Realtime Yukarin is the application for real-time voice conversion with a single command. This application needs trained deep learning models and a GPU computer. The source code is an OSS and MIT license. So you can modify this code, or use it for your applications whether commercial or non-commercial.
- Windows
- GeForce GTX 1060
- 6GB GPU memory
- Intel Core i7-7700 CPU @ 3.60GHz
- Python 3.6
pip install -r requirements.txtYou need two trained models, a first stage model responsible for voice conversion and a second stage model for enhancing the quality of the converted results. You can create a first stage model with Yukarin and a second stage model with Become Yukarin.
Also, for voice pitch conversion, you need a file of frequency statistics at Yukarin.
Here, each filename is as follows:
| Content | Filename |
|---|---|
| Frequency statistics for input voice | ./sample/input_statistics.npy |
| Frequency statistics for target voice | ./sample/target_statistics.npy |
| First stage model from Yukarin | ./sample/model_stage1/predictor.npz |
| First stage's config file | ./sample/model_stage1/config.json |
| Second stage model from Become Yukarin | ./sample/model_stage2/predictor.npz |
| Second stage's config file | ./sample/model_stage2/config.json |
You can verify prepared files with executing ./check.py.
The following example converts 5 seconds voice data of input.wav, and save to output.wav.
python check.py \
--input_path 'input.wav' \
--input_time_length 5 \
--output_path 'output.wav' \
--input_statistics_path './sample/input_statistics.npy' \
--target_statistics_path './sample/target_statistics.npy' \
--stage1_model_path './sample/model_stage1/predictor.npz' \
--stage1_config_path './sample/model_stage1/config.json' \
--stage2_model_path './sample/model_stage2/predictor.npz' \
--stage2_config_path './sample/model_stage2/config.json' \
If you have problems, you can ask questions on Github Issue.
To perform real-time voice conversion, create a config file config.yaml and run ./run.py.
python run.py ./config.yaml# Name of input sound device. Partial Match. Details are below.
input_device_name: str
# Name of output sound device. Partial Match. Details are below.
output_device_name: str
# Input sampling rate
input_rate: int
# Output sampling rate
output_rate: int
# frame_period for Acoustic feature
frame_period: int
# Length of voice to convert at one time (seconds).
# If it is too long, delay will increase, and if it is too short, processing will not catch up.
buffer_time: float
# Method to calclate the fundamental frequency. world ofr crepe.
# CREPE needs additional libraries, details are requirements.txt
extract_f0_mode: world
# Length of voice to be synthesized at one time (number of samples)
vocoder_buffer_size: int
# Amplitude scaling for input.
# When it is more than 1, the amplitude becomes large, and when it is less than 1, the amplitude becomes small.
input_scale: float
# Amplitude scaling for output.
# When it is more than 1, the amplitude becomes large, and when it is less than 1, the amplitude becomes small.
output_scale: float
# Silence threshold for input (db).
# The smaller the value, the easier it is to silence.
input_silent_threshold: float
# Silence threshold for output (db).
# The smaller the value, the easier it is to silence.
output_silent_threshold: float
# Overlap for encoding (seconds)
encode_extra_time: float
# Overlap for converting (seconds)
convert_extra_time: float
# Overlap for decoding (seconds)
decode_extra_time: float
# Path of frequency statistics file
input_statistics_path: str
target_statistics_path: str
# Path of trained model file
stage1_model_path: str
stage1_config_path: str
stage2_model_path: str
stage2_config_path: strIn the example below, Logitech Speaker is the name of the sound device.
