Cey 25 Mms Tts Mozilla Nmz
Fine-tuned facebook/mms-tts-nmz on Mozilla Data Collective data for Nawdem (nmz).
Source du modèle
- Type
- Other
- Taille du fichier
- 139 MB
- Modèle de base
- facebook/mms-tts-nmz
- Version
- vRevision 1c28258
- Mis à jour
- 21 sept. 2026
- Téléchargements
- 5
- Tâches
- Audio
- Source
- Hugging Face
Description de la source
MMS-TTS Nawdem — Mozilla Fine-tuned
Fine-tuned facebook/mms-tts-nmz on Mozilla Data Collective data for Nawdem (nmz).
Training Statistics
| Metric | Value |
|---|---|
| Training samples | 14070 |
| Validation samples | 2483 |
| Best val mel-L1 | 4.0182 |
| Dataset source | Mozilla Data Collective |
Usage
from transformers import VitsModel, VitsTokenizer
import torch, torchaudio
model = VitsModel.from_pretrained("Umbaji/cey-25-mms-tts-mozilla-nmz")
tokenizer = VitsTokenizer.from_pretrained("Umbaji/cey-25-mms-tts-mozilla-nmz")
inputs = tokenizer("your text here", return_tensors="pt")
with torch.no_grad():
waveform = model(**inputs).waveform[0]
torchaudio.save("out.wav", waveform.unsqueeze(0), model.config.sampling_rate)
Training
- Loss: Mel-spectrogram L1
- Optimizer: AdamW (lr=2e-4, betas=(0.8, 0.99))
- Scheduler: ExponentialLR γ=0.999
- Epochs: 1 | Effective batch: 16
Fine-tuned: 2026-06-12 — Mozilla Data Collective + Eyaa-Tom
Sources
1 source
Sources
1 sourceVérifié 21 sept.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
2 extraits
Extraits de sources
2 extraits--- language: - nmz tags: - text-to-speech - mms-tts - vits - nmz - west-africa - mozilla-data-collective - finetuned license: cc-by-nc-4.0 base_model: facebook/mms-tts-nmz pipeline_tag: text-to-speech --- # MMS-TTS Nawdem — Mozilla Fine-tuned Fine-tuned [facebook/mms-tts-nmz](https://huggingface.co/facebook/mms-tts-nmz) on Mozilla Data Collective data for **Nawdem** (`nmz`). ## Training Statistics | Metric | Value | |--------|-------| | Training samples | 14070 | | Validation samples | 2483 | | Best val mel-L1 | 4.0182 | | Dataset source | Mozilla Data Collective | ## Usage ```python from transformers import VitsModel, VitsTokenizer import torch, torchaudio model = VitsModel.from_pretrained("Umbaji/cey-25-mms-tts-mozilla-nmz") tokenizer = VitsTokenizer.from_pretrained("Umbaji/cey-25-mms-tts-mozilla-nmz") inputs = tokenizer("your text here", return_tensors="pt") with torch.no_grad(): waveform = model(**inputs).waveform[0] torchaudio.save("out.wav", waveform.unsqueeze(0), model.config.sampling_rate) ``` ## Training - **Loss**: Mel-spectrogram L1 - **Optimizer**: AdamW (lr=2e-4, betas=(0.8, 0.99)) - **Scheduler**: ExponentialLR γ=0.999 - **Epochs**: 1 | **Effective batch**: 16 *Fine-tuned: 2026-06-12 — Mozilla Data Collective + Eyaa-Tom*
Source context: 5 downloads · 0 likes · Pipeline text-to-speech · Repo Umbaji/cey-25-mms-tts-mozilla-nmz
