Hi guys,
I am trying to train WaveRNN from this repo https://github.com/erogol/WaveRNN. I am using the default config:
{
“model_name”: “libriTTS-360”,
“model_description”: “Training a universal vocoder.”,
"audio":{
// 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, // DATASET-RELATED: 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": true // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
},
"distributed":{
"backend": "nccl",
"url": "tcp:\/\/localhost:54321"
},
"epochs": 10000,
"grad_clip": 500,
"lr": 0.0001,
"warmup_steps": 10,
"batch_size": 64,
"checkpoint_step": 10000,
"print_step": 10,
"num_workers": 4,
"mel_len": 10,
"pad": 2,
"use_aux_net": false,
"use_upsample_net": false,
"upsample_factors": [5, 5, 12],
"mode": "mold", // mold [string], gauss [string], bits [int]
"mulaw": true, // apply mulaw if mode is bits
}
I just changed the sample rate to be 22050 to match my dataset and do_trim_silences = false
I used this notebook to extract the tts spectrograms: https://github.com/mozilla/TTS/blob/master/notebooks/ExtractTTSpectrogram.ipynb
However when i try to train WaveRNN i get the following error:
coarse = np.stack(coarse).astype(np.float32)
ValueError: all input arrays must have the same shape
I have previously trained a Tacotron 2 model.