Create app.py

app.py

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+import gradio as gr
+# Import the Parler TTS package
+from parler_tts import TTS
+
+# Initialize the TTS model for Hindi with voice cloning capabilities.
+# (Adjust the initialization parameters if needed.)
+tts_model = TTS(language="hi")
+
+def extract_speaker_embedding(voice_sample_path):
+    """
+    Extract the speaker embedding from the provided voice sample.
+    This function assumes that the TTS model exposes an 'extract_embedding' method.
+    """
+    # The extraction method below is pseudocode and might differ
+    # from the actual implementation in Parler TTS.
+    speaker_embedding = tts_model.extract_embedding(voice_sample_path)
+    return speaker_embedding
+
+def synthesize_voice_with_cloning(voice_sample_path, hindi_text):
+    """
+    Synthesize Hindi text into speech using voice cloning.
+    
+    Steps:
+      1. Extract speaker embedding from the provided voice sample.
+      2. Synthesize speech from the input Hindi text conditioned on the extracted embedding.
+    """
+    # Step 1: Get the speaker embedding from the voice sample.
+    speaker_embedding = extract_speaker_embedding(voice_sample_path)
+    
+    # Step 2: Synthesize the speech using the extracted speaker embedding.
+    # The parameter 'speaker_embedding' might be named differently in the actual API.
+    audio_output = tts_model.synthesize(text=hindi_text, speaker_embedding=speaker_embedding)
+    return audio_output
+
+# Build the Gradio interface for deployment
+iface = gr.Interface(
+    fn=synthesize_voice_with_cloning,
+    inputs=[
+        gr.Audio(source="upload", type="filepath", label="Upload Hindi Voice Sample"),
+        gr.Textbox(lines=3, placeholder="Enter Hindi text here...", label="Hindi Text")
+    ],
+    outputs=gr.Audio(label="Generated Speech"),
+    title="Hindi TTS with Voice Cloning",
+    description="Upload a Hindi voice sample and enter Hindi text to generate cloned speech."
+)
+
+if __name__ == "__main__":
+    iface.launch()