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import torch
from transformers import SpeechT5ForTextToSpeech, SpeechT5Processor, SpeechT5HifiGan
import soundfile as sf
import gradio as gr
import scipy.io.wavfile as wav
import numpy as np
import wave
from datasets import load_dataset, Audio, config
from IPython.display import Audio
# Load the TTS model from the Hugging Face Hub
checkpoint = "Abdullah-Habib/urdu_speech_tt" # Replace with your actual model name
processor = SpeechT5Processor.from_pretrained(checkpoint)
model = SpeechT5ForTextToSpeech.from_pretrained(checkpoint)
tokenizer = processor.tokenizer
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
# Buckwalter to Unicode mapping
buck2uni = {
u"\u0627":"a",
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":"" #seperator 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 = processor(text=roman_urdu, return_tensors="pt", type = "numpy")
# Generate audio from the SpeechT5 model
# speaker_embeddings = torch.tensor(np.load("speaker_embeddings.npy"))
speaker_embeddings = torch.load("speaker_embeddings_29.pt")
# speaker_embeddings= torch.tensor([[-0.0917, -0.0461, 0.0347, 0.0341, 0.0197, -0.0438, -0.0377, -0.0212, 0.0361, 0.0220, -0.0676, -0.0731, 0.0827, 0.0132, 0.0187, 0.0577, -0.0026, 0.0618, 0.0088, 0.0159, 0.0344, 0.0243, -0.0164, -0.0430, -0.0556, -0.0044, -0.0413, -0.0003, 0.0310, 0.0369, -0.0034, 0.0424, 0.0474, 0.0102, 0.0392, -0.0611, 0.0405, 0.0652, -0.0386, -0.0638, 0.0255, -0.0411, 0.0398, 0.0490, 0.0297, -0.1218, -0.0206, 0.0146,-0.0649, 0.0550, 0.0177, 0.0407, 0.0017, -0.0113, -0.0990, -0.0015,0.0158, 0.0481, 0.0286, 0.0300, 0.0346, -0.0104, -0.0142, -0.0005,0.0264, 0.0412, 0.0227, -0.0389, -0.0489, -0.0750, 0.0238, 0.0101,0.0171, 0.0141, 0.0224, 0.0344, 0.0402, 0.0336, -0.0641, -0.0818, -0.0731, -0.0470, -0.0512, -0.0602, -0.0344, -0.0442, -0.0541, 0.0097, 0.0198, 0.0482, 0.0323, -0.0885, 0.0210, -0.0798, 0.0417, -0.0436, 0.0402, 0.0256, -0.0641, -0.0668, -0.0023, -0.0706, -0.0928, 0.0121, 0.0355, -0.0376, 0.0522, 0.0482, 0.0200, 0.0290, -0.0698, -0.0232, 0.0878, 0.0044, 0.0559, 0.0581, -0.0718, 0.0095, -0.0538, 0.0125, 0.0023, -0.0562, 0.0424, 0.0261, -0.0498, 0.0255, -0.0840, 0.0331, 0.0406, 0.0162, -0.0522, 0.0218, 0.0323, 0.0359, 0.0128, -0.0891, -0.0569, 0.0031, -0.0694, -0.0102, 0.0118, 0.0033, 0.0127, 0.0589, -0.0783, 0.0179, 0.0200, -0.0371, 0.0325, -0.1033, 0.0483, -0.0343, -0.0714, 0.0102, 0.0665, 0.0278, 0.0285, -0.0653, -0.0834, 0.0196, 0.0399, 0.0085, 0.0246, -0.0400, 0.0215, 0.0083, 0.0302, 0.0204, 0.0360, 0.0309, -0.0306, -0.0828, 0.0142, -0.0614, -0.0103, 0.0372, -0.0456, 0.0291, 0.0565, -0.0271, 0.0518, -0.0671, 0.0012, -0.0048, -0.0565, -0.0092, 0.0336, 0.0476, -0.0351, -0.0698, 0.0487, 0.0313, -0.0491, 0.0401, 0.0246, 0.0178, 0.0405, 0.0012, 0.0311, -0.0041, 0.0367, 0.0330, -0.0609, 0.0099, -0.0097, 0.0173, 0.0494, -0.0305, 0.0272, -0.0349, 0.0025, -0.0697, -0.0414, 0.0604, -0.0707, 0.0420, 0.0380, -0.0731, 0.0546, 0.0339, -0.0758, 0.0365, -0.0712, -0.0140, 0.0365, 0.0477, 0.0796, 0.0572, 0.0212, 0.0098, 0.0133, 0.0261, 0.0329, -0.0269, 