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| from transformers import pipeline | |
| import gradio as gr | |
| # Initialize the text-to-speech pipeline with a model from Hugging Face's Model Hub | |
| model_name = "kakao-enterprise/vits-ljs" | |
| text_to_speech_pipeline = pipeline("text-to-speech", model=model_name) | |
| def generate_speech(text): | |
| # Generate speech from the input text | |
| out = text_to_speech_pipeline(text) | |
| # The output is a list of tensors, convert to numpy array | |
| audio_data = out[0]["array"] | |
| return audio_data, 22050 # Return audio data and sampling rate | |
| # Create the Gradio interface | |
| interface = gr.Interface(fn=generate_speech, | |
| inputs=gr.Textbox(lines=2, placeholder="Type something here..."), | |
| outputs=gr.Audio(type="numpy", label="Generated Speech"), | |
| title="Text-to-Speech with Hugging Face", | |
| description="Enter text to generate speech using a model from Hugging Face's Model Hub.") | |
| # Launch the app | |
| interface.launch() | |