Create app.py

app.py

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+import gradio as gr
+from transformers import AutoProcessor, BarkModel
+import scipy.io.wavfile
+import torch
+import os
+
+# Initialize model and processor
+processor = AutoProcessor.from_pretrained("suno/bark")
+model = BarkModel.from_pretrained("suno/bark")
+
+def text_to_speech(text, voice_preset="v2/hi_speaker_2"):
+    # Generate audio from text
+    inputs = processor(text, voice_preset=voice_preset)
+    
+    # Generate audio
+    audio_array = model.generate(**inputs)
+    audio_array = audio_array.cpu().numpy().squeeze()
+    
+    # Get sample rate from model config
+    sample_rate = model.generation_config.sample_rate
+    
+    # Create temporary file path
+    output_path = "temp_audio.wav"
+    
+    # Save audio file
+    scipy.io.wavfile.write(output_path, rate=sample_rate, data=audio_array)
+    
+    return output_path
+
+# Define available voice presets
+voice_presets = [
+    "v2/hi_speaker_1",
+    "v2/hi_speaker_2",
+    "v2/hi_speaker_3",
+    "v2/hi_speaker_4",
+    "v2/hi_speaker_5"
+]
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=text_to_speech,
+    inputs=[
+        gr.Textbox(label="Enter text (Hindi or English)", placeholder="तुम बहुत अच्छे हो..."),
+        gr.Dropdown(choices=voice_presets, value="v2/hi_speaker_2", label="Select Voice")
+    ],
+    outputs=gr.Audio(label="Generated Speech"),
+    title="Bark Text-to-Speech",
+    description="Convert text to speech using the Bark model. Supports Hindi and English text.",
+    examples=[
+        ["तुम बहुत अच्छे हो और मैं भी तुम्हारी तरह अच्छा हूँ", "v2/hi_speaker_2"],
+        ["You are very nice and I am also nice like you", "v2/hi_speaker_1"]
+    ]
+)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()