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Add ALYS + voice provider credits (#6)
Browse files- Add ALYS + voice provider credits (49a207cd9b3b36417f51889fdff3c21440ee4c2d)
Co-authored-by: Aster <Asteriski@users.noreply.huggingface.co>
- checkpoints/ALYS.ckpt +3 -0
- config.yaml +14 -6
- configs/ALYS.py +48 -0
checkpoints/ALYS.ckpt
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version https://git-lfs.github.com/spec/v1
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oid sha256:475fb4b9f56f8d14812ee78e4d5b39b2e58f60d8d0350e84587ed21a9ba96fca
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size 409439345
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config.yaml
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checkpoint: checkpoints/Kiritan.ckpt
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readme: |
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This model is trained on the Tohoku Kiritan dataset and released under the [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.
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It has a cute, yet powerful voice.
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default_speaker: "kiritan"
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- name: "Tohoku Itako (Feminine)"
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checkpoint: checkpoints/Itako.ckpt
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readme: |
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This model is trained on the Tohoku Itako dataset and released under the [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.
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It has a bright and whispery voice.
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default_speaker: "itako"
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- name: "No.7 (Feminine)"
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checkpoint: checkpoints/Seven.ckpt
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readme: |
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This model is trained on the No.7 dataset and released under the [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.
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It has a strong and sharp voice.
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default_speaker: "seven"
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- name: "Yoko (Feminine)"
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checkpoint: checkpoints/Ritsu.ckpt
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readme: |
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This model is trained on the Namine Ritsu ENUNU Dataset and released under the [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.
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It has a powerful and throaty voice.
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default_speaker: "ritsu"
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- name: "S (Masculine)"
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checkpoint: checkpoints/Azure.ckpt
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readme: |
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This model is trained on a dataset known as Azure Cobalt and released under the [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.
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It has a stable, mature voice.
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default_speaker: "azure"
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checkpoint: checkpoints/Kiritan.ckpt
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readme: |
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This model is trained on the Tohoku Kiritan dataset and released under the [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.
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It has a cute, yet powerful voice. CV: Akaneya Himika
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default_speaker: "kiritan"
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- name: "Tohoku Itako (Feminine)"
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checkpoint: checkpoints/Itako.ckpt
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readme: |
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This model is trained on the Tohoku Itako dataset and released under the [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.
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It has a bright and whispery voice. CV: Kido Ibuki
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default_speaker: "itako"
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- name: "No.7 (Feminine)"
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checkpoint: checkpoints/Seven.ckpt
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readme: |
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This model is trained on the No.7 dataset and released under the [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.
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It has a strong and sharp voice. CV: Koiwai Kotori
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default_speaker: "seven"
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- name: "Yoko (Feminine)"
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checkpoint: checkpoints/Ritsu.ckpt
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readme: |
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This model is trained on the Namine Ritsu ENUNU Dataset and released under the [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.
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It has a powerful and throaty voice. CV: Canon
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default_speaker: "ritsu"
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- name: "S (Masculine)"
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checkpoint: checkpoints/Azure.ckpt
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readme: |
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This model is trained on a dataset known as Azure Cobalt and released under the [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.
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It has a stable, mature voice. CV: Aster
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default_speaker: "azure"
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- name: "ALYS (Feminine)"
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config: configs/ALYS.py
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checkpoint: checkpoints/ALYS.ckpt
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readme: |
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This model is trained on the ALYS DB 001 JPN dataset, originally produced by Voxwave and released under the [GPL-3.0](https://choosealicense.com/licenses/gpl-3.0/) license.
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It has a slightly soft voice. CV: Poucet
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default_speaker: "ALYS"
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configs/ALYS.py
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from fish_diffusion.datasets.hifisinger import HiFiSVCDataset
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from fish_diffusion.datasets.utils import get_datasets_from_subfolder
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_base_ = [
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"./_base_/archs/hifi_svc.py",
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"./_base_/trainers/base.py",
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"./_base_/schedulers/exponential.py",
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"./_base_/datasets/hifi_svc.py",
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]
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speaker_mapping = {
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"ALYS": 0,
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}
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model = dict(
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type="HiFiSVC",
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speaker_encoder=dict(
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input_size=len(speaker_mapping),
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),
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)
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preprocessing = dict(
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text_features_extractor=dict(
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type="ContentVec",
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),
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pitch_extractor=dict(
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type="CrepePitchExtractor",
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keep_zeros=False,
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f0_min=40.0,
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f0_max=1600.0,
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),
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energy_extractor=dict(
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type="RMSEnergyExtractor",
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),
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augmentations=[
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dict(
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type="FixedPitchShifting",
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key_shifts=[-5.0, 5.0],
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probability=0.75,
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),
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],
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)
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trainer = dict(
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# Disable gradient clipping, which is not supported by custom optimization
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gradient_clip_val=None,
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max_steps=1000000,
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)
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