rinflan commited on
Commit
2bb6707
1 Parent(s): 05de822

Upload 2 files

Browse files
svc_cn_hubert_soft_finetune.py ADDED
@@ -0,0 +1,77 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
2
+
3
+ from fish_diffusion.datasets.audio_folder import AudioFolderDataset
4
+
5
+ _base_ = [
6
+ "./_base_/archs/diff_svc_v2.py",
7
+ "./_base_/trainers/base.py",
8
+ "./_base_/schedulers/warmup_cosine_finetune.py",
9
+ "./_base_/datasets/audio_folder.py",
10
+ ]
11
+
12
+ speaker_mapping = {
13
+ "Placeholder": 0,
14
+ }
15
+
16
+ dataset = dict(
17
+ train=dict(
18
+ _delete_=True, # Delete the default train dataset
19
+ type="ConcatDataset",
20
+ datasets=[
21
+ dict(
22
+ type="AudioFolderDataset",
23
+ path="dataset/train",
24
+ speaker_id=speaker_mapping["Placeholder"],
25
+ ),
26
+ ],
27
+ # Are there any other ways to do this?
28
+ collate_fn=AudioFolderDataset.collate_fn,
29
+ ),
30
+ valid=dict(
31
+ _delete_=True, # Delete the default valid dataset
32
+ type="ConcatDataset",
33
+ datasets=[
34
+ dict(
35
+ type="AudioFolderDataset",
36
+ path="dataset/valid",
37
+ speaker_id=speaker_mapping["Placeholder"],
38
+ ),
39
+ ],
40
+ collate_fn=AudioFolderDataset.collate_fn,
41
+ ),
42
+ )
43
+
44
+ model = dict(
45
+ speaker_encoder=dict(
46
+ input_size=len(speaker_mapping),
47
+ ),
48
+ text_encoder=dict(
49
+ type="NaiveProjectionEncoder",
50
+ input_size=256,
51
+ output_size=256,
52
+ ),
53
+ )
54
+
55
+ preprocessing = dict(
56
+ text_features_extractor=dict(
57
+ type="ChineseHubertSoft",
58
+ pretrained=True,
59
+ gate_size=25,
60
+ ),
61
+ pitch_extractor=dict(
62
+ type="ParselMouthPitchExtractor",
63
+ ),
64
+ )
65
+
66
+ # The following trainer val and save checkpoints every 1000 steps
67
+ trainer = dict(
68
+ val_check_interval=1000,
69
+ callbacks=[
70
+ ModelCheckpoint(
71
+ filename="{epoch}-{step}-{valid_loss:.2f}",
72
+ every_n_train_steps=5000,
73
+ save_top_k=-1,
74
+ ),
75
+ LearningRateMonitor(logging_interval="step"),
76
+ ],
77
+ )
svc_cn_hubert_soft_finetune_crepe.py ADDED
@@ -0,0 +1,77 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
2
+
3
+ from fish_diffusion.datasets.audio_folder import AudioFolderDataset
4
+
5
+ _base_ = [
6
+ "./_base_/archs/diff_svc_v2.py",
7
+ "./_base_/trainers/base.py",
8
+ "./_base_/schedulers/warmup_cosine_finetune.py",
9
+ "./_base_/datasets/audio_folder.py",
10
+ ]
11
+
12
+ speaker_mapping = {
13
+ "Placeholder": 0,
14
+ }
15
+
16
+ dataset = dict(
17
+ train=dict(
18
+ _delete_=True, # Delete the default train dataset
19
+ type="ConcatDataset",
20
+ datasets=[
21
+ dict(
22
+ type="AudioFolderDataset",
23
+ path="dataset/train",
24
+ speaker_id=speaker_mapping["Placeholder"],
25
+ ),
26
+ ],
27
+ # Are there any other ways to do this?
28
+ collate_fn=AudioFolderDataset.collate_fn,
29
+ ),
30
+ valid=dict(
31
+ _delete_=True, # Delete the default valid dataset
32
+ type="ConcatDataset",
33
+ datasets=[
34
+ dict(
35
+ type="AudioFolderDataset",
36
+ path="dataset/valid",
37
+ speaker_id=speaker_mapping["Placeholder"],
38
+ ),
39
+ ],
40
+ collate_fn=AudioFolderDataset.collate_fn,
41
+ ),
42
+ )
43
+
44
+ model = dict(
45
+ speaker_encoder=dict(
46
+ input_size=len(speaker_mapping),
47
+ ),
48
+ text_encoder=dict(
49
+ type="NaiveProjectionEncoder",
50
+ input_size=256,
51
+ output_size=256,
52
+ ),
53
+ )
54
+
55
+ preprocessing = dict(
56
+ text_features_extractor=dict(
57
+ type="ChineseHubertSoft",
58
+ pretrained=True,
59
+ gate_size=25,
60
+ ),
61
+ pitch_extractor=dict(
62
+ type="CrepePitchExtractor",
63
+ ),
64
+ )
65
+
66
+ # The following trainer val and save checkpoints every 1000 steps
67
+ trainer = dict(
68
+ val_check_interval=1000,
69
+ callbacks=[
70
+ ModelCheckpoint(
71
+ filename="{epoch}-{step}-{valid_loss:.2f}",
72
+ every_n_train_steps=5000,
73
+ save_top_k=-1,
74
+ ),
75
+ LearningRateMonitor(logging_interval="step"),
76
+ ],
77
+ )