Try to train Synthesizer

[rlutsyshyn]

Try to train synthesized on train-clean-100 data, but have the next one issue:

╰─ python synthesizer_preprocess_audio.py datasets --datasets_name LibriSpeech --subfolders train-clean-100
Arguments:
    datasets_root:   datasets
    out_dir:         datasets/SV2TTS/synthesizer
    n_processes:     None
    skip_existing:   False
    hparams:         
    no_alignments:   False
    datasets_name:   LibriSpeech
    subfolders:      train-clean-100

Using data from:
    datasets/LibriSpeech/train-clean-100
LibriSpeech: 100%|█████████████████████████████████████████| 251/251 [00:00<00:00, 6260.45speakers/s]
The dataset consists of 0 utterances, 0 mel frames, 0 audio timesteps (0.00 hours).
Traceback (most recent call last):
  File "synthesizer_preprocess_audio.py", line 59, in <module>
    preprocess_dataset(**vars(args))
  File "/home/roma/Real-Time-Voice-Cloning/synthesizer/preprocess.py", line 49, in preprocess_dataset
    print("Max input length (text chars): %d" % max(len(m[5]) for m in metadata))
ValueError: max() arg is an empty sequence

can you help me with this? In the next one steps I also want to try train vocoder on that data

[ghost]

Do you have the LibriSpeech alignments? A link is on this page: https://github.com/CorentinJ/Real-Time-Voice-Cloning/wiki/Training

If it can't find the alignment text files then it thinks there's nothing to process for LibriSpeech.

[rlutsyshyn]

Yep, see this, but how can I create own one for future fine tuning on my data?

[ghost]

An alignment file is used to split long utterances into smaller ones. It is unnecessary for datasets like LibriTTS where you can discard samples that are too long and still have a lot of data remaining. See the violin plot below

If you are making a custom dataset, just try to make your samples 2 to 7 seconds in length for training and don't bother with generating alignments. You can manually split long utterances yourself. If you have a very large number of files to work with and must automate it, use something like the Montreal Forced Aligner.

For finetuning your data just make your dataset look like: #437 (comment)

From https://arxiv.org/pdf/1904.02882v1.pdf

[rlutsyshyn]

Can you help me with creating dataset for training? I use data from https://www.caito.de/2019/01/the-m-ailabs-speech-dataset/ for Ukrainian.
Data looks like:

uk_UK
    |---by_book
          |----female
          |----male
              |---speaker_name
                        |---wavs
                        |---metadata.csv (which consists <filename.wav> | Text in that sound

            ......

Mb I can do it automatically or something like that?

[ghost]

@rlutsyshyn I suggest you write a script that does this:

1. Make a list of every metadata.csv
2. For each metadata.csv:
   • Parse the metadata using something like this code snippet: Training a new model based on LibriTTS #449 (comment)
   • You will have 3 variables: filename, text, and normalized text
   • Save the normalized text into filename.txt (so you will have 1 file per utterance)

After you do this, you can move files around to make it look like #437 (comment) and the command I provided there should work.

[rlutsyshyn]

Hey, how can I contact you? I have some more questions but here is not comfortable to ask it.

[ghost]

I apologize, I am not available to provide consultation outside of the issues board here. For now, my priorities are 1) code development and 2) bug fixes. I answer support questions as time permits but that is not my purpose here.

[rlutsyshyn]

Okay, understood. I have created dataset for new training (just for testing used one speaker), when I start synthesizer_preprocess_audio.py first it seems good, but after I have an error like that:

Arguments:
    datasets_root:   datasets
    out_dir:         datasets/SV2TTS/synthesizer
    n_processes:     None
    skip_existing:   False
    hparams:         
    no_alignments:   True
    datasets_name:   Ukrainian
    subfolders:      female

Using data from:
    datasets/Ukrainian/female
Ukrainian:   0%|                                                         | 0/1 [01:03<?, ?speakers/s]
multiprocessing.pool.RemoteTraceback: 
"""
Traceback (most recent call last):
  File "/home/roma/miniconda3/envs/work/lib/python3.7/multiprocessing/pool.py", line 121, in worker
    result = (True, func(*args, **kwds))
  File "/home/roma/Стільниця/Work/NMT/Real-Time-Voice-Cloning/synthesizer/preprocess.py", line 76, in preprocess_speaker
    assert text_fpath.exists()
AssertionError
"""

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "synthesizer_preprocess_audio.py", line 59, in <module>
    preprocess_dataset(**vars(args))
  File "/home/roma/Стільниця/Work/NMT/Real-Time-Voice-Cloning/synthesizer/preprocess.py", line 35, in preprocess_dataset
    for speaker_metadata in tqdm(job, datasets_name, len(speaker_dirs), unit="speakers"):
  File "/home/roma/miniconda3/envs/work/lib/python3.7/site-packages/tqdm/std.py", line 1129, in __iter__
    for obj in iterable:
  File "/home/roma/miniconda3/envs/work/lib/python3.7/multiprocessing/pool.py", line 748, in next
    raise value
AssertionError

[ghost]

This is in your traceback: assert text_fpath.exists()

Please check that for every filename.wav in your folder, there is a corresponding filename.txt in the same location.

[ghost]

Thank you for reporting the bug with librosa 0.8.0 @rlutsyshyn .