Error when trying to use custom trained wavernn model

I’m using latest master branches of MozillaTTS and related WaveRNN branch (

I have trained a test model for WaveRNN vocoder and trying to use newly trained model in MozillaTTS for Tacotron1. My WaveRNN configuration is like this:

“model_name”: “wavernn_4241_finetune_gaussian”,
“model_description”: “Gaussian model as in Clarinet”,

    "audio_processor": "audio",     // to use dictate different audio processors, if available.
    // Audio processing parameters
    "num_mels": 80,         // size of the mel spec frame. 
    "num_freq": 1025,       // number of stft frequency levels. Size of the linear spectogram frame.
    "sample_rate": 16000,   // wav sample-rate. If different than the original data, it is resampled.
    "frame_length_ms": 50,  // stft window length in ms.
    "frame_shift_ms": 12.5, // stft window hop-lengh in ms.
    "preemphasis": 0.98,    // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
    "min_level_db": -100,   // normalization range
    "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
    "power": 1.5,           // value to sharpen wav signals after GL algorithm.
    "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
    // Normalization parameters
    "signal_norm": true,    // normalize the spec values in range [0, 1]
    "symmetric_norm": false, // move normalization to range [-1, 1]
    "max_norm": 1,          // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
    "clip_norm": true,      // clip normalized values into the range.
    "mel_fmin": 0.0,         // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
    "mel_fmax": 8000.0,        // maximum freq level for mel-spec. Tune for dataset!!
    "do_trim_silence": false  // KEEP ALWAYS FALSE

    "backend": "nccl",
    "url": "tcp:\/\/localhost:54321"

"epochs": 10000,
"grad_clip": 1000,
"lr": 0.0001,
"warmup_steps": 100,
"batch_size": 32,
"checkpoint_step": 1000,
"print_step": 10,
"num_workers": 8,
"mel_len": 8,
"pad": 2,
"mulaw": true,
"use_aux_net": false,
"use_upsample_net": false,
"upsample_factors": [5, 5, 11],
"mode": "mold",      // model with gaussian (gaus), misture of logistic dist (mold). or raw bit output (# bits)."

When I try to use this wavernn model together with tacotron1 model, as soon as I try a test synthesis over server, I got the following error:

“RuntimeError: size mismatch, m1: [1 x 1026], m2: [81 x 512] at /opt/conda/conda-bld/pytorch_1573049310284/work/aten/src/THC/generic/”

The error stems from the following line in WaveRNN/models/ line 246:

“x = self.I(x)”

Here, the size of “x” is [1 x 1026] while the size of “I” is [81 x 512]

the size of “I” is initialized in line:

“self.I = nn.Linear(feat_dims + 1, rnn_dims)”

and the size is naturally [81 x 512]

I wonder if the latest master branch of mozillaTTS is compatible with WaveRNN or if there is any other error I make.

Thanks in advance.

I’ve not run WaveRNN for a long while. So there might be a mismatch. But it should be easy to fix. If you fix it, it 'd be a nice PR.

Hi Eren,
I think I have found the error:
in the file at line 190:
wav = self.wavernn.generate(vocoder_input, batched=self.config.is_wavernn_batched, target=11000, overlap=550)

the definition of vocoder_input is:

vocoder_input = torch.FloatTensor(postnet_output.T).unsqueeze(0)

here, postnet_output is 1025 dimensional while decoder_output is 80 dimensional which is an expected input to wavernn vocoder.

when I gave decoder_output instead of postnet_output to the vocoder, the error disappeared but synthesized waveforms are unintelligible.

Could you correct me if I’m doing anything wrong?


I don’t think I can look at warernn right now. Sorry about it but I am out of my bandwidth right now.