# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AOLCDROM/XTTSv2-Hi_ft", dtype="auto")Quick Links
XTTSv2 checkpoints finedtuned with the forked Coqui TTS (https://github.com/idiap/coqui-ai-TTS) for Hindi speech
Trained using the Indic TTS Database (https://www.iitm.ac.in/donlab/tts/) and Mozilla Common Voice 18.0 Hindi dataset (https://commonvoice.mozilla.org/en/datasets)
Rename checkpoint to model.pth and replace original XTTSv2 model, or use according to how your implementation of XTTSv2.
The checkpoints with the highest step count may not be the best. I think the best quality output here is best_model_43036.pth Reference speaker audio files are in ./speakers-hi of this repo
Use language code 'hi' at inference for Hindi speech, use language code 'hi' with English text to generate English with the learned Hindi pronounciations.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="AOLCDROM/XTTSv2-Hi_ft")