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svc_cn_hubert_soft_finetune.py

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+from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
+
+from fish_diffusion.datasets.audio_folder import AudioFolderDataset
+
+_base_ = [
+    "./_base_/archs/diff_svc_v2.py",
+    "./_base_/trainers/base.py",
+    "./_base_/schedulers/warmup_cosine_finetune.py",
+    "./_base_/datasets/audio_folder.py",
+]
+
+speaker_mapping = {
+    "Placeholder": 0,
+}
+
+dataset = dict(
+    train=dict(
+        _delete_=True,  # Delete the default train dataset
+        type="ConcatDataset",
+        datasets=[
+            dict(
+                type="AudioFolderDataset",
+                path="dataset/train",
+                speaker_id=speaker_mapping["Placeholder"],
+            ),
+        ],
+        # Are there any other ways to do this?
+        collate_fn=AudioFolderDataset.collate_fn,
+    ),
+    valid=dict(
+        _delete_=True,  # Delete the default valid dataset
+        type="ConcatDataset",
+        datasets=[
+            dict(
+                type="AudioFolderDataset",
+                path="dataset/valid",
+                speaker_id=speaker_mapping["Placeholder"],
+            ),
+        ],
+        collate_fn=AudioFolderDataset.collate_fn,
+    ),
+)
+
+model = dict(
+    speaker_encoder=dict(
+        input_size=len(speaker_mapping),
+    ),
+    text_encoder=dict(
+        type="NaiveProjectionEncoder",
+        input_size=256,
+        output_size=256,
+    ),
+)
+
+preprocessing = dict(
+    text_features_extractor=dict(
+        type="ChineseHubertSoft",
+        pretrained=True,
+        gate_size=25,
+    ),
+    pitch_extractor=dict(
+        type="ParselMouthPitchExtractor",
+    ),
+)
+
+# The following trainer val and save checkpoints every 1000 steps
+trainer = dict(
+    val_check_interval=1000,
+    callbacks=[
+        ModelCheckpoint(
+            filename="{epoch}-{step}-{valid_loss:.2f}",
+            every_n_train_steps=5000,
+            save_top_k=-1,
+        ),
+        LearningRateMonitor(logging_interval="step"),
+    ],
+)

svc_cn_hubert_soft_finetune_crepe.py

@@ -0,0 +1,77 @@
+from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
+
+from fish_diffusion.datasets.audio_folder import AudioFolderDataset
+
+_base_ = [
+    "./_base_/archs/diff_svc_v2.py",
+    "./_base_/trainers/base.py",
+    "./_base_/schedulers/warmup_cosine_finetune.py",
+    "./_base_/datasets/audio_folder.py",
+]
+
+speaker_mapping = {
+    "Placeholder": 0,
+}
+
+dataset = dict(
+    train=dict(
+        _delete_=True,  # Delete the default train dataset
+        type="ConcatDataset",
+        datasets=[
+            dict(
+                type="AudioFolderDataset",
+                path="dataset/train",
+                speaker_id=speaker_mapping["Placeholder"],
+            ),
+        ],
+        # Are there any other ways to do this?
+        collate_fn=AudioFolderDataset.collate_fn,
+    ),
+    valid=dict(
+        _delete_=True,  # Delete the default valid dataset
+        type="ConcatDataset",
+        datasets=[
+            dict(
+                type="AudioFolderDataset",
+                path="dataset/valid",
+                speaker_id=speaker_mapping["Placeholder"],
+            ),
+        ],
+        collate_fn=AudioFolderDataset.collate_fn,
+    ),
+)
+
+model = dict(
+    speaker_encoder=dict(
+        input_size=len(speaker_mapping),
+    ),
+    text_encoder=dict(
+        type="NaiveProjectionEncoder",
+        input_size=256,
+        output_size=256,
+    ),
+)
+
+preprocessing = dict(
+    text_features_extractor=dict(
+        type="ChineseHubertSoft",
+        pretrained=True,
+        gate_size=25,
+    ),
+    pitch_extractor=dict(
+        type="CrepePitchExtractor",
+    ),
+)
+
+# The following trainer val and save checkpoints every 1000 steps
+trainer = dict(
+    val_check_interval=1000,
+    callbacks=[
+        ModelCheckpoint(
+            filename="{epoch}-{step}-{valid_loss:.2f}",
+            every_n_train_steps=5000,
+            save_top_k=-1,
+        ),
+        LearningRateMonitor(logging_interval="step"),
+    ],
+)