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's voice. 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:

1. Net1(phoneme classification) classify someone's utterances to one of phoneme classes at every timestep.
   • Phonemes are speaker-independent while waveforms are speaker-dependent.
2. 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.

References

• "Phonetic posteriorgrams for many-to-one voice conversion without parallel data training", 2016 IEEE International Conference on Multimedia and Expo (ICME)
• "TACOTRON: TOWARDS END-TO-END SPEECH SYNTHESIS", Submitted to Interspeech 2017