Text-to-Speech
NeMo
Ganda
tts
fastpitch
hifigan
luganda
african-languages
low-resource
edge
on-device
Instructions to use Cal3bd3v/ganda-nemo-experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use Cal3bd3v/ganda-nemo-experimental with NeMo:
# tag did not correspond to a valid NeMo domain.
- Notebooks
- Google Colab
- Kaggle
Transfer from cxlrd/luganda-tts-v3
Browse files- .gitattributes +2 -0
- README.md +34 -0
- luganda_fastpitch.nemo +3 -0
- luganda_hifigan.nemo +3 -0
.gitattributes
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luganda_fastpitch.nemo filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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language: lg
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license: apache-2.0
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tags: [text-to-speech, tts, nemo, luganda, african-languages]
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datasets: [Sunbird/salt]
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---
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# Luganda TTS v3
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Text-to-Speech system for Luganda using NVIDIA NeMo.
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## Models
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| Model | Description | Size |
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|-------|-------------|------|
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| `luganda_fastpitch.nemo` | FastPitch spectrogram generator | 187 MB |
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| `luganda_hifigan.nemo` | HiFi-GAN neural vocoder | 339 MB |
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## Training
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- **Dataset**: Sunbird/salt (~2,380 samples, 2.69 hours)
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- **FastPitch**: 20,000 steps
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- **HiFi-GAN**: 20,000 steps
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- **Sample Rate**: 22,050 Hz
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## Usage
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```python
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from nemo.collections.tts.models import FastPitchModel, HifiGanModel
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fastpitch = FastPitchModel.restore_from("luganda_fastpitch.nemo")
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hifigan = HifiGanModel.restore_from("luganda_hifigan.nemo")
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text = "Oli otya?"
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spec = fastpitch.generate_spectrogram(tokens=fastpitch.parse(text))
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audio = hifigan.convert_spectrogram_to_audio(spec=spec)
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```
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version https://git-lfs.github.com/spec/v1
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oid sha256:1eddc12da7d377bb3bf1dc0dc3a761dc86a0f4bdd153577faf528c1d1e23d759
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size 187023360
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luganda_hifigan.nemo
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version https://git-lfs.github.com/spec/v1
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oid sha256:d9d19d517bcb3042fe3c776fe151894f98e3ae96825f1d14b0c62b81a69e0681
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size 339210240
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