameters -- use optimized hyperparameter
• --no_merge -- turn off merging audio from same speaker talk continuously
• --input_path -- path to data folder, default \data
• --path_reference_audio -- path to reference data folder of therapist's audios, default \reference_audio

All flags are optional. For example $ python infer.py -- default models from pyanote with default hyperparams and result merges by speaker.

therapist's Issues

change path in database.yaml

in database.yaml
MyDatabase: data_train/{uri}.wav

MyDatabase: data_train/{uri}.wav захардкожено. Если я другую папку в input_path указываю, то не работает.