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speecht5_finetuned_voxpopuli_nl

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speecht5_finetuned_voxpopuli_nl / app.py

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	import torch
	from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
	from urllib.request import urlopen
	from io import BytesIO
	import soundfile as sf
	import numpy as np

	# Load the TTS model from the Hugging Face Hub
	model_name = "jjyaoao/speecht5_finetuned_voxpopuli_nl" # Replace with your actual model name
	model = Wav2Vec2ForCTC.from_pretrained(model_name)
	tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name)

	# Buckwalter to Unicode mapping
	buck2uni = {
	u"\u0627": "A",
	u"\u0675": "A",
	u"\u0673": "A",
	u"\u0630": "A",
	u"\u0622": "AA",
	u"\u0628": "B",
	u"\u067E": "P",
	u"\u062A": "T",
	u"\u0637": "T",
	u"\u0679": "T",
	u"\u062C": "J",
	u"\u0633": "S",
	u"\u062B": "S",
	u"\u0635": "S",
	u"\u0686": "CH",
	u"\u062D": "H",
	u"\u0647": "H",
	u"\u0629": "H",
	u"\u06DF": "H",
	u"\u062E": "KH",
	u"\u062F": "D",
	u"\u0688": "D",
	u"\u0630": "Z",
	u"\u0632": "Z",
	u"\u0636": "Z",
	u"\u0638": "Z",
	u"\u068E": "Z",
	u"\u0631": "R",
	u"\u0691": "R",
	u"\u0634": "SH",
	u"\u063A": "GH",
	u"\u0641": "F",
	u"\u06A9": "K",
	u"\u0642": "K",
	u"\u06AF": "G",
	u"\u0644": "L",
	u"\u0645": "M",
	u"\u0646": "N",
	u"\u06BA": "N",
	u"\u0648": "O",
	u"\u0649": "Y",
	u"\u0626": "Y",
	u"\u06CC": "Y",
	u"\u06D2": "E",
	u"\u06C1": "H",
	u"\u064A": "E",
	u"\u06C2": "AH",
	u"\u06BE": "H",
	u"\u0639": "A",
	u"\u0643": "K",
	u"\u0621": "A",
	u"\u0624": "O",
	u"\u060C": "", # separator ulta comma
	}

	def transString(string, reverse=0):
	"""Given a Unicode string, transliterate into Buckwalter. To go from
	Buckwalter back to Unicode, set reverse=1"""
	for k, v in buck2uni.items():
	if not reverse:
	string = string.replace(k, v)
	else:
	string = string.replace(v, k)
	return string


	def generate_audio(text):
	# Convert input text to Roman Urdu
	roman_urdu = transString(text)

	# Tokenize the input text
	inputs = tokenizer(roman_urdu, return_tensors="pt").input_values

	# Generate speech from the model
	with torch.no_grad():
	logits = model(inputs).logits

	# Convert logits to audio waveform
	predicted_ids = torch.argmax(logits, dim=-1)
	audio = tokenizer.decode(predicted_ids[0], skip_special_tokens=True)

	return audio


	# Example usage
	def main():
	# Get input text in Urdu
	input_text_urdu = input("Enter text in Urdu: ")

	# Generate audio
	audio_output = generate_audio(input_text_urdu)

	# Save audio as a .wav file
	sf.write("output.wav", audio_output, samplerate=22050)

	print("Audio generated and saved as 'output.wav'")


	if __name__ == "__main__":
	main()