Spaces:
Runtime error
Runtime error
| import streamlit as st | |
| import requests | |
| import base64 | |
| from io import BytesIO | |
| import pandas as pd | |
| # Set page config | |
| st.set_page_config( | |
| page_title="Nigerian Text-to-Speech", | |
| page_icon="🎙️", | |
| layout="wide" | |
| ) | |
| # Define the available voices and languages | |
| AVAILABLE_VOICES = { | |
| "Female": ["zainab", "idera", "regina", "chinenye", "joke", "remi"], | |
| "Male": ["jude", "tayo", "umar", "osagie", "onye", "emma"] | |
| } | |
| AVAILABLE_LANGUAGES = ["english", "yoruba", "igbo", "hausa"] | |
| # IMPORTANT: Replace this with the ngrok URL shown in your Colab notebook | |
| # Example: API_BASE_URL = "https://a1b2-34-56-78-90.ngrok.io" | |
| API_BASE_URL = st.text_input( | |
| "Enter the ngrok URL from Colab (e.g., https://a1b2-34-56-78-90.ngrok.io)", | |
| value="", | |
| key="api_url" | |
| ) | |
| # Derive the TTS endpoint from the base URL | |
| if API_BASE_URL: | |
| API_TTS_ENDPOINT = f"{API_BASE_URL}/tts" | |
| # Test connection to backend | |
| try: | |
| health_check = requests.get(f"{API_BASE_URL}") | |
| if health_check.status_code == 200: | |
| st.success(f"✅ Connected to backend API successfully!") | |
| else: | |
| st.warning(f"⚠️ Backend API returned status code {health_check.status_code}") | |
| except Exception as e: | |
| st.error(f"❌ Cannot connect to backend API: {str(e)}") | |
| else: | |
| st.warning("⚠️ Please enter the ngrok URL from your Colab notebook to continue") | |
| # App title and description | |
| st.title("Nigerian Text-to-Speech") | |
| st.markdown(""" | |
| Convert text to speech with authentic Nigerian accents. This app uses YarnGPT, a text-to-speech model | |
| that generates natural Nigerian-accented speech in English, Yoruba, Igbo, and Hausa. | |
| """) | |
| # Create tabs for different functions | |
| tab1, tab2, tab3 = st.tabs(["Basic TTS", "Batch Processing", "About"]) | |
| # Tab 1: Basic TTS | |
| with tab1: | |
| col1, col2 = st.columns([3, 1]) | |
| with col1: | |
| # Text input | |
| text_input = st.text_area( | |
| "Enter text to convert to speech", | |
| "Welcome to Nigeria, the giant of Africa. Our diverse cultures and languages make us unique.", | |
| height=150 | |
| ) | |
| # Generate button | |
| generate_button = st.button("Generate Audio", type="primary", disabled=not API_BASE_URL) | |
| with col2: | |
| # Options | |
| language = st.selectbox("Language", AVAILABLE_LANGUAGES) | |
| gender = st.radio("Gender", ["Female", "Male"]) | |
| voice = st.selectbox("Voice", AVAILABLE_VOICES[gender]) | |
| st.info(f"Selected voice: **{voice}** ({gender.lower()})") | |
| # Generate audio when button is clicked | |
| if generate_button and text_input and API_BASE_URL: | |
| with st.spinner("Generating audio... (This may take a minute as the audio is processed through Colab)"): | |
| try: | |
| # Call the API with timeout increased | |
| response = requests.post( | |
| API_TTS_ENDPOINT, | |
| json={"text": text_input, "language": language, "voice": voice}, | |
| timeout=100000 # Increase timeout to 2 minutes | |
| ) | |
| if response.status_code == 200: | |
| # Get response data | |
| audio_data = response.json() | |
| # Save info in session state | |
| st.session_state.last_text = text_input | |
| st.session_state.last_voice = voice | |
| st.session_state.last_language = language | |
| # Display success and audio player | |
| st.success("Audio generated successfully!") | |
| st.markdown(f"Voice: **{voice}** | Language: **{language}**") | |
| # Handle base64-encoded audio | |
| if "audio_base64" in audio_data: | |
| audio_bytes = base64.b64decode(audio_data["audio_base64"]) | |
| audio_stream = BytesIO(audio_bytes) | |
| # Play audio directly from the stream | |
| st.audio(audio_stream, format="audio/wav") | |
| else: | |
| # Fall back to URL method (legacy support) | |
| audio_url = f"{API_BASE_URL}{audio_data['audio_url']}" | |
| st.warning("Using legacy URL-based audio (may not work)") | |
| st.code(audio_url, language="text") | |
| st.audio(audio_url, format="audio/wav") | |
| else: | |
| st.error(f"Error: {response.status_code} - {response.text}") | |
| except Exception as e: | |
| st.error(f"Error generating audio: {str(e)}") | |
| st.info(f"Make sure the backend API is running and accessible at {API_BASE_URL}") | |
| # Tab 2: Batch Processing | |
| with tab2: | |
| st.header("Batch Text-to-Speech Conversion") | |
| st.markdown(""" | |
| Process multiple text entries at once. Upload a CSV file with the following columns: | |
| - `text`: The text to convert to speech | |
| - `language` (optional): Language for the text (english, yoruba, igbo, hausa) | |
| - `voice` (optional): Voice name to use | |
| """) | |
| # File uploader | |
| uploaded_file = st.file_uploader("Upload CSV file", type="csv") | |
| if uploaded_file and API_BASE_URL: | |
| # Process the file | |
| try: | |
| df = pd.read_csv(uploaded_file) | |
| if "text" not in df.columns: | |
| st.error("CSV file must contain a 'text' column") | |
| else: | |
| st.dataframe(df.head()) | |
| # Default values | |
| default_language = st.selectbox("Default language", AVAILABLE_LANGUAGES) | |
| default_voice = st.selectbox("Default voice", AVAILABLE_VOICES["Female"] + AVAILABLE_VOICES["Male"]) | |
| if st.button("Process Batch", disabled=not API_BASE_URL): | |
| # Create a container for audio files | |
| audio_container = st.container() | |
| progress_bar = st.progress(0) | |
| status_text = st.empty() | |
| # Process each row | |
| results = [] | |
| audio_files = [] # Store audio data for playback | |
| for i, row in enumerate(df.itertuples()): | |
| # Update progress | |
| progress = int((i + 1) / len(df) * 100) | |
| progress_bar.progress(progress) | |
| status_text.text(f"Processing item {i+1} of {len(df)}...") | |
| # Get text and parameters | |
| text = row.text | |
| lang = getattr(row, 'language', default_language) if hasattr(row, 'language') else default_language | |
| voice_name = getattr(row, 'voice', default_voice) if hasattr(row, 'voice') else default_voice | |
| try: | |
| # Make API call with increased timeout | |
| response = requests.post( | |
| API_TTS_ENDPOINT, | |
| json={"text": text, "language": lang, "voice": voice_name}, | |
| timeout=120 # Increase timeout to 2 minutes | |
| ) | |
| if response.status_code == 200: | |
| audio_data = response.json() | |
| # Handle base64-encoded audio | |
| if "audio_base64" in audio_data: | |
| audio_bytes = base64.b64decode(audio_data["audio_base64"]) | |
| audio_files.append({ | |
| "index": i, | |
| "bytes": audio_bytes, | |
| "text": text, | |
| "voice": voice_name, | |
| "language": lang | |
| }) | |
| status = "Success" | |
| else: | |
| # Fall back to URL method (legacy support) | |
| audio_url = f"{API_BASE_URL}{audio_data['audio_url']}" | |
| status = "Success (URL mode)" | |
| # Add to results | |
| results.append({ | |
| "text": text[:50] + "..." if len(text) > 50 else text, | |
| "language": lang, | |
| "voice": voice_name, | |
| "status": status | |
| }) | |
| else: | |
| results.append({ | |
| "text": text[:50] + "..." if len(text) > 50 else text, | |
| "language": lang, | |
| "voice": voice_name, | |
| "status": f"Error: {response.status_code}" | |
| }) | |
| except Exception as e: | |
| results.append({ | |
| "text": text[:50] + "..." if len(text) > 50 else text, | |
| "language": lang, | |
| "voice": voice_name, | |
| "status": f"Error: {str(e)}" | |
| }) | |
| # Show results | |
| st.success("Batch processing completed!") | |
| results_df = pd.DataFrame(results) | |
| st.dataframe(results_df) | |
| # Display audio players for successful generations | |
| with audio_container: | |
| st.subheader("Generated Audio Files") | |
| for audio_item in audio_files: | |
| st.markdown(f"**{audio_item['index']+1}. {audio_item['text'][:50]}...** ({audio_item['voice']}, {audio_item['language']})") | |
| audio_stream = BytesIO(audio_item["bytes"]) | |
| st.audio(audio_stream, format="audio/wav") | |
| st.markdown("---") | |
| except Exception as e: | |
| st.error(f"Error processing file: {str(e)}") | |
| elif not API_BASE_URL: | |
| st.warning("Please enter the ngrok URL first to enable batch processing") | |
| # Tab 3: About | |
| with tab3: | |
| st.header("About YarnGPT") | |
| col1, col2 = st.columns([1, 1]) | |
| with col1: | |
| st.markdown(""" | |
| ### Features | |
| - 🗣️ 12 preset voices (6 male, 6 female) | |
| - 🎯 Trained on 2000+ hours of Nigerian audio | |
| - 🔊 24kHz high-quality audio output | |
| - 📝 Support for long-form text | |
| ### Model Details | |
| - Base: HuggingFaceTB/SmolLM2-360M | |
| - Training: 5 epochs on A100 GPU | |
| - Data: Nigerian movies, podcasts, and open-source audio | |
| """) | |
| with col2: | |
| st.markdown(""" | |
| ### Available Voices | |
| - **Female**: zainab, idera, regina, chinenye, joke, remi | |
| - **Male**: jude, tayo, umar, osagie, onye, emma | |
| ### Limitations | |
| - English to Nigerian-accented English primarily | |
| - May not capture all Nigerian accent variations | |
| - Training data includes auto-generated content | |
| """) | |
| st.markdown(""" | |
| ### Credits | |
| - YarnGPT was created by Saheed Abdulrahman, a Unilag student | |
| - Model is available as open source on [GitHub](https://github.com/saheedniyi02/yarngpt) | |
| - Web demo: [https://yarngpt.co/](https://yarngpt.co/) | |
| """) | |
| # Footer | |
| st.markdown("---") | |
| st.markdown("Developed for a Nigerian News App Podcaster API | Powered by YarnGPT") |