Create app.py

app.py

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+import gradio as gr
+import torch
+import torchaudio as ta
+from huggingface_hub import hf_hub_download
+from peft import PeftModel
+import tempfile
+import sys
+import os
+
+# Ensure chatterbox is installed (usually handled via requirements.txt in HF spaces)
+try:
+    from chatterbox.tts import ChatterboxTTS
+except ImportError:
+    print("chatterbox-tts is not installed. Please add it to your requirements.txt.")
+    sys.exit(1)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+repo_id = "Praha-Labs/PrahaTTS-ML"
+
+def load_model():
+    print(f"Loading base Chatterbox model on {device}...")
+    # Load the base model
+    model = ChatterboxTTS.from_pretrained(device=device)
+    
+    print("Downloading and applying custom Indic tokenizer...")
+    try:
+        # Download tokenizer_indic.json from the adapter repository
+        tokenizer_path = hf_hub_download(repo_id=repo_id, filename="tokenizer_indic.json")
+        
+        # Override the default tokenizer.
+        # Note: Depending on Chatterbox's exact structure, the tokenizer might be 
+        # on the text-to-speech backbone component (e.g., model.tokenizer or model.t3.tokenizer).
+        # We try assigning it directly if the library supports loading from a custom path.
+        if hasattr(model, 'tokenizer'):
+            # Some versions use a load/from_file method
+            if hasattr(model.tokenizer, 'load_from_file'):
+                model.tokenizer.load_from_file(tokenizer_path)
+            else:
+                print("Warning: Custom tokenizer replacement might require library-specific logic.")
+    except Exception as e:
+        print(f"Warning during tokenizer load: {e}")
+
+    print("Loading LoRA adapter weights...")
+    try:
+        # Load the PEFT adapter onto the LLaMA backbone/Transformer model
+        # Chatterbox architecture uses 't3' (Text-to-Speech Token Generator)
+        if hasattr(model, 't3'):
+            model.t3 = PeftModel.from_pretrained(model.t3, repo_id)
+        else:
+            # Fallback if the architecture wraps it differently
+            model = PeftModel.from_pretrained(model, repo_id)
+        print("LoRA adapter loaded successfully.")
+    except Exception as e:
+        print(f"Failed to load PEFT adapter: {e}")
+        
+    return model
+
+# Initialize Model
+tts_model = load_model()
+
+def synthesize_audio(text, ref_audio, exaggeration, cfg):
+    if not text.strip():
+        return None, "Please enter some text."
+        
+    audio_prompt_path = ref_audio if ref_audio else None
+    
+    try:
+        # Generate the waveform
+        wav = tts_model.generate(
+            text, 
+            audio_prompt_path=audio_prompt_path,
+            exaggeration=exaggeration,
+            cfg=cfg
+        )
+        
+        # Save generated audio to a temporary file for Gradio
+        temp_out = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
+        ta.save(temp_out.name, wav.cpu(), tts_model.sr)
+        return temp_out.name, "Generation successful!"
+        
+    except Exception as e:
+        return None, f"Generation Error: {str(e)}"
+
+# Define the Gradio Interface
+with gr.Blocks(title="PrahaTTS-ML: Malayalam TTS", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🗣️ PrahaTTS-ML: Malayalam LoRA Adapter for Chatterbox TTS")
+    gr.Markdown(
+        "This Space runs the [Praha-Labs/PrahaTTS-ML](https://huggingface.co/Praha-Labs/PrahaTTS-ML) model. "
+        "It is a Malayalam LoRA adapter built on top of ResembleAI's Chatterbox non-turbo TTS model. \n\n"
+        "**Note**: Provide up to 5-10 seconds of clear reference audio for voice cloning capabilities."
+    )
+    
+    with gr.Row():
+        with gr.Column():
+            text_input = gr.Textbox(
+                label="Input Text (Malayalam/English)", 
+                lines=4, 
+                placeholder="നമസ്കാരം, మీరు ఎలా ఉన్నారు?"
+            )
+            ref_audio_input = gr.Audio(
+                label="Reference Voice Audio (Optional, for Voice Cloning)", 
+                type="filepath"
+            )
+            
+            with gr.Accordion("Advanced Voice Controls", open=False):
+                exaggeration_slider = gr.Slider(
+                    minimum=0.0, maximum=1.0, value=0.5, step=0.05, 
+                    label="Emotion Exaggeration", 
+                    info="Lower for monotone, higher for dramatic/expressive"
+                )
+                cfg_slider = gr.Slider(
+                    minimum=0.0, maximum=1.0, value=0.5, step=0.05, 
+                    label="CFG Weight", 
+                    info="Lower if speech is too fast, higher to strictly mimic the reference voice"
+                )
+                
+            generate_btn = gr.Button("Synthesize Speech", variant="primary")
+            
+        with gr.Column():
+            audio_output = gr.Audio(label="Generated Output", interactive=False)
+            status_output = gr.Textbox(label="Status Logging", interactive=False)
+            
+    # Connect logic to UI
+    generate_btn.click(
+        fn=synthesize_audio,
+        inputs=[text_input, ref_audio_input, exaggeration_slider, cfg_slider],
+        outputs=[audio_output, status_output]
+    )
+
+if __name__ == "__main__":
+    demo.launch()