sapinsapin/filipinospeechcorpus
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How to use sapinsapin/speecht5_tts-fsc with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-to-speech", model="sapinsapin/speecht5_tts-fsc") # Load model directly
from transformers import AutoProcessor, AutoModelForTextToSpectrogram
processor = AutoProcessor.from_pretrained("sapinsapin/speecht5_tts-fsc")
model = AutoModelForTextToSpectrogram.from_pretrained("sapinsapin/speecht5_tts-fsc", device_map="auto")microsoft/speecht5_tts finetuned for Filipino
(Tagalog/Taglish) on
sapinsapin/filipinospeechcorpus.
Trained for 1000 steps on 1867 read-speech clips (batch 4x8, lr 1e-05, fp32 + gradient checkpointing). Synthesized listen-test samples are in samples/ (speechbrain x-vector speaker conditioning + microsoft/speecht5_hifigan vocoder).
| metric | value |
|---|---|
| eval_loss | 0.4432 |
Trained with finetune_tts.py from the
halohalo pipeline; the dataset
adapter normalizes each corpus to (audio@16k, text, speaker_id) so corpora
are swappable with a --dataset flag.
Base model
microsoft/speecht5_tts