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full tensorflow implementation of the paper: StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks https://arxiv.org/abs/1806.02169
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| 2026-07-24 | 273 |
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## StarGAN Voice Conversion This is a tensorflow implementation of the paper: [StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks](https://arxiv.org/abs/1806.02169). **The converted voice examples are in *converted* directory** ## Dependencies - Python 3.6 (or higher) - tensorflow 1.8 - librosa - pyworld - tensorboard - scikit-learn > NOTE:According to some feedbacks, we recommend to use tensorflow version 1.8 exactly. (Tensorflow 1.11 generate nonsense results) ## Usage #### Download dataset Download the vcc 2016 dataset to the current directory and create `train directory` and `test directory`. ``` python download.py --datasets vcc2016 --train_dir ./data/fourspeakers --test_dir ./data/fourspeakers_test For simplicity use: python download.py ``` The downloaded zip files are extracted to `./data/vcc2016_training` and `./data/evaluation_all`. 1. **training set:** In the experiment, we choose **four speakers** from `./data/vcc2016_training`. We move the corresponding folder(eg. SF1,SF2,TM1,TM2 ) to `./data/fourspeakers`. 2. **testing set** In the experiment, we choose **four speakers** from `./data/evaluation_all`. We move the corresponding folder(eg. SF1,SF2,TM1,TM2 ) to `./data/fourspeakers_test`. The data directory now looks like this: ``` data ├── fourspeakers (training set) │ ├── SF1 │ ├── SF2 │ ├── TM1 │ └── TM2 ├── fourspeakers_test (testing set) │ ├── SF1 │ ├── SF2 │ ├── TM1 │ └── TM2 ├── vcc2016_training (vcc 2016 training set) │ ├── ... ├── evaluation_all (vcc 2016 evaluation set, we use it as testing set) │ ├── ... ``` #### Preprocess dataset Extract features (mcep, f0, ap) from each speech clip. The features are stored as npy files. We also calculate the statistical characteristics for each speaker. ``` python preprocess.py --input_dir ./data/fourspeakers --output_dir ./data/processed --ispad True For simplicity use: python preprocess.py ``` This process may take a few minutes ! **Note that test set doesn’t need preprocess.** #### Train Read npy files from `processed_dir` to train model and raw wav files from` test_wav_dir` to randomly generate some samples using the model during training. ``` python train.py --processed_dir ./data/processed --test_wav_dir ./data/fourspeakers_test For simplicity use: python train.py ``` #### Convert Restore model from `model_dir`, convert source_speaker’s speech to target_speaker’s speech. The results are strored in `./converted_voices` ``` python convert.py --model_dir ./your_model_dir --source_speaker SF1 --target_speaker TM1 ``` ## Summary The network structure shown as follows:  **Note: Our implementation follows the original paper’s network structure**, while [pytorch StarGAN-VC code](https://github.com/liusongxiang/StarGAN-Voice-Conversion)‘network is different from the paper as it’s classifier shares the Discriminator’s weights. Both ways generate good converted speeches. ## Reference [CycleGAN-VC code](https://github.com/leimao/Voice_Converter_CycleGAN) [pytorch StarGAN-VC code](https://github.com/hujinsen/pytorch-StarGAN-VC) [StarGAN code](https://github.com/taki0112/StarGAN-Tensorflow) [StarGAN-VC paper](https://arxiv.org/abs/1806.02169) [StarGAN paper](https://arxiv.org/abs/1806.02169) [CycleGAN paper](https://arxiv.org/abs/1703.10593v4) --- If you feel this repo is good, please **star** ! Your encouragement is my biggest motivation!
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:75277b808a1f62f8, topic:tensorflow