Published Flowtron LibriTTS2K model does not include iteration or optimizer

[ttt733]

Unless I'm missing something, the fine-tuning instructions in the readme do not work. In train.py:

iteration = checkpoint_dict['iteration']
...
    if len(ignore_layers) > 0:
        ...
    else:
        optimizer.load_state_dict(checkpoint_dict['optimizer'])

Hacking around the missing iteration value with iteration = 1 has been mentioned in previous issues, and the optimizer can be skipped over by putting a dummy value into ignore_layers, but it seems like making the published model fit the code would be ideal.

[ttt733]

I cannot get the LibriTTS2K model to work with inference either, actually. I do not think the model's inheriting the weights properly, as it seems to be generating only random noise. If you see anything I'm doing wrong, let me know - if I can get it working, I'll put in a PR to update the readme instructions. config.json

{
    "train_config": {
        "output_directory": "/outdir",
        "epochs": 10000000,
        "optim_algo": "RAdam",
        "learning_rate": 1e-3,
        "weight_decay": 1e-6,
        "grad_clip_val": 1,
        "sigma": 1.0,
        "iters_per_checkpoint": 1000,
        "batch_size": 1,
        "seed": 1234,
        "checkpoint_path": "models/flowtron_libritts2p3k.pt",
        "ignore_layers": [],
        "finetune_layers": [],
        "include_layers": ["speaker", "encoder", "embedding"],
        "warmstart_checkpoint_path": "",
        "with_tensorboard": true,
        "fp16_run": true,
        "gate_loss": true,
        "use_ctc_loss": true,
        "ctc_loss_weight": 0.01,
        "blank_logprob": -8,
        "ctc_loss_start_iter": 10000
    },
    "data_config": {
        "training_files": "filelists/libritts_train_clean_100_audiopath_text_sid_shorterthan10s_atleast5min_train_filelist.txt",
        "validation_files": "filelists/libritts_train_clean_100_audiopath_text_sid_atleast5min_val_filelist.txt",
        "text_cleaners": ["flowtron_cleaners"],
        "p_arpabet": 0.5,
        "cmudict_path": "data/cmudict_dictionary",
        "sampling_rate": 22050,
        "filter_length": 1024,
        "hop_length": 256,
        "win_length": 1024,
        "mel_fmin": 0.0,
        "mel_fmax": 8000.0,
        "max_wav_value": 32768.0,
        "use_attn_prior": true,
        "attn_prior_threshold": 0.0,
        "prior_cache_path": "/attention_prior_cache",
        "betab_scaling_factor": 1.0,
        "keep_ambiguous": false
    },
    "dist_config": {
        "dist_backend": "nccl",
        "dist_url": "tcp://localhost:54321"
    },
    "model_config": {
        "n_speakers": 123,
        "n_speaker_dim": 128,
        "n_text": 185,
        "n_text_dim": 512,
        "n_flows": 2,
        "n_mel_channels": 80,
        "n_attn_channels": 640,
        "n_hidden": 1024,
        "n_lstm_layers": 2,
        "mel_encoder_n_hidden": 512,
        "n_components": 0,
        "mean_scale": 0.0,
        "fixed_gaussian": true,
        "dummy_speaker_embedding": false,
        "use_gate_layer": true,
        "use_cumm_attention": false
    }
}

Command: python inference.py -o ./outdir -c config.json -f models/flowtron_libritts2p3k.pt -w models/waveglow_256channels_universal_v5.pt -t "It is well known that deep generative models have a rich latent space!" -i 1088 Output: Plus a 410 kb wav file of static. The waveglow model (v5) is the one linked in that repo's readme. And since it was mentioned in #74, my torch version is torch==1.8.1+cu111, though I wasn't sure what exactly was meant by "try inference in fp32."

[andi-808]

waveglow_256channels_universal_v5.pt gives me nothing but noise as well. I could not figure out what was happening for a long time and then I switched to v4 and everything worked.

[rafaelvalle]

@ttt733 are you able to produce spectrograms with the pre-trained model?

[ttt733]

No. I'm attempting to use the LibriTTS2k linked in the repo, and I've tried with waveglow v5 and v4 without success. In my latest attempt I'm also getting an error from pytorch:

~/dev/flowtron$ python inference.py -o ./outdir -c config.json -f models/flowtron_libritts2p3k.pt -w models/waveglow_256channels_universal_v4.pt -t "It is well known that deep generative models have a rich latent space!" -i 1088
/home/trevor/anaconda3/envs/blitz/lib/python3.8/site-packages/torch/serialization.py:671: SourceChangeWarning: source code of class 'torch.nn.modules.conv.ConvTranspose1d' has changed. Saved a reverse patch to ConvTranspose1d.patch. Run `patch -p0 < ConvTranspose1d.patch` to revert your changes.
  warnings.warn(msg, SourceChangeWarning)
/home/trevor/anaconda3/envs/blitz/lib/python3.8/site-packages/torch/serialization.py:671: SourceChangeWarning: source code of class 'torch.nn.modules.container.ModuleList' has changed. Tried to save a patch, but couldn't create a writable file ModuleList.patch. Make sure it doesn't exist and your working directory is writable.
  warnings.warn(msg, SourceChangeWarning)
/home/trevor/anaconda3/envs/blitz/lib/python3.8/site-packages/torch/serialization.py:671: SourceChangeWarning: source code of class 'torch.nn.modules.conv.Conv1d' has changed. Saved a reverse patch to Conv1d.patch. Run `patch -p0 < Conv1d.patch` to revert your changes.
  warnings.warn(msg, SourceChangeWarning)

The result is the same as what I posted above. Pytorch version is still 1.10.0.dev20210609.

[egorsmkv]

It works with waveglow_256channels_ljs_v3.pt

to download :

curl -LO 'https://api.ngc.nvidia.com/v2/models/nvidia/waveglow_ljs_256channels/versions/3/files/waveglow_256channels_ljs_v3.pt'