Update app.py

app.py

@@ -1,7 +1,190 @@
+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
 
-def greet(name):
-    return "Hello " + name + "!!"
+# 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")
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+
+# 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.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'],
+    ['مجھے کھانے سے لطف نہیں آیا۔']
+]
+
+
+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()