Create 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("Solo448/SpeechT5-tuned-bn")
+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("ucalyptus/train-bn", split="train", 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 = [
+    ("অ", "a"),
+    ("আ", "aa"),
+    ("ই", "i"),
+    ("ঈ", "ee"),
+    ("উ", "u"),
+    ("ঊ", "oo"),
+    ("ঋ", "ri"),
+    ("এ", "e"),
+    ("ঐ", "oi"),
+    ("ও", "o"),
+    ("ঔ", "ou"),
+    ("ক", "k"),
+    ("খ", "kh"),
+    ("গ", "g"),
+    ("ঘ", "gh"),
+    ("ঙ", "ng"),
+    ("চ", "ch"),
+    ("ছ", "chh"),
+    ("জ", "j"),
+    ("ঝ", "jh"),
+    ("ঞ", "nj"),
+    ("ট", "t"),
+    ("ঠ", "th"),
+    ("ড", "d"),
+    ("ঢ", "dh"),
+    ("ণ", "nr"),
+    ("ত", "t"),
+    ("থ", "th"),
+    ("দ", "d"),
+    ("ধ", "dh"),
+    ("ন", "n"),
+    ("প", "p"),
+    ("ফ", "ph"),
+    ("ব", "b"),
+    ("ভ", "bh"),
+    ("ম", "m"),
+    ("য", "ya"),
+    ("র", "r"),
+    ("ল", "l"),
+    ("শ", "sha"),
+    ("ষ", "sh"),
+    ("স", "s"),
+    ("হ", "ha"),
+    ("ড়", "rh"),
+    ("ঢ়", "rh"),
+    ("য়", "y"),
+    ("ৎ", "t"),
+    ("ঃ", "h"),
+    ("ঁ", "n"),
+    ("়", ""),
+    ("া", "a"),
+    ("ি", "i"),
+    ("ী", "ii"),
+    ("ু", "u"),
+    ("ূ", "uu"),
+    ("ৃ", "r"),
+    ("ে", "e"),
+    ("ৈ", "oi"),
+    ("ো", "o"),
+    ("ৌ", "ou"),
+    ("্", ""),
+    ("ৎ", "t"),
+    ("ৗ", "ou"),
+    ("ড়", "r"),
+    ("ঢ়", "r"),
+    ("য়", "y"),
+    ("ৰ", "r"),
+    ("৵", "lee"),
+    ("ং", "ng"),
+    ("১", "1"),
+    ("২", "2"),
+    ("৩", "3"),
+    ("৪", "4"),
+    ("৫", "5"),
+    ("৬", "6"),
+    ("৭", "7"),
+    ("৮", "8"),
+    ("৯", "9"),
+    ("০", "0")
+    ]
+    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="Bengali Text-to-Speech",
+    description="Enter bengali text to convert to speech"
+)
+
+iface.launch(share=True)