开源软件名称：

deep-voice-conversion

开源软件地址：

https://gitee.com/heimu11/deep-voice-conversion

开源软件介绍：

Voice Conversion with Non-Parallel Data

Subtitle: Speaking like Kate Winslet

Authors: Dabi Ahn([email protected]), Kyubyong Park([email protected])

Samples

https://soundcloud.com/andabi/sets/voice-style-transfer-to-kate-winslet-with-deep-neural-networks

Intro

What if you could imitate a famous celebrity's voice or sing like a famous singer?This project started with a goal to convert someone's voice to a specific target voice.So called, it's voice style transfer.We worked on this project that aims to convert someone's voice to a famous English actress Kate Winslet'svoice.We implemented a deep neural networks to achieve that and more than 2 hours of audio book sentences read by Kate Winslet are used as a dataset.

Model Architecture

This is a many-to-one voice conversion system.The main significance of this work is that we could generate a target speaker's utterances without parallel data like <source's wav, target's wav>, <wav, text> or <wav, phone>, but only waveforms of the target speaker.(To make these parallel datasets needs a lot of effort.)All we need in this project is a number of waveforms of the target speaker's utterances and only a small set of <wav, phone> pairs from a number of anonymous speakers.

The model architecture consists of two modules:

Net1(phoneme classification) classify someone's utterances to one of phoneme classes at every timestep.
- Phonemes are speaker-independent while waveforms are speaker-dependent.
Net2(speech synthesis) synthesize speeches of the target speaker from the phones.

We applied CBHG(1-D convolution bank + highway network + bidirectional GRU) modules that are mentioned in Tacotron.CBHG is known to be good for capturing features from sequential data.

Net1 is a classifier.

Process: wav -> spectrogram -> mfccs -> phoneme dist.
Net1 classifies spectrogram to phonemes that consists of 60 English phonemes at every timestep.
- For each timestep, the input is log magnitude spectrogram and the target is phoneme dist.
Objective function is cross entropy loss.
TIMIT dataset used.
- contains 630 speakers' utterances and corresponding phones that speaks similar sentences.
Over 70% test accuracy

Net2 is a synthesizer.

Net2 contains Net1 as a sub-network.

Process: net1(wav -> spectrogram -> mfccs -> phoneme dist.) -> spectrogram -> wav
Net2 synthesizes the target speaker's speeches.
- The input/target is a set of target speaker's utterances.
Since Net1 is already trained in previous step, the remaining part only should be trained in this step.
Loss is reconstruction error between input and target. (L2 distance)
Datasets
- Target1(anonymous female): Arctic dataset (public)
- Target2(Kate Winslet): over 2 hours of audio book sentences read by her (private)
Griffin-Lim reconstruction when reverting wav from spectrogram.

Implementations

Requirements

python 2.7
tensorflow >= 1.1
numpy >= 1.11.1
librosa == 0.5.1

Settings

sample rate: 16,000Hz
window length: 25ms
hop length: 5ms

Procedure

Train phase: Net1 and Net2 should be trained sequentially.
- Train1(training Net1)
  - Run train1.py to train and eval1.py to test.
- Train2(training Net2)
  - Run train2.py to train and eval2.py to test.
    - Train2 should be trained after Train1 is done!
Convert phase: feed forward to Net2
- Run convert.py to get result samples.
- Check Tensorboard's audio tab to listen the samples.
- Take a look at phoneme dist. visualization on Tensorboard's image tab.
  - x-axis represents phoneme classes and y-axis represents timesteps
  - the first class of x-axis means silence.

Tips (Lessons We've learned from this project)

Window length and hop length have to be small enough to be able to fit in only a phoneme.
Obviously, sample rate, window length and hop length should be same in both Net1 and Net2.
Before ISTFT(spectrogram to waveforms), emphasizing on the predicted spectrogram by applying power of 1.0~2.0 is helpful for removing noisy sound.
It seems that to apply temperature to softmax in Net1 is not so meaningful.
IMHO, the accuracy of Net1(phoneme classification) does not need to be so perfect.
- Net2 can reach to near optimal when Net1 accuracy is correct to some extent.