"""Streamlit app for converting documents to podcasts."""
import io
import re
from pathlib import Path
import numpy as np
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
@st.cache_resource
def load_text_to_text_model():
return load_llama_cpp_model(
model_id="bartowski/Qwen2.5-7B-Instruct-GGUF/Qwen2.5-7B-Instruct-Q8_0.gguf"
)
@st.cache_resource
def load_text_to_speech_model(lang_code: str):
return load_tts_model("hexgrad/Kokoro-82M", **{"lang_code": lang_code})
def numpy_to_wav(audio_array: np.ndarray, sample_rate: int) -> io.BytesIO:
"""
Convert a numpy array to audio bytes in .wav format, ready to save into a file.
"""
wav_io = io.BytesIO()
sf.write(wav_io, audio_array, sample_rate, format="WAV")
wav_io.seek(0)
return wav_io
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
sample_rate = 24000
st.title("Document To Podcast")
st.markdown("Built with: ⭐ https://github.com/mozilla-ai/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()
tts_link = "- [hexgrad/Kokoro-82M](https://github.com/hexgrad/kokoro)"
SPEAKERS = DEFAULT_SPEAKERS
text_model = load_text_to_text_model()
st.markdown(
"For this demo, we are using the following models: \n"
"- [Qwen2.5-3B-Instruct](https://huggingface.co/bartowski/Qwen2.5-3B-Instruct-GGUF)\n"
f"{tts_link}\n"
)
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 SPEAKERS:
s.pop("id", None)
speakers = st.data_editor(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"]
)
)
if speakers[0]["voice_profile"][0] != speakers[1]["voice_profile"][0]:
raise ValueError(
"Both Kokoro speakers need to have the same language code. "
"More info here https://huggingface.co/hexgrad/Kokoro-82M/blob/main/VOICES.md"
)
# Get which language is used for generation from the first character of the Kokoro voice profile
language_code = speakers[0]["voice_profile"][0]
speech_model = load_text_to_speech_model(lang_code=language_code)
sample_rate = speech_model.sample_rate
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=sample_rate)
st.session_state.audio.append(speech)
text = ""
st.session_state.script += "}"
if st.session_state[gen_button]:
audio_np = stack_audio_segments(
st.session_state.audio, sample_rate, silence_pad=0.0
)
audio_wav = numpy_to_wav(audio_np, sample_rate)
if st.download_button(
label="Save Podcast to audio file",
data=audio_wav,
file_name="podcast.wav",
):
st.markdown("Podcast saved to disk!")
if st.download_button(
label="Save Podcast script to text file",
data=st.session_state.script,
file_name="script.txt",
):
st.markdown("Script saved to disk!")