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| import os | |
| import shutil | |
| import streamlit as st | |
| import torchaudio | |
| import IPython | |
| from tortoise.api import TextToSpeech | |
| from tortoise.utils.audio import load_voice | |
| # Initialize TextToSpeech model | |
| tts = TextToSpeech() | |
| # Constants | |
| PRESETS = ["ultra_fast", "fast", "standard", "high_quality", "very_fast"] | |
| UPLOAD_FOLDER = "./uploads" | |
| OUTPUT_FOLDER = "./output" | |
| voice_samples = None | |
| conditioning_latents = None | |
| # Create upload and output directories if they don't exist | |
| os.makedirs(UPLOAD_FOLDER, exist_ok=True) | |
| os.makedirs(OUTPUT_FOLDER, exist_ok=True) | |
| # Streamlit UI elements | |
| st.title("Tortoise Text-to-Speech App") | |
| # Upload .wav files | |
| st.sidebar.header("Upload Audio Samples") | |
| uploaded_files = st.sidebar.file_uploader( | |
| "Upload Audio Samples for a New Voice", | |
| accept_multiple_files=True, | |
| type=["wav"], | |
| ) | |
| # Create a new voice | |
| voice_name = st.sidebar.text_input("New Voice Name", help="Enter a name for your new voice.") | |
| if st.sidebar.button("Create Voice") and voice_name.strip() != "": | |
| new_voice_name = voice_name.strip().replace(" ", "_") | |
| voices_dir = f"./tortoise/voices/{new_voice_name}/" | |
| if os.path.exists(voices_dir): | |
| shutil.rmtree(voices_dir) | |
| os.makedirs(voices_dir) | |
| for index, uploaded_file in enumerate(uploaded_files): | |
| bytes_data = uploaded_file.read() | |
| with open(f"{voices_dir}voice_sample{index}.wav", "wb") as wav_file: | |
| wav_file.write(bytes_data) | |
| st.sidebar.success(f"Voice '{voice_name}' created successfully!") | |
| voice_samples, conditioning_latents = load_voice(voice_name) | |
| # Input text and settings | |
| st.header("Text-to-Speech Generation") | |
| text = st.text_area( | |
| "Enter Text", | |
| help="Enter the text you want to convert to speech.", | |
| value="Joining two modalities results in a surprising increase in generalization! What would happen if we combined them all?", | |
| ) | |
| preset = st.selectbox("Preset", PRESETS, help="Select a voice preset.") | |
| voices = [v for v in os.listdir("tortoise/voices") if v != "cond_latent_example"] | |
| voice = st.selectbox("Voice", voices, help="Select a voice to use for generation.") | |
| # Generate speech | |
| if st.button("Generate Speech"): | |
| if voice_name.strip() == "": | |
| st.warning("Please create a voice first.") | |
| else: | |
| st.info("Generating speech...") | |
| # Load voice samples | |
| # voice_samples, conditioning_latents = load_voice(voice) | |
| print(voice_samples) | |
| # Generate speech with Tortoise | |
| gen = tts.tts_with_preset( | |
| text, | |
| voice_samples=voice_samples, | |
| conditioning_latents=conditioning_latents, | |
| preset=preset, | |
| ) | |
| # Save and display the generated audio | |
| output_path = os.path.join(OUTPUT_FOLDER, "generated.wav") | |
| torchaudio.save(output_path, gen.squeeze(0).cpu(), 24000) | |
| print(output_path) | |
| # Log the path of the generated audio | |
| st.write(f"Generated audio saved at: {output_path}") | |
| # Display the generated audio | |
| st.subheader("Generated Output") | |
| st.audio(output_path, format="audio/wav") | |
| if st.checkbox("Play Audio"): | |
| IPython.display.Audio(output_path) | |
| st.success("Speech generated successfully!") | |
| # Clean up uploaded files and output directory | |
| if st.sidebar.button("Clean Up"): | |
| shutil.rmtree(UPLOAD_FOLDER) | |
| os.makedirs(UPLOAD_FOLDER, exist_ok=True) | |
| shutil.rmtree(OUTPUT_FOLDER) | |
| os.makedirs(OUTPUT_FOLDER, exist_ok=True) | |
| st.sidebar.success("Clean up completed!") | |
| # Display information | |
| st.sidebar.header("Information") | |
| st.sidebar.markdown( | |
| "This app allows you to create a new voice by uploading .wav files. You can then generate speech " | |
| "using the selected voice and preset. You can play the generated audio and clean up uploaded files and the output directory when needed." | |
| ) | |