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GPT_SoVITS/module/data_utils.py

@@ -1,6 +1,8 @@
-import time, logging
+import time
+import logging
 import os
-import random, traceback
+import random
+import traceback
 import numpy as np
 import torch
 import torch.utils.data
@@ -12,15 +14,12 @@ from text import cleaned_text_to_sequence
 from utils import load_wav_to_torch, load_filepaths_and_text
 import torch.nn.functional as F
 from functools import lru_cache
-import torch
 import requests
 from scipy.io import wavfile
 from io import BytesIO
-
-# from config import exp_dir
 from my_utils import load_audio
 
-
+# ZeroDivisionError fixed by Tybost (https://github.com/RVC-Boss/GPT-SoVITS/issues/79)
 class TextAudioSpeakerLoader(torch.utils.data.Dataset):
     """
     1) loads audio, speaker_id, text pairs
@@ -44,7 +43,7 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
 
         for line in lines:
             tmp = line.split("\t")
-            if len(tmp) != 4:
+            if (len(tmp) != 4):
                 continue
             self.phoneme_data[tmp[0]] = [tmp[1]]
 
@@ -52,7 +51,7 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
         tmp = self.audiopaths_sid_text
         leng = len(tmp)
         min_num = 100
-        if leng < min_num:
+        if (leng < min_num):
             self.audiopaths_sid_text = []
             for _ in range(max(2, int(min_num / leng))):
                 self.audiopaths_sid_text += tmp
@@ -77,20 +76,28 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
         for audiopath in tqdm(self.audiopaths_sid_text):
             try:
                 phoneme = self.phoneme_data[audiopath][0]
-                phoneme = phoneme.split(" ")
+                phoneme = phoneme.split(' ')
                 phoneme_ids = cleaned_text_to_sequence(phoneme)
             except Exception:
                 print(f"{audiopath} not in self.phoneme_data !")
                 skipped_phone += 1
                 continue
+
             size = os.path.getsize("%s/%s" % (self.path5, audiopath))
             duration = size / self.sampling_rate / 2
+
+            if duration == 0:
+                print(f"Zero duration for {audiopath}, skipping...")
+                skipped_dur += 1
+                continue
+
             if 54 > duration > 0.6 or self.val:
                 audiopaths_sid_text_new.append([audiopath, phoneme_ids])
                 lengths.append(size // (2 * self.hop_length))
             else:
                 skipped_dur += 1
                 continue
+
         print("skipped_phone: ", skipped_phone, ", skipped_dur: ", skipped_dur)
         print("total left: ", len(audiopaths_sid_text_new))
         assert len(audiopaths_sid_text_new) > 1  # 至少能凑够batch size，这里todo
@@ -103,10 +110,8 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
         try:
             spec, wav = self.get_audio("%s/%s" % (self.path5, audiopath))
             with torch.no_grad():
-                ssl = torch.load(
+                ssl = torch.load("%s/%s.pt" % (self.path4, audiopath), map_location="cpu")
-                    "%s/%s.pt" % (self.path4, audiopath), map_location="cpu"
+                if (ssl.shape[-1] != spec.shape[-1]):
-                )
-                if ssl.shape[-1] != spec.shape[-1]:
                     typee = ssl.dtype
                     ssl = F.pad(ssl.float(), (0, 1), mode="replicate").to(typee)
                 ssl.requires_grad = False
@@ -117,25 +122,15 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
             ssl = torch.zeros(1, 768, 100)
             text = text[-1:]
             print("load audio or ssl error!!!!!!", audiopath)
-        # print(ssl.requires_grad,spec.requires_grad,wav.requires_grad,text.requires_grad)
         return (ssl, spec, wav, text)
 
     def get_audio(self, filename):
-        audio_array = load_audio(
+        audio_array = load_audio(filename, self.sampling_rate)  # load_audio的方法是已经归一化到-1~1之间的，不用再/32768
-            filename, self.sampling_rate
-        )  # load_audio的方法是已经归一化到-1~1之间的，不用再/32768
-        # print(filename,audio_array.max(),audio_array.min(),audio_array.mean())
         audio = torch.FloatTensor(audio_array)  # /32768
         audio_norm = audio
         audio_norm = audio_norm.unsqueeze(0)
-        spec = spectrogram_torch(
+        spec = spectrogram_torch(audio_norm, self.filter_length, self.sampling_rate, self.hop_length, self.win_length,
-            audio_norm,
+                                  center=False)
-            self.filter_length,
-            self.sampling_rate,
-            self.hop_length,
-            self.win_length,
-            center=False,
-        )
         spec = torch.squeeze(spec, 0)
         return spec, audio_norm
 
