fix

utils.py

@@ -1,40 +1,9 @@
-import librosa.display
 import matplotlib.pyplot as plt
 import json
 import torch
 import torchaudio
 import hifigan
 
-
-def manual_logging(logger, item, idx, tag, global_step, data_type, config):
-
-    if data_type == "audio":
-        audio = item[idx, ...].detach().cpu().numpy()
-        logger.add_audio(
-            tag,
-            audio,
-            global_step,
-            sample_rate=config["preprocess"]["sampling_rate"],
-        )
-    elif data_type == "image":
-        image = item[idx, ...].detach().cpu().numpy()
-        fig, ax = plt.subplots()
-        _ = librosa.display.specshow(
-            image,
-            x_axis="time",
-            y_axis="linear",
-            sr=config["preprocess"]["sampling_rate"],
-            hop_length=config["preprocess"]["frame_shift"],
-            fmax=config["preprocess"]["sampling_rate"] // 2,
-            ax=ax,
-        )
-        logger.add_figure(tag, fig, global_step)
-    else:
-        raise NotImplementedError(
-            "Data type given to logger should be [audio] or [image]"
-        )
-
-
 def load_vocoder(config):
     with open(
         "hifigan/config_{}.json".format(config["general"]["feature_type"]), "r"
@@ -47,80 +16,6 @@ def load_vocoder(config):
         param.requires_grad = False
     return vocoder
 
-
-def get_conv_padding(kernel_size, dilation=1):
-    return int((kernel_size * dilation - dilation) / 2)
-
-
-def plot_and_save_mels(wav, save_path, config):
-    spec_module = torchaudio.transforms.MelSpectrogram(
-        sample_rate=config["preprocess"]["sampling_rate"],
-        n_fft=config["preprocess"]["fft_length"],
-        win_length=config["preprocess"]["frame_length"],
-        hop_length=config["preprocess"]["frame_shift"],
-        f_min=config["preprocess"]["fmin"],
-        f_max=config["preprocess"]["fmax"],
-        n_mels=config["preprocess"]["n_mels"],
-        power=1,
-        center=True,
-        norm="slaney",
-        mel_scale="slaney",
-    )
-    spec = spec_module(wav.unsqueeze(0))
-    log_spec = torch.log(
-        torch.clamp_min(spec, config["preprocess"]["min_magnitude"])
-        * config["preprocess"]["comp_factor"]
-    )
-    fig, ax = plt.subplots()
-    _ = librosa.display.specshow(
-        log_spec.squeeze(0).numpy(),
-        x_axis="time",
-        y_axis="linear",
-        sr=config["preprocess"]["sampling_rate"],
-        hop_length=config["preprocess"]["frame_shift"],
-        fmax=config["preprocess"]["sampling_rate"] // 2,
-        ax=ax,
-        cmap="viridis",
-    )
-    fig.savefig(save_path, bbox_inches="tight", pad_inches=0)
-
-
-def plot_and_save_mels_all(wavs, keys, save_path, config):
-    spec_module = torchaudio.transforms.MelSpectrogram(
-        sample_rate=config["preprocess"]["sampling_rate"],
-        n_fft=config["preprocess"]["fft_length"],
-        win_length=config["preprocess"]["frame_length"],
-        hop_length=config["preprocess"]["frame_shift"],
-        f_min=config["preprocess"]["fmin"],
-        f_max=config["preprocess"]["fmax"],
-        n_mels=config["preprocess"]["n_mels"],
-        power=1,
-        center=True,
-        norm="slaney",
-        mel_scale="slaney",
-    )
-    fig, ax = plt.subplots(nrows=3, ncols=3, figsize=(18, 18))
-    for i, key in enumerate(keys):
-        wav = wavs[key][0, ...].cpu()
-        spec = spec_module(wav.unsqueeze(0))
-        log_spec = torch.log(
-            torch.clamp_min(spec, config["preprocess"]["min_magnitude"])
-            * config["preprocess"]["comp_factor"]
-        )
-        ax[i // 3, i % 3].set(title=key)
-        _ = librosa.display.specshow(
-            log_spec.squeeze(0).numpy(),
-            x_axis="time",
-            y_axis="linear",
-            sr=config["preprocess"]["sampling_rate"],
-            hop_length=config["preprocess"]["frame_shift"],
-            fmax=config["preprocess"]["sampling_rate"] // 2,
-            ax=ax[i // 3, i % 3],
-            cmap="viridis",
-        )
-    fig.savefig(save_path, bbox_inches="tight", pad_inches=0)
-
-
 def configure_args(config, args):
     for key in ["stage", "corpus_type", "source_path", "aux_path", "preprocessed_path"]:
         if getattr(args, key) != None: