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| 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() |