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| import gradio as gr | |
| import torch | |
| import torchaudio | |
| from huggingface_hub import snapshot_download | |
| from tts import StepAudioTTS | |
| from tokenizer import StepAudioTokenizer | |
| import os | |
| import tempfile | |
| import spaces | |
| class StepAudioDemo: | |
| def __init__(self): | |
| # Download models from HuggingFace | |
| print("Downloading models from HuggingFace...") | |
| self.model_path = snapshot_download(repo_id="stepfun-ai/Step-Audio-TTS-3B") | |
| self.tokenizer_path = snapshot_download(repo_id="stepfun-ai/Step-Audio-Tokenizer") | |
| # Initialize models | |
| print("Initializing models...") | |
| self.encoder = StepAudioTokenizer(self.tokenizer_path) | |
| self.tts_engine = StepAudioTTS(self.model_path, self.encoder) | |
| # Create temporary directory for outputs | |
| self.temp_dir = tempfile.mkdtemp() | |
| print("Models loaded and ready!") | |
| def generate_tts(self, text, speaker_name): | |
| """Generate TTS audio""" | |
| try: | |
| output_audio, sr = self.tts_engine(text, speaker_name) | |
| output_path = os.path.join(self.temp_dir, "output_tts.wav") | |
| torchaudio.save(output_path, output_audio, sr) | |
| return output_path | |
| except Exception as e: | |
| return f"Error generating audio: {str(e)}" | |
| def generate_clone(self, text, prompt_audio, prompt_text): | |
| """Generate cloned voice audio""" | |
| try: | |
| clone_speaker = { | |
| "speaker": "clone", | |
| "prompt_text": prompt_text, | |
| "wav_path": prompt_audio | |
| } | |
| output_audio, sr = self.tts_engine(text, "", clone_speaker) | |
| output_path = os.path.join(self.temp_dir, "output_clone.wav") | |
| torchaudio.save(output_path, output_audio, sr) | |
| return output_path | |
| except Exception as e: | |
| return f"Error generating cloned audio: {str(e)}" | |
| def create_demo(): | |
| demo = StepAudioDemo() | |
| with gr.Blocks() as interface: | |
| gr.Markdown("# Step Audio TTS Demo") | |
| with gr.Tabs(): | |
| # TTS Tab | |
| with gr.TabItem("Text-to-Speech"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| tts_text = gr.Textbox( | |
| label="Input Text", | |
| placeholder="Enter text to synthesize...", | |
| lines=5 | |
| ) | |
| speaker_name = gr.Textbox( | |
| label="Speaker Name", | |
| placeholder="Enter speaker name (e.g., 闫雨婷)", | |
| value="闫雨婷" | |
| ) | |
| tts_button = gr.Button("Generate Speech") | |
| with gr.Column(): | |
| tts_output = gr.Audio(label="Generated Audio") | |
| tts_button.click( | |
| fn=demo.generate_tts, | |
| inputs=[tts_text, speaker_name], | |
| outputs=tts_output | |
| ) | |
| # Voice Cloning Tab | |
| with gr.TabItem("Voice Cloning"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| clone_text = gr.Textbox( | |
| label="Input Text", | |
| placeholder="Enter text to synthesize with cloned voice...", | |
| lines=5 | |
| ) | |
| prompt_text = gr.Textbox( | |
| label="Prompt Text", | |
| placeholder="Enter the transcript of your prompt audio...", | |
| lines=3 | |
| ) | |
| prompt_audio = gr.Audio( | |
| label="Upload Prompt Audio", | |
| type="filepath" | |
| ) | |
| clone_button = gr.Button("Generate Cloned Speech") | |
| with gr.Column(): | |
| clone_output = gr.Audio(label="Generated Audio") | |
| clone_button.click( | |
| fn=demo.generate_clone, | |
| inputs=[clone_text, prompt_audio, prompt_text], | |
| outputs=clone_output | |
| ) | |
| gr.Markdown(""" | |
| ## Usage Notes: | |
| - For basic TTS: Enter text and speaker name in the Text-to-Speech tab | |
| - For voice cloning: Upload a prompt audio file, enter its transcript, and the text you want to synthesize | |
| - Generation may take a few moments depending on text length | |
| """) | |
| return interface | |
| if __name__ == "__main__": | |
| demo = create_demo() | |
| demo.queue().launch() |