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| """Streamlit app for converting documents to podcasts.""" | |
| import re | |
| from pathlib import Path | |
| import soundfile as sf | |
| import streamlit as st | |
| from document_to_podcast.inference.text_to_speech import text_to_speech | |
| from document_to_podcast.preprocessing import DATA_LOADERS, DATA_CLEANERS | |
| from document_to_podcast.inference.model_loaders import ( | |
| load_llama_cpp_model, | |
| load_tts_model, | |
| ) | |
| from document_to_podcast.config import DEFAULT_PROMPT, DEFAULT_SPEAKERS, Speaker | |
| from document_to_podcast.inference.text_to_text import text_to_text_stream | |
| from document_to_podcast.utils import stack_audio_segments | |
| def load_text_to_text_model(): | |
| return load_llama_cpp_model( | |
| model_id="allenai/OLMoE-1B-7B-0924-Instruct-GGUF/olmoe-1b-7b-0924-instruct-q8_0.gguf" | |
| ) | |
| def load_text_to_speech_model(): | |
| return load_tts_model("OuteAI/OuteTTS-0.2-500M-GGUF/OuteTTS-0.2-500M-FP16.gguf") | |
| script = "script" | |
| audio = "audio" | |
| gen_button = "generate podcast button" | |
| if script not in st.session_state: | |
| st.session_state[script] = "" | |
| if audio not in st.session_state: | |
| st.session_state.audio = [] | |
| if gen_button not in st.session_state: | |
| st.session_state[gen_button] = False | |
| def gen_button_clicked(): | |
| st.session_state[gen_button] = True | |
| st.title("Document To Podcast") | |
| st.header("Upload a File") | |
| uploaded_file = st.file_uploader( | |
| "Choose a file", type=["pdf", "html", "txt", "docx", "md"] | |
| ) | |
| st.header("Or Enter a Website URL") | |
| url = st.text_input("URL", placeholder="https://blog.mozilla.ai/...") | |
| if uploaded_file is not None or url: | |
| st.divider() | |
| st.header("Loading and Cleaning Data") | |
| st.markdown( | |
| "[Docs for this Step](https://mozilla-ai.github.io/document-to-podcast/step-by-step-guide/#step-1-document-pre-processing)" | |
| ) | |
| st.divider() | |
| if uploaded_file: | |
| extension = Path(uploaded_file.name).suffix | |
| raw_text = DATA_LOADERS[extension](uploaded_file) | |
| else: | |
| extension = ".html" | |
| raw_text = DATA_LOADERS["url"](url) | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.subheader("Raw Text") | |
| st.text_area( | |
| f"Number of characters before cleaning: {len(raw_text)}", | |
| f"{raw_text[:500]} . . .", | |
| ) | |
| clean_text = DATA_CLEANERS[extension](raw_text) | |
| with col2: | |
| st.subheader("Cleaned Text") | |
| st.text_area( | |
| f"Number of characters after cleaning: {len(clean_text)}", | |
| f"{clean_text[:500]} . . .", | |
| ) | |
| st.session_state["clean_text"] = clean_text | |
| st.divider() | |
| if "clean_text" in st.session_state: | |
| clean_text = st.session_state["clean_text"] | |
| st.divider() | |
| st.header("Downloading and Loading models") | |
| st.markdown( | |
| "[Docs for this Step](https://mozilla-ai.github.io/document-to-podcast/step-by-step-guide/#step-2-podcast-script-generation)" | |
| ) | |
| st.divider() | |
| text_model = load_text_to_text_model() | |
| speech_model = load_text_to_speech_model() | |
| st.markdown( | |
| "For this demo, we are using the following models: \n" | |
| "- [OLMoE-1B-7B-0924-Instruct](https://huggingface.co./allenai/OLMoE-1B-7B-0924-Instruct-GGUF)\n" | |
| "- [OuteAI/OuteTTS-0.2-500M](https://huggingface.co./OuteAI/OuteTTS-0.2-500M-GGUF)" | |
| ) | |
| st.markdown( | |
| "You can check the [Customization Guide](https://mozilla-ai.github.io/document-to-podcast/customization/)" | |
| " for more information on how to use different models." | |
| ) | |
| # ~4 characters per token is considered a reasonable default. | |
| max_characters = text_model.n_ctx() * 4 | |
| if len(clean_text) > max_characters: | |
| st.warning( | |
| f"Input text is too big ({len(clean_text)})." | |
| f" Using only a subset of it ({max_characters})." | |
| ) | |
| clean_text = clean_text[:max_characters] | |
| st.divider() | |
| st.header("Podcast generation") | |
| st.markdown( | |
| "[Docs for this Step](https://mozilla-ai.github.io/document-to-podcast/step-by-step-guide/#step-3-audio-podcast-generation)" | |
| ) | |
| st.divider() | |
| st.subheader("Speaker configuration") | |
| for s in DEFAULT_SPEAKERS: | |
| s.pop("id", None) | |
| speakers = st.data_editor(DEFAULT_SPEAKERS, num_rows="dynamic") | |
| if st.button("Generate Podcast", on_click=gen_button_clicked): | |
| for n, speaker in enumerate(speakers): | |
| speaker["id"] = n + 1 | |
| speakers_str = "\n".join( | |
| str(Speaker.model_validate(speaker)) | |
| for speaker in speakers | |
| if all( | |
| speaker.get(x, None) for x in ["name", "description", "voice_profile"] | |
| ) | |
| ) | |
| system_prompt = DEFAULT_PROMPT.replace("{SPEAKERS}", speakers_str) | |
| with st.spinner("Generating Podcast..."): | |
| text = "" | |
| for chunk in text_to_text_stream( | |
| clean_text, text_model, system_prompt=system_prompt.strip() | |
| ): | |
| text += chunk | |
| if text.endswith("\n") and "Speaker" in text: | |
| st.session_state.script += text | |
| st.write(text) | |
| speaker_id = re.search(r"Speaker (\d+)", text).group(1) | |
| voice_profile = next( | |
| speaker["voice_profile"] | |
| for speaker in speakers | |
| if speaker["id"] == int(speaker_id) | |
| ) | |
| with st.spinner("Generating Audio..."): | |
| speech = text_to_speech( | |
| text.split(f'"Speaker {speaker_id}":')[-1], | |
| speech_model, | |
| voice_profile, | |
| ) | |
| st.audio(speech, sample_rate=speech_model.sample_rate) | |
| st.session_state.audio.append(speech) | |
| text = "" | |
| if st.session_state[gen_button]: | |
| if st.button("Save Podcast to audio file"): | |
| st.session_state.audio = stack_audio_segments( | |
| st.session_state.audio, speech_model.audio_codec.sr | |
| ) | |
| sf.write( | |
| "podcast.wav", | |
| st.session_state.audio, | |
| samplerate=speech_model.sample_rate, | |
| ) | |
| st.markdown("Podcast saved to disk!") | |
| if st.button("Save Podcast script to text file"): | |
| with open("script.txt", "w") as f: | |
| st.session_state.script += "}" | |
| f.write(st.session_state.script) | |
| st.markdown("Script saved to disk!") | |