@@ -152,10 +147,7 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
 
     def random_slice(self, ssl, wav, mel):
         assert abs(ssl.shape[-1] - wav.shape[-1] // self.hop_length) < 3, (
-            "first",
+        "first", ssl.shape, wav.shape)
-            ssl.shape,
-            wav.shape,
-        )
 
         len_mel = mel.shape[1]
         if self.val:
@@ -176,20 +168,13 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
             mel = mel[:, :sep_point]
 
         assert abs(ssl.shape[-1] - wav2.shape[-1] // self.hop_length) < 3, (
-            ssl.shape,
+        ssl.shape, wav.shape, wav2.shape, mel.shape, sep_point, self.hop_length, sep_point * self.hop_length, dir)
-            wav.shape,
-            wav2.shape,
-            mel.shape,
-            sep_point,
-            self.hop_length,
-            sep_point * self.hop_length,
-            dir,
-        )
         return reference_mel, ssl, wav2, mel
 
 
-class TextAudioSpeakerCollate:
+class TextAudioSpeakerCollate():
-    """Zero-pads model inputs and targets"""
+    """ Zero-pads model inputs and targets
+    """
 
     def __init__(self, return_ids=False):
         self.return_ids = return_ids
@@ -202,8 +187,8 @@ class TextAudioSpeakerCollate:
         """
         # Right zero-pad all one-hot text sequences to max input length
         _, ids_sorted_decreasing = torch.sort(
-            torch.LongTensor([x[1].size(1) for x in batch]), dim=0, descending=True
+            torch.LongTensor([x[1].size(1) for x in batch]),
-        )
+            dim=0, descending=True)
 
         max_ssl_len = max([x[0].size(2) for x in batch])
         max_ssl_len = int(2 * ((max_ssl_len // 2) + 1))
@@ -246,16 +231,7 @@ class TextAudioSpeakerCollate:
             text_padded[i, :text.size(0)] = text
             text_lengths[i] = text.size(0)
 
-        return (
+        return ssl_padded, ssl_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, text_padded, text_lengths
-            ssl_padded,
-            ssl_lengths,
-            spec_padded,
-            spec_lengths,
-            wav_padded,
-            wav_lengths,
-            text_padded,
-            text_lengths,
-        )
 
 
 class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
@@ -268,18 +244,9 @@ class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
     Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
     """
 
-    def __init__(
+    def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
-        self,
-        dataset,
-        batch_size,
-        boundaries,
-        num_replicas=None,
-        rank=None,
-        shuffle=True,
-    ):
         super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
         self.lengths = dataset.lengths
-        # print(233333333333333,self.lengths,dir(dataset))
         self.batch_size = batch_size
         self.boundaries = boundaries
 
@@ -295,24 +262,22 @@ class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
             if idx_bucket != -1:
                 buckets[idx_bucket].append(i)
 
-        for i in range(len(buckets) - 1, 0, -1):
+        i = len(buckets) - 1
-            # for i in range(len(buckets) - 1, -1, -1):
+        while i >= 0:
             if len(buckets[i]) == 0:
                 buckets.pop(i)
                 self.boundaries.pop(i + 1)
+            i -= 1
 
         num_samples_per_bucket = []
         for i in range(len(buckets)):
             len_bucket = len(buckets[i])
             total_batch_size = self.num_replicas * self.batch_size
-            rem = (
+            rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
-                total_batch_size - (len_bucket % total_batch_size)
-            ) % total_batch_size
             num_samples_per_bucket.append(len_bucket + rem)
         return buckets, num_samples_per_bucket
 
     def __iter__(self):
-        # deterministically shuffle based on epoch
         g = torch.Generator()
         g.manual_seed(self.epoch)
 
@@ -331,25 +296,13 @@ class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
             ids_bucket = indices[i]
             num_samples_bucket = self.num_samples_per_bucket[i]
 
-            # add extra samples to make it evenly divisible
             rem = num_samples_bucket - len_bucket
-            ids_bucket = (
+            ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
-                ids_bucket
-                + ids_bucket * (rem // len_bucket)
-                + ids_bucket[: (rem % len_bucket)]
-            )
 
-            # subsample
             ids_bucket = ids_bucket[self.rank::self.num_replicas]
 
-            # batching
             for j in range(len(ids_bucket) // self.batch_size):
-                batch = [
+                batch = [bucket[idx] for idx in ids_bucket[j * self.batch_size:(j + 1) * self.batch_size]]
-                    bucket[idx]
-                    for idx in ids_bucket[
-                        j * self.batch_size : (j + 1) * self.batch_size
-                    ]
-                ]
                 batches.append(batch)
 
         if self.shuffle: