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| # Utils | |
| import timeit | |
| import soundfile as sf | |
| # Streamlit | |
| import streamlit as st | |
| # Custom elements | |
| from elements.component import ( | |
| centered_text, | |
| ) | |
| def generate_voice( | |
| input_text, | |
| ): | |
| # TTS Inference | |
| start_time = timeit.default_timer() | |
| c, c_length, phoneme = st.session_state.TTS.tokenize(input_text) | |
| tok_time = timeit.default_timer() - start_time | |
| start_time = timeit.default_timer() | |
| voice = st.session_state.TTS.vocalize(c, c_length) | |
| tts_time = timeit.default_timer() - start_time | |
| # Time stats | |
| total_infer_time = tts_time + tok_time | |
| audio_time = voice.shape[-1] / 22050 | |
| rtf = total_infer_time / audio_time | |
| rt_ratio = 1 / rtf | |
| # Save audio (bug in Streamlit, can't play numpy array directly) | |
| sf.write(f"cache_sound/{st.session_state.random_str}.wav", voice[0,0], 22050) | |
| # Play audio | |
| st.audio(f"cache_sound/{st.session_state.random_str}.wav", format = "audio/wav") | |
| st.caption("Generated Voice") | |
| st.code( | |
| f"💬 Output Audio: {str(audio_time)[:6]} sec.\n\n⏳ Elapsed time for:\n => Tokenization: {str(tok_time)[:6]} sec.\n => Model Inference: {str(tts_time)[:6]} sec.\n\n⏰ Real-time Factor (RTF): {str(rtf)[:6]}\n\n🏃 The model runs {str(rt_ratio)[:6]} x faster than real-time \ | |
| ", | |
| language = "bash", | |
| ) | |
| st.caption("Elapsed Time Stats") |