File size: 5,171 Bytes
216b2d7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 | ---
language:
- lg
license: cc-by-sa-4.0
library_name: everyvoice
tags:
- text-to-speech
- tts
- everyvoice
- fastspeech2
- open-bible
- luganda
pipeline_tag: text-to-speech
datasets:
- davidguzmanr/open-bible-resources
inference: false
---
# EveryVoice Open Bible — Luganda
A multispeaker text-to-speech model for **Luganda**, trained from scratch on
the [Open Bible](https://huggingface.co/datasets/davidguzmanr/open-bible-resources)
corpus using the [EveryVoice](https://github.com/EveryVoiceTTS/EveryVoice) TTS toolkit
(FastSpeech2 acoustic model + HiFi-GAN vocoder, 22,050 Hz output).
The model is conditioned on speaker embeddings learned during training. A speaker
name from the training set must be supplied at inference time.
## Files
| File | Purpose |
|------|---------|
| `feature_prediction.ckpt` | Trained FastSpeech2 feature-prediction weights. |
| `vocoder.ckpt` | HiFi-GAN vocoder checkpoint (optional — can be replaced with a universal vocoder). |
| `config/` | EveryVoice YAML config files (shared data, text, feature-prediction, spec-to-wav). |
| `filelist.psv` | Pipe-separated training filelist (`basename|language|speaker|characters|phones`). |
## Intended use
- Multispeaker TTS for Luganda using one of the training-set speaker voices.
- Research on multilingual TTS, low-resource TTS evaluation, and listening
studies on Open Bible–style read-speech.
## How to use
Install EveryVoice:
```bash
pip install everyvoice
```
Download the checkpoint and run inference:
```python
import torch
from pathlib import Path
from huggingface_hub import snapshot_download
from everyvoice.config.type_definitions import DatasetTextRepresentation
from everyvoice.model.feature_prediction.FastSpeech2_lightning.fs2.cli.synthesize import (
get_global_step,
synthesize_helper,
)
from everyvoice.model.feature_prediction.FastSpeech2_lightning.fs2.model import FastSpeech2
from everyvoice.model.feature_prediction.FastSpeech2_lightning.fs2.type_definitions import (
SynthesizeOutputFormats,
)
from everyvoice.model.vocoder.HiFiGAN_iSTFT_lightning.hfgl.utils import (
load_hifigan_from_checkpoint,
)
from everyvoice.utils.heavy import get_device_from_accelerator
repo_id = "multilingual-tts/EveryVoice-OpenBible-Luganda"
local = Path(snapshot_download(repo_id))
ckpt_path = local / "feature_prediction.ckpt"
vocoder_path = local / "vocoder.ckpt"
accelerator = "gpu" if torch.cuda.is_available() else "cpu"
device = get_device_from_accelerator(accelerator)
model = FastSpeech2.load_from_checkpoint(str(ckpt_path)).to(device)
model.eval()
global_step = get_global_step(ckpt_path)
vocoder_ckpt = torch.load(str(vocoder_path), map_location=device, weights_only=True)
vocoder_model, vocoder_config = load_hifigan_from_checkpoint(vocoder_ckpt, device)
vocoder_global_step = get_global_step(vocoder_path)
# Pick any speaker from the model
speaker = next(iter(model.speaker2id.keys()))
language = next(iter(model.lang2id.keys()))
print(f"Available speakers: {list(model.speaker2id.keys())}")
filelist_data = [
{
"basename": "sample-0",
"characters": "...", # text to synthesise in Luganda
"language": language,
"speaker": speaker,
"duration_control": 1.0,
}
]
output_dir = Path("everyvoice_output")
output_dir.mkdir(exist_ok=True)
synthesize_helper(
model=model,
texts=None,
style_reference=None,
language=None,
speaker=None,
duration_control=1.0,
global_step=global_step,
output_type=[SynthesizeOutputFormats.wav],
text_representation=DatasetTextRepresentation.characters,
accelerator=accelerator,
devices="auto",
device=device,
batch_size=1,
num_workers=1,
filelist=None,
filelist_data=filelist_data,
output_dir=output_dir,
teacher_forcing_directory=None,
vocoder_model=vocoder_model,
vocoder_config=vocoder_config,
vocoder_global_step=vocoder_global_step,
)
# Generated WAVs land in output_dir/wav/
```
## Training data
- **Source:** `davidguzmanr/open-bible-resources`, config `Luganda`
- **Size:** approximately 21,553 utterances
- **Speakers:** multispeaker; speaker identity is fixed to one of the training-set
voices and selected by name at inference time
- **Sample rate:** 22,050 Hz
## Training procedure
- Acoustic model: FastSpeech2 (non-autoregressive, duration-prediction based).
- Vocoder: HiFi-GAN (iSTFT variant).
- Character-level tokenizer built from the training transcripts.
- Trained with the [EveryVoice](https://github.com/EveryVoiceTTS/EveryVoice) toolkit.
Audio preprocessing and training are reproducible via the upstream
[open-bible-models](https://github.com/davidguzmanr/open-bible-models) repo.
## Evaluation
Evaluated alongside other Open-Bible TTS systems on character/word error rate
(via Meta's Omnilingual ASR) and UTMOSv2 naturalness scores. See the
[open-bible-models](https://github.com/davidguzmanr/open-bible-models) repository
for the evaluation pipeline and the
[open-bible-surveys](https://github.com/davidguzmanr/open-bible-surveys) repository
for the human-listening survey methodology.
|