Create app.py

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()