Upload usage.py

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from transformers import VitsModel, AutoTokenizer
import torch
import scipy
import numpy as np

model = VitsModel.from_pretrained("indonesian-tts")
tokenizer = AutoTokenizer.from_pretrained("indonesian-tts")
text ="Saya menelepon dari kantor pajak."
inputs = tokenizer(text, return_tensors="pt")

with torch.no_grad():
 output = model(**inputs).waveform
output_np = output.squeeze().numpy()  # Remove extra dimensions
output_np = output_np / np.max(np.abs(output_np))  # Normalize to prevent clipping
            
scipy.io.wavfile.write(
                "indonesian.wav",  # Better file naming with padding
                rate=model.config.sampling_rate,
                data=output_np
                )
        

	from transformers import VitsModel, AutoTokenizer
	import torch
	import scipy
	import numpy as np

	model = VitsModel.from_pretrained("indonesian-tts")
	tokenizer = AutoTokenizer.from_pretrained("indonesian-tts")
	text ="Saya menelepon dari kantor pajak."
	inputs = tokenizer(text, return_tensors="pt")

	with torch.no_grad():
	output = model(**inputs).waveform
	output_np = output.squeeze().numpy() # Remove extra dimensions
	output_np = output_np / np.max(np.abs(output_np)) # Normalize to prevent clipping

	scipy.io.wavfile.write(
	"indonesian.wav", # Better file naming with padding
	rate=model.config.sampling_rate,
	data=output_np
	)