| import random |
| import time |
| import numpy as np |
|
|
| def clone_voice(samples, traits): |
| |
| time.sleep(3) |
| |
| duration = 5 |
| sample_rate = 44100 |
| t = np.linspace(0, duration, int(sample_rate * duration), False) |
| waveform = np.sin(440 * 2 * np.pi * t) * 0.5 |
| return (waveform * 32767).astype(np.int16) |
|
|
| def restore_speech(samples): |
| |
| time.sleep(3) |
| |
| duration = 5 |
| sample_rate = 44100 |
| t = np.linspace(0, duration, int(sample_rate * duration), False) |
| waveform = np.sin(440 * 2 * np.pi * t) * 0.5 |
| return (waveform * 32767).astype(np.int16) |
|
|
| def generate_historical_dialogue(personality, user_input): |
| |
| time.sleep(2) |
| responses = [ |
| f"As {personality}, I find your question about '{user_input}' intriguing. In my time, we...", |
| f"Interesting inquiry about '{user_input}'. During my era as {personality}, I observed that...", |
| f"Your question on '{user_input}' touches upon a crucial aspect of my work as {personality}. Let me explain...", |
| f"Ah, '{user_input}'! This reminds me of a time when I, {personality}, encountered a similar situation...", |
| f"From the perspective of {personality}, I can say that '{user_input}' relates to my experiences in the following way..." |
| ] |
| response = random.choice(responses) |
| st.session_state.conversation_history.append(f"You: {user_input}") |
| st.session_state.conversation_history.append(f"{personality}: {response}") |
| return response |
|
|