Revert "fix: rollback changes"

This reverts commit 652e60a.

src/so_vits_svc_fork/modules/mel_processing.py

@@ -1,4 +1,4 @@
-"""from logging import getLogger
+from logging import getLogger
 
 import torch
 import torch.utils.data
@@ -27,6 +27,8 @@ def spec_to_mel_torch(spec: torch.Tensor, hps: HParams) -> torch.Tensor:
         sample_rate=hps.data.sampling_rate,
         f_min=hps.data.mel_fmin,
         f_max=hps.data.mel_fmax,
+        mel_scale="slaney",
+        norm="slaney",
     ).to(spec.device)(spec)
 
 
@@ -41,165 +43,7 @@ def mel_spectrogram_torch(audio: torch.Tensor, hps: HParams) -> torch.Tensor:
         f_max=hps.data.mel_fmax,
         power=1.0,
         window_fn=torch.hann_window,
+        mel_scale="slaney",
+        norm="slaney",
         normalized=False,
-    ).to(audio.device)(audio)"""
-
-from logging import getLogger
-
-import torch
-import torch.utils.data
-from librosa.filters import mel as librosa_mel_fn
-
-LOG = getLogger(__name__)
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, hps, center=False):
-    if torch.min(y) < -1.0:
-        LOG.info("min value is ", torch.min(y))
-    if torch.max(y) > 1.0:
-        LOG.info("max value is ", torch.max(y))
-    n_fft = hps.data.filter_length
-    hop_size = hps.data.hop_length
-    win_size = hps.data.win_length
-    global hann_window
-    dtype_device = str(y.dtype) + "_" + str(y.device)
-    wnsize_dtype_device = str(win_size) + "_" + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
-            dtype=y.dtype, device=y.device
-        )
-
-    y = torch.nn.functional.pad(
-        y.unsqueeze(1),
-        (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
-        mode="reflect",
-    )
-    y = y.squeeze(1)
-
-    spec = torch.stft(
-        y,
-        n_fft,
-        hop_length=hop_size,
-        win_length=win_size,
-        window=hann_window[wnsize_dtype_device],
-        center=center,
-        pad_mode="reflect",
-        normalized=False,
-        onesided=True,
-        return_complex=False,
-    )
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, hps):
-    sampling_rate = hps.data.sampling_rate
-    n_fft = hps.data.filter_length
-    num_mels = hps.data.n_mel_channels
-    fmin = hps.data.mel_fmin
-    fmax = hps.data.mel_fmax
-    global mel_basis
-    dtype_device = str(spec.dtype) + "_" + str(spec.device)
-    fmax_dtype_device = str(fmax) + "_" + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(
-            sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
-        )
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
-            dtype=spec.dtype, device=spec.device
-        )
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
-
-
-def mel_spectrogram_torch(y, hps, center=False):
-    sampling_rate = hps.data.sampling_rate
-    n_fft = hps.data.filter_length
-    num_mels = hps.data.n_mel_channels
-    fmin = hps.data.mel_fmin
-    fmax = hps.data.mel_fmax
-    hop_size = hps.data.hop_length
-    win_size = hps.data.win_length
-    if torch.min(y) < -1.0:
-        LOG.info(f"min value is {torch.min(y)}")
-    if torch.max(y) > 1.0:
-        LOG.info(f"max value is {torch.max(y)}")
-
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + "_" + str(y.device)
-    fmax_dtype_device = str(fmax) + "_" + dtype_device
-    wnsize_dtype_device = str(win_size) + "_" + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(
-            sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
-        )
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
-            dtype=y.dtype, device=y.device
-        )
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
-            dtype=y.dtype, device=y.device
-        )
-
-    y = torch.nn.functional.pad(
-        y.unsqueeze(1),
-        (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
-        mode="reflect",
-    )
-    y = y.squeeze(1)
-
-    spec = torch.stft(
-        y,
-        n_fft,
-        hop_length=hop_size,
-        win_length=win_size,
-        window=hann_window[wnsize_dtype_device],
-        center=center,
-        pad_mode="reflect",
-        normalized=False,
-        onesided=True,
-        return_complex=False,
-    )
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec
+    ).to(audio.device)(audio)

src/so_vits_svc_fork/preprocessing/preprocess_hubert_f0.py

@@ -8,7 +8,6 @@
 import librosa
 import numpy as np
 import torch
-import torchaudio
 from fairseq.models.hubert import HubertModel
 from joblib import Parallel, delayed
 from tqdm import tqdm
@@ -17,7 +16,7 @@
 from so_vits_svc_fork import utils
 
 from ..hparams import HParams
-from ..modules.mel_processing import spec_to_mel_torch, spectrogram_torch
+from ..modules.mel_processing import mel_spectrogram_torch, spectrogram_torch
 from ..utils import get_total_gpu_memory
 from .preprocess_utils import check_hubert_min_duration
 
@@ -66,9 +65,8 @@ def _process_one(
     torch.cuda.empty_cache()
 
     # Compute spectrogram
-    audio, sr = torchaudio.load(filepath)
-    spec = spectrogram_torch(audio, hps).squeeze(0)
-    mel_spec = spec_to_mel_torch(spec, hps)
+    spec = spectrogram_torch(audio, hps)
+    mel_spec = mel_spectrogram_torch(audio, hps)
     torch.cuda.empty_cache()
 
     # fix lengths
@@ -94,7 +92,7 @@ def _process_one(
         "f0": f0,
         "uv": uv,
         "content": c,
-        "audio": audio,
+        "audio": audio.unsqueeze(0),
         "spk": spk,
     }
     data = {k: v.cpu() for k, v in data.items()}

src/so_vits_svc_fork/train.py

@@ -230,7 +230,7 @@ def _train_and_evaluate(
             ) = net_g(c, f0, uv, spec, g=g, c_lengths=lengths, spec_lengths=lengths)
 
             y_mel = commons.slice_segments(
-                mel, ids_slice, hps.train.segment_size // hps.data.hop_length
+                mel, ids_slice, hps.train.segment_size // hps.data.hop_length + 1
             )
             y_hat_mel = mel_spectrogram_torch(y_hat.squeeze(1), hps)
             y = commons.slice_segments(