Create v1.txt

v1.txt

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+import gradio as gr
+from TTS.api import TTS
+import numpy as np
+from scipy.io import wavfile
+import tempfile
+import os
+
+# Load the YourTTS model once at startup
+tts = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False)
+sample_rate = tts.synthesizer.output_sample_rate
+
+def generate_speech(reference_audio, text):
+    """
+    Generate speech audio mimicking the voice from the reference audio.
+    
+    Parameters:
+    reference_audio (str): Filepath to the uploaded voice sample.
+    text (str): Text to convert to speech.
+    
+    Returns:
+    str: Path to the generated audio file
+    """
+    # Generate speech using the reference audio and text
+    wav = tts.tts(text=text, speaker_wav=reference_audio, language="en")
+    # Convert list to numpy array
+    wav_np = np.array(wav, dtype=np.float32)
+    
+    # Create a temporary file to save the audio
+    temp_file = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
+    temp_file_path = temp_file.name
+    # Save the audio to the temporary file
+    wavfile.write(temp_file_path, sample_rate, wav_np)
+    temp_file.close()
+    
+    return temp_file_path
+
+# Build the Gradio interface
+with gr.Blocks(title="Voice Cloning TTS") as app:
+    gr.Markdown("## Voice Cloning Text-to-Speech")
+    gr.Markdown("Upload a short voice sample in English, then enter text to hear it in your voice!")
+    
+    with gr.Row():
+        audio_input = gr.Audio(type="filepath", label="Upload Your Voice Sample (English)")
+        text_input = gr.Textbox(label="Enter Text to Convert to Speech", placeholder="e.g., I love chocolate")
+    
+    generate_btn = gr.Button("Generate Speech")
+    audio_output = gr.Audio(label="Generated Speech", interactive=False)
+    
+    # Connect the button to the generation function
+    generate_btn.click(
+        fn=generate_speech,
+        inputs=[audio_input, text_input],
+        outputs=audio_output
+    )
+
+# Launch the application
+app.launch()