text: Title: New pretrained synthesizer model (tensorflow)
  Body: Trained on LibriSpeech, using the current synthesizer (tensorflow). This performs similarly to the current model, with fewer random gaps appearing in the middle of synthesized utterances. It handles short input texts better too.
  
  ### Download link: https://www.dropbox.com/s/3kyjgew55c4yxtf/librispeech_270k_tf.zip?dl=0
  
  Unzip the file and move the `logs-pretrained` folder to `synthesizer/saved_models`.
  
  I am not going to provide scripts to reproduce the training. For anyone interested, you will need to curate LibriSpeech to have more consistent prosody. This is what I did when running synthesizer_preprocess_audio.py:
  
  1. In synthesizer/hparams.py, set `silence_min_duration_split=0.05`
  2. Right before [this line](https://github.com/CorentinJ/Real-Time-Voice-Cloning/blob/8f71d678d2457dffc4d07b52e75be11433313e15/synthesizer/preprocess.py#L182), run `encoder.preprocess_wav()` on each wav, this will use voice activation detection to trim silences (see #501). Compare the lengths of the "before" and  "after" wavs. If they don't match then it means a silence is detected and it is discarded. I keep the "before" wav if the lengths match.
  3. Post-process `datasets_root/SV2TTS/synthesizer/train.txt` to include utterances between 225 and 600 mel frames (2.8 to 7.5 sec). This leaves 48 hours of training data.
  4. Train from scratch for about 270k steps. I used a batch size of 12 because of limited GPU memory.