0.0437, -0.0359, 0.0296, 0.0180, -0.0008, 0.0668, -0.0448, 0.0269, -0.0734, 0.0194, -0.0494, 0.0432, 0.0449, 0.0442, 0.0389, 0.0530, 0.0420, 0.0021, 0.0084, -0.0820, -0.0081, 0.0326, 0.0265, 0.0536, -0.0714, 0.0188, 0.0298, -0.0737, 0.0110, 0.0340, 0.0016, 0.0262, 0.0179, 0.0109, 0.0426, -0.0538, 0.0649, 0.0160, 0.0146, -0.0419, -0.0851, 0.0138, 0.0399, 0.0445, -0.0849, -0.0425, 0.0293, 0.0477, 0.0108, -0.0941, -0.0386, 0.0600, 0.0089, 0.0557,-0.0892, 0.0026, 0.0192, 0.0136, -0.0207, -0.0023, 0.0163, 0.0263, -0.0112, 0.0245, 0.0411, 0.0285, 0.0267, 0.0297, 0.0213, -0.0577, 0.0169, 0.0592, 0.0227, 0.0290, 0.0074, 0.0197, 0.0282, 0.0368,0.0064, 0.0092, -0.0896, -0.0693, -0.0295, 0.0316, -0.0674, 0.0645,-0.0655, 0.0355, -0.0389, 0.0134, 0.0299, -0.0534, 0.0537, 0.0900, -0.0770, -0.0666, -0.0600, -0.0019, 0.0276, 0.0590, -0.0705, 0.0222, 0.0517, -0.0089, 0.0063, -0.0270, 0.0185, -0.0626, -0.0065, 0.0187,-0.0670, 0.0216, 0.0356, 0.0384, -0.0268, -0.0628, -0.0443, -0.0195, -0.0495, 0.1405, 0.0274, -0.0455, -0.0068, 0.0686, -0.0756, -0.0073, -0.0981, 0.0025, 0.0383, 0.0157, 0.0651, 0.0252, -0.0665, 0.0054, 0.0223, 0.0509, 0.0101, 0.0454, -0.0527, 0.0252, -0.0157, -0.0022, 0.0526, 0.0224, 0.0494, 0.0293, -0.0808, -0.1220, 0.0196, 0.0135, 0.0303, -0.0467, 0.0411, -0.0639, 0.0358, 0.0499, 0.0425, 0.0169, -0.0579, 0.0388, 0.0414, -0.0101, 0.0490, -0.0773, 0.0478, -0.0238, -0.0142, -0.0508, 0.0018, -0.0085, 0.0198, 0.0126, 0.0133, -0.0554, -0.0583, -0.0699, -0.0167, 0.0131, 0.0288, -0.0132, 0.0343, -0.0476, -0.0039, -0.0825, -0.1180, -0.0570, -0.0590, 0.0233, 0.0500, -0.0328, -0.0426, 0.0241, 0.0441, 0.0372, 0.0488, -0.0366, -0.0233, -0.0118, -0.0256, 0.0254, 0.0041, 0.0119, 0.0423, 0.0178, -0.0245, -0.0769, 0.0056, 0.0428, 0.0341, -0.0009, -0.0197, 0.0395, 0.0247, 0.0090, 0.0098, -0.0083, 0.0346, 0.0411, 0.0416, 0.0413, 0.0312, 0.0054, 0.0390, -0.0571, -0.0403, 0.0441, -0.0132, 0.0117, 0.0467, 0.0516,-0.0639, 0.0296, 0.0337, -0.0557, 0.0110, 0.0277, -0.0026, 0.0347, 0.0301, 0.0056, -0.0572, -0.0663, 0.0124, -0.0065, 0.0222, 0.0441,-0.0570, -0.0519, 0.0132, 0.0323, 0.0401, 0.0357, -0.0555, 0.0310,0.0028, -0.0102, -0.0598, 0.0153, -0.0438, 0.0268, -0.0097, 0.0388,-0.0330, -0.0277, -0.0581, -0.0389, 0.0099, 0.0371, -0.0455, 0.0553, 0.0753, -0.0154, -0.0385, 0.0359, 0.0403, 0.0464, 0.0499, -0.0365]])
speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
return speech
def text_to_speech(text):
# Generate audio
audio_output = generate_audio(text)
output_path = "output.wav"
sf.write(output_path, audio_output.numpy(), 16000, "PCM_16")
return output_path
examples = [
['اگر رشتے داری ہے تو پیسے کی'],
['میری تعلیم جیکی کی ہے۔']
]
interface = gr.Interface(fn=text_to_speech, inputs="text", outputs="audio", verbose = True, title="Urdu TTS",
description = "A simple Urdu Text to Speech Application. It is not by any means perfect and will not work for all text. You can sometimes expect it to generate random noise on an input of your choice. Right now it works successfully on very basic urdu text, such the ones in the example.", examples = examples)
interface.launch() |