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| import streamlit as st | |
| from transformers import pipeline | |
| import io | |
| import numpy as np | |
| import requests | |
| # Caching the text-to-speech model | |
| def load_tts_pipeline(): | |
| return pipeline("text-to-speech", model="microsoft/speecht5_tts") | |
| # Initialize the model once using cache | |
| tts_pipe = load_tts_pipeline() | |
| # Initialize session state for conversation history, bot response, and selected options | |
| if 'conversation_history' not in st.session_state: | |
| st.session_state.conversation_history = "" | |
| if 'tts_audio' not in st.session_state: | |
| st.session_state.tts_audio = None | |
| def get_speaker_embeddings(): | |
| url = "https://huggingface.co/datasets/Matthijs/cmu-arctic-xvectors/resolve/main/xvectors.npy" | |
| response = requests.get(url) | |
| if 'application/octet-stream' in response.headers['Content-Type']: | |
| with io.BytesIO(response.content) as buffer: | |
| try: | |
| speaker_embeddings = np.load(buffer, allow_pickle=True) | |
| except ValueError as e: | |
| st.error("Failed to load speaker embeddings.") | |
| speaker_embeddings = None | |
| else: | |
| st.error("Unexpected file type.") | |
| speaker_embeddings = None | |
| return speaker_embeddings | |
| def convert_text_to_speech(text): | |
| speaker_embeddings = get_speaker_embeddings() # Obtain speaker embeddings | |
| if speaker_embeddings is None: | |
| return None | |
| try: | |
| audio = tts_pipe(text, speaker_embeddings=speaker_embeddings) | |
| return audio | |
| except Exception as e: | |
| st.error(f"Error generating speech: {e}") | |
| return None | |
| def convert_audio_to_bytes(audio): | |
| audio_buffer = io.BytesIO(audio['audio']) | |
| return audio_buffer | |
| # Sidebar options | |
| st.sidebar.title("App Settings") | |
| feature = st.sidebar.radio("Choose Feature:", ("Text-to-Speech", "Other Options")) | |
| if feature == "Text-to-Speech": | |
| st.title("🗣 Text-to-Speech Converter") | |
| st.subheader("Convert your text to speech using a TTS model!") | |
| user_message = st.text_area("Enter text to convert to speech:") | |
| if st.button("Convert"): | |
| if user_message: | |
| tts_audio = convert_text_to_speech(user_message) | |
| if tts_audio: | |
| audio_bytes = convert_audio_to_bytes(tts_audio) | |
| st.audio(audio_bytes, format='audio/wav') | |
| st.success("Conversion successful!") | |
| else: | |
| st.error("Conversion failed.") | |
| else: | |
| st.warning("Please enter text before converting.") | |
| st.markdown("---") | |
| st.markdown("### About this App") | |
| st.info("This app uses a text-to-speech model from the Hugging Face Transformers library. Enter text to hear it spoken out loud.") | |
| st.sidebar.markdown("---") | |
| st.sidebar.write("Created by [Your Name](https://github.com/yourprofile)") | |