Update app.py

app.py

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+import gradio as gr
+import torch
+import python_multipart
+import os
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+from datasets import load_dataset, Audio
+import numpy as np
+from speechbrain.inference import EncoderClassifier
+
+# Load models and processor
+processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+model = SpeechT5ForTextToSpeech.from_pretrained("Sana1207/Hindi_SpeechT5_finetuned")
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+
+# Load speaker encoder
+device = "cuda" if torch.cuda.is_available() else "cpu"
+speaker_model = EncoderClassifier.from_hparams(
+    source="speechbrain/spkrec-xvect-voxceleb",
+    run_opts={"device": device},
+    savedir=os.path.join("/tmp", "speechbrain/spkrec-xvect-voxceleb")
+)
+
+# Load a sample from the dataset for speaker embedding
+try:
+    dataset = load_dataset("mozilla-foundation/common_voice_17_0", "hi", split="validated", trust_remote_code=True)
+    dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
+    sample = dataset[0]
+    speaker_embedding = create_speaker_embedding(sample['audio']['array'])
+except Exception as e:
+    print(f"Error loading dataset: {e}")
+    # Use a random speaker embedding as fallback
+    speaker_embedding = torch.randn(1, 512)
+
+def create_speaker_embedding(waveform):
+    with torch.no_grad():
+        speaker_embeddings = speaker_model.encode_batch(torch.tensor(waveform))
+        speaker_embeddings = torch.nn.functional.normalize(speaker_embeddings, dim=2)
+        speaker_embeddings = speaker_embeddings.squeeze().cpu().numpy()
+    return speaker_embeddings
+
+def text_to_speech(text):
+    # Clean up text
+    replacements = [
+    ("अ", "a"),
+    ("आ", "aa"),
+    ("इ", "i"),
+    ("ई", "ee"),
+    ("उ", "u"),
+    ("ऋ", "ri"),
+    ("ए", "ae"),
+    ("ऐ", "ai"),
+    ("ऑ", "au"),
+    ("ओ", "o"),
+    ("औ", "au"),
+    ("क", "k"),
+    ("ख", "kh"),
+    ("ग", "g"),
+    ("घ", "gh"),
+    ("च", "ch"),
+    ("छ", "chh"),
+    ("ज", "j"),
+    ("झ", "jh"),
+    ("ञ", "gna"),
+    ("ट", "t"),
+    ("ठ", "th"),
+    ("ड", "d"),
+    ("ढ", "dh"),
+    ("ण", "nr"),
+    ("त", "t"),
+    ("थ", "th"),
+    ("द", "d"),
+    ("ध", "dh"),
+    ("न", "n"),
+    ("प", "p"),
+    ("फ", "ph"),
+    ("ब", "b"),
+    ("भ", "bh"),
+    ("म", "m"),
+    ("य", "ya"),
+    ("र", "r"),
+    ("ल", "l"),
+    ("व", "w"),
+    ("श", "sha"),
+    ("ष", "sh"),
+    ("स", "s"),
+    ("ह", "ha"),
+    ("़", "ng"),
+    ("्", ""),
+    ("ऽ", ""),
+    ("ा", "a"),
+    ("ि", "i"),
+    ("ी", "ee"),
+    ("ु", "u"),
+    ("ॅ", "n"),
+    ("े", "e"),
+    ("ै", "oi"),
+    ("ो", "o"),
+    ("ौ", "ou"),
+    ("ॅ", "n"),
+    ("ॉ", "r"),
+    ("ू", "uh"),
+    ("ृ", "ri"),
+    ("ं", "n"),
+    ("क़", "q"),
+    ("ज़", "z"),
+    ("ड़", "r"),
+    ("ढ़", "rh"),
+    ("फ़", "f"),
+    ("|", ".")
+    ]
+    for src, dst in replacements:
+        text = text.replace(src, dst)
+
+    inputs = processor(text=text, return_tensors="pt")
+    speech = model.generate_speech(inputs["input_ids"], speaker_embedding, vocoder=vocoder)
+    return (16000, speech.numpy())
+
+iface = gr.Interface(
+    fn=text_to_speech,
+    inputs="text",
+    outputs="audio",
+    title="Hindi Text-to-Speech",
+    description="Enter Hindi text to convert it into an Audio"
+)
+
+iface.launch(share=True)