audioldm/hifigan/utilities.py

import os
import json

import torch
import numpy as np

import audioldm.hifigan as hifigan

HIFIGAN_16K_64 = {
    "resblock": "1",
    "num_gpus": 6,
    "batch_size": 16,
    "learning_rate": 0.0002,
    "adam_b1": 0.8,
    "adam_b2": 0.99,
    "lr_decay": 0.999,
    "seed": 1234,
    "upsample_rates": [5, 4, 2, 2, 2],
    "upsample_kernel_sizes": [16, 16, 8, 4, 4],
    "upsample_initial_channel": 1024,
    "resblock_kernel_sizes": [3, 7, 11],
    "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
    "segment_size": 8192,
    "num_mels": 64,
    "num_freq": 1025,
    "n_fft": 1024,
    "hop_size": 160,
    "win_size": 1024,
    "sampling_rate": 16000,
    "fmin": 0,
    "fmax": 8000,
    "fmax_for_loss": None,
    "num_workers": 4,
    "dist_config": {
        "dist_backend": "nccl",
        "dist_url": "tcp://localhost:54321",
        "world_size": 1,
    },
}


def get_available_checkpoint_keys(model, ckpt):
    print("==> Attemp to reload from %s" % ckpt)
    state_dict = torch.load(ckpt)["state_dict"]
    current_state_dict = model.state_dict()
    new_state_dict = {}
    for k in state_dict.keys():
        if (
            k in current_state_dict.keys()
            and current_state_dict[k].size() == state_dict[k].size()
        ):
            new_state_dict[k] = state_dict[k]
        else:
            print("==> WARNING: Skipping %s" % k)
    print(
        "%s out of %s keys are matched"
        % (len(new_state_dict.keys()), len(state_dict.keys()))
    )
    return new_state_dict


def get_param_num(model):
    num_param = sum(param.numel() for param in model.parameters())
    return num_param


def get_vocoder(config, device):
    config = hifigan.AttrDict(HIFIGAN_16K_64)
    vocoder = hifigan.Generator(config)
    vocoder.eval()
    vocoder.remove_weight_norm()
    vocoder.to(device)
    return vocoder


def vocoder_infer(mels, vocoder, lengths=None):
    vocoder.eval()
    with torch.no_grad():
        wavs = vocoder(mels).squeeze(1)

    wavs = (wavs.cpu().numpy() * 32768).astype("int16")

    if lengths is not None:
        wavs = wavs[:, :lengths]

    return wavs