Upload app.py

app.py

@@ -0,0 +1,190 @@
+import torch
+import soundfile as sf
+import gradio as gr
+from transformers import AutoModel, AutoProcessor
+
+# ==========================================
+# Audio8 Model
+# ==========================================
+
+MODEL_ID = "Audio8/Audio8-TTS-Preview-0.6b"
+
+# Select GPU if available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Use BF16 on GPU, FP32 on CPU
+dtype = torch.bfloat16 if device == "cuda" else torch.float32
+
+print("=" * 60)
+print("Audio8 Text-to-Speech")
+print("=" * 60)
+
+print("PyTorch:", torch.__version__)
+print("CUDA Available:", torch.cuda.is_available())
+
+if torch.cuda.is_available():
+    print("GPU:", torch.cuda.get_device_name(0))
+
+# ==========================================
+# Load Processor
+# ==========================================
+
+print("\nLoading processor...")
+
+processor = AutoProcessor.from_pretrained(
+    MODEL_ID,
+    trust_remote_code=True
+)
+
+# ==========================================
+# Load Model
+# ==========================================
+
+print("Loading Audio8 model...")
+
+model = AutoModel.from_pretrained(
+    MODEL_ID,
+    trust_remote_code=True,
+    dtype=dtype
+).eval().to(device)
+
+print("✅ Audio8 model loaded successfully!")
+
+# ==========================================
+# Generate Speech
+# ==========================================
+
+def generate_speech(text):
+
+    # Check empty input
+    if not text or not text.strip():
+        return None
+
+    print("\n" + "=" * 50)
+    print("Generating speech...")
+    print("Text:", text)
+
+    # Process text
+    inputs = processor(
+        text=[text],
+        return_tensors="pt"
+    )
+
+    # Move inputs to GPU/CPU
+    inputs = {
+        name: value.to(device)
+        for name, value in inputs.items()
+    }
+
+    # Generate speech tokens
+    with torch.inference_mode():
+
+        output = model.generate(
+            **inputs,
+            max_new_tokens=1024,
+            temperature=0.8,
+            top_p=0.95,
+            top_k=50,
+            do_sample=True,
+            return_dict_in_generate=True,
+        )
+
+    # Decode generated audio
+    waveforms, waveform_lengths = model.decode_audio(
+        output.codes
+    )
+
+    # Convert to NumPy
+    audio = waveforms[
+        0,
+        :int(waveform_lengths[0])
+    ].float().cpu().numpy()
+
+    # ==========================================
+    # Normalize Audio Volume
+    # ==========================================
+
+    peak = abs(audio).max()
+
+    if peak > 0:
+        audio = audio / peak
+        audio = audio * 0.95
+
+    # ==========================================
+    # Save Audio
+    # ==========================================
+
+    output_file = "output.wav"
+
+    sf.write(
+        output_file,
+        audio,
+        model.config.codec_sample_rate
+    )
+
+    print("✅ Speech generated!")
+    print("🎵 Saved:", output_file)
+
+    return output_file
+
+
+# ==========================================
+# Gradio User Interface
+# ==========================================
+
+with gr.Blocks(title="SmartVoiceAI") as app:
+
+    gr.Markdown(
+        """
+        # 🔊 SmartVoiceAI
+
+        ### AI Text-to-Speech
+
+        Enter your text below and generate natural-sounding speech
+        using the Audio8 TTS model.
+        """
+    )
+
+    # Text input
+    text_input = gr.Textbox(
+        label="Enter Text",
+        placeholder="Type something here...",
+        lines=6
+    )
+
+    # Generate button
+    generate_button = gr.Button(
+        "🔊 Generate Speech",
+        variant="primary"
+    )
+
+    # Audio output
+    audio_output = gr.Audio(
+        label="Generated Voice",
+        type="filepath",
+        autoplay=True
+    )
+
+    # Button action
+    generate_button.click(
+        fn=generate_speech,
+        inputs=text_input,
+        outputs=audio_output
+    )
+
+    gr.Markdown(
+        """
+        ---
+        **Model:** Audio8-TTS-Preview-0.6b  
+        **Device:** CUDA / CPU
+        """
+    )
+
+
+# ==========================================
+# Start Application
+# ==========================================
+
+if __name__ == "__main__":
+
+    app.launch()