audiovision

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Real-time Speech Separation, Noise Suppression & Speaker Recognition

audiovision 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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# AudioVision

[![Alt Text](https://raw.githubusercontent.com/jadfegh/audiovision/master/audiovision.gif)](https://youtu.be/pLerOFTmngQ)

### Training data generation

Use `train_data_gen.py` script to generate training data samples. Use audio books or voice samples placed in the `train_data_gen\audio_samples` folder.

`train_data_gen.py` takes each mono wav audio files withing the `train_data_gen\audio_samples` folder and converts each 1s bin into a  ~ 224x224 spectrogram.

All original samples should be named after their labels, with the following filename structure:
1. label
2. _ (underscore)
3. anything_else.wav

All generated spectrogrammes will be placed within `tf_files/speakers` separated into distinct folders depending on their label.

Use the batch script `speaker_of_interest_picker.bat` to specify up to 4 speakers of interest from those in `tf_files/speakers`, all others will be merged into a new class *other*.

### Docker instruction for training
*Mostly inspired by [Pete Warden's TensorFlow for Poets](https://petewarden.com/2016/02/28/tensorflow-for-poets/)*

Run latest tensorflow container ([Installation instructions](https://www.tensorflow.org/install/docker)):

`docker run -it -p 0.0.0.0:6006:6006 --rm -v d:/:/p3 -w /. tensorflow/tensorflow:latest bash`

**Note:** p3 is a shared drive between PC and container containing this cloned repository

From within the container:
```
cd p3/audiovision

IMAGE_SIZE=224

ARCHITECTURE="mobilenet_0.50_${IMAGE_SIZE}"

python -m scripts.retrain \
  --bottleneck_dir=tf_files/bottlenecks \
  --how_many_training_steps=4000 \
  --model_dir=tf_files/models/ \
  --summaries_dir=tf_files/training_summaries/"${ARCHITECTURE}" \
  --output_graph=tf_files/retrained_graph.pb \
  --output_labels=tf_files/retrained_labels.txt \
  --architecture="${ARCHITECTURE}" \
  --image_dir=tf_files/speakers
```
  
**Note**: follow training progress on Tensorboard, from an other console:
```
docker ps

docker exec -it <container_id> bash

cd p3/audiovision

tensorboard --logdir tf_files/training_summaries &
```

If you encounter an error, run: `pkill -f "tensorboard"`

### Start TCP server (port 1000) + audio stream to spectrogramme performer
**Purpose:** TCP PCM sink server + real-time inference spectrogram buffer generator
```
cd p3/audiovision
python flow.py
```

### OPTIONAL - Gstreamer TCP audio sink server (Port 1010)
Install the following softwares:
- [VB-CABLE Virtual Audio Device](https://www.vb-audio.com/Cable/index.htm)
- [Gstreamer](https://gstreamer.freedesktop.org/documentation/index.html)
```
gst-launch-1.0 -v tcpserversrc host=0.0.0.0 port=10000 ! rawaudioparse use-sink-caps=false format=pcm pcm-format=s16le sample-rate=44100 num-channels=4 ! audioconvert ! audioresample ! volume volume=7 ! directsoundsink
```

**Note:** From Windows' Audio Mixer, redirect gstreamer stream to the virtual audio cable.

### Configure source separation setup

**Setup:** Raspberry Pi + Seeed's ReSpeaker 4-Mic Array.

Follow instructions to install the seeed voice card driver: [seeed-voicecard source code](https://github.com/respeaker/seeed-voicecard):

```
sudo apt-get update
sudo apt-get upgrade
git clone https://github.com/respeaker/seeed-voicecard.git
cd seeed-voicecard
sudo ./install.sh
reboot
```

Install [ODAS](https://github.com/introlab/odas) (Open embeddeD Audition System):

```
sudo apt-get install libfftw3-dev libconfig-dev libasound2-dev libgconf-2-4
sudo apt-get install cmake
git clone https://github.com/introlab/odas.git
mkdir odas/build
cd odas/build
cmake ..
make
```

### Start microphones array TCP client - ODAS live
Use the configuration file `respeaker_4_mic_array.cfg` that specifies our TCP servers IP as well as their matching ports.

**Note:** make sure port sink 1000 and 1010 are configured for separated.pcm and postfiltered.pcm

```
cd odas/bin
.odaslive -vc ..config//odas/config/odaslive/respeaker_4_mic_array.cfg.
```

### Docker instruction for inference request + web service handler (Flask)
```
docker run -it -p 0.0.0.0:5000:5000 --rm -v d:/:/p3 -w /. tensorflow/tensorflow:latest bash
```
From within the container:
```
pip install flask

cd p3/audiovision

export FLASK_APP=tensorflow_flask.py

flask run --host=0.0.0.0
```
  Flask will deliver all the UI files within the `static` folder and handle all the HTTP inference requests. The UI is done using [Bootstrap](https://getbootstrap.com/) + the great [RNNoise](https://people.xiph.org/~jm/demo/rnnoise/) noise suppression demo slightly modified for real-time playback.

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