|
|
import gradio as gr |
|
|
import pixeltable as pxt |
|
|
from pixeltable.iterators import DocumentSplitter |
|
|
from pixeltable.functions import openai |
|
|
import os |
|
|
import requests |
|
|
import tempfile |
|
|
|
|
|
def process_document(pdf_file, api_key, voice_choice, style_choice, chunk_size, temperature, max_tokens, progress=gr.Progress()): |
|
|
try: |
|
|
os.environ['OPENAI_API_KEY'] = api_key |
|
|
|
|
|
progress(0.1, desc="Initializing...") |
|
|
pxt.drop_dir('document_audio', force=True) |
|
|
pxt.create_dir('document_audio') |
|
|
|
|
|
docs = pxt.create_table( |
|
|
'document_audio.documents', |
|
|
{ |
|
|
'document': pxt.DocumentType(), |
|
|
'voice': pxt.StringType(), |
|
|
'style': pxt.StringType() |
|
|
} |
|
|
) |
|
|
|
|
|
progress(0.2, desc="Processing document...") |
|
|
docs.insert([{ |
|
|
'document': pdf_file.name, |
|
|
'voice': voice_choice, |
|
|
'style': style_choice |
|
|
}]) |
|
|
|
|
|
chunks = pxt.create_view( |
|
|
'document_audio.chunks', |
|
|
docs, |
|
|
iterator=DocumentSplitter.create( |
|
|
document=docs.document, |
|
|
separators='token_limit', |
|
|
limit=chunk_size |
|
|
) |
|
|
) |
|
|
|
|
|
progress(0.4, desc="Text processing...") |
|
|
chunks['content_response'] = openai.chat_completions( |
|
|
messages=[ |
|
|
{ |
|
|
'role': 'system', |
|
|
'content': """Transform this text segment into clear, concise content. |
|
|
Structure: |
|
|
1. Core concepts and points |
|
|
2. Supporting details |
|
|
3. Key takeaways""" |
|
|
}, |
|
|
{'role': 'user', 'content': chunks.text} |
|
|
], |
|
|
model='gpt-4o-mini-2024-07-18', |
|
|
max_tokens=max_tokens, |
|
|
temperature=temperature |
|
|
) |
|
|
chunks['content'] = chunks.content_response['choices'][0]['message']['content'] |
|
|
|
|
|
progress(0.6, desc="Script generation...") |
|
|
chunks['script_response'] = openai.chat_completions( |
|
|
messages=[ |
|
|
{ |
|
|
'role': 'system', |
|
|
'content': f"""Convert content to audio script. |
|
|
Style: {docs.style} |
|
|
Format: |
|
|
- Clear sentence structures |
|
|
- Natural pauses (...) |
|
|
- Term definitions when needed |
|
|
- Proper transitions |
|
|
- Appropriate pronunciation guidance""" |
|
|
}, |
|
|
{'role': 'user', 'content': chunks.content} |
|
|
], |
|
|
model='gpt-4o-mini-2024-07-18', |
|
|
max_tokens=max_tokens, |
|
|
temperature=temperature |
|
|
) |
|
|
chunks['script'] = chunks.script_response['choices'][0]['message']['content'] |
|
|
|
|
|
progress(0.8, desc="Audio synthesis...") |
|
|
@pxt.udf(return_type=pxt.AudioType()) |
|
|
def generate_audio(script: str, voice: str): |
|
|
if not script or not voice: |
|
|
return None |
|
|
try: |
|
|
response = requests.post( |
|
|
"https://api.openai.com/v1/audio/speech", |
|
|
headers={"Authorization": f"Bearer {api_key}"}, |
|
|
json={"model": "tts-1", "input": script, "voice": voice} |
|
|
) |
|
|
if response.status_code == 200: |
|
|
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.mp3') |
|
|
temp_file.write(response.content) |
|
|
temp_file.close() |
|
|
return temp_file.name |
|
|
except Exception as e: |
|
|
print(f"Error in audio synthesis: {e}") |
|
|
return None |
|
|
|
|
|
chunks['audio'] = generate_audio(chunks.script, docs.voice) |
|
|
|
|
|
audio_path = chunks.select(chunks.audio).tail(1)['audio'][0] |
|
|
|
|
|
results = chunks.select(chunks.content, chunks.script).collect() |
|
|
|
|
|
display_data = [ |
|
|
[f"Segment {idx + 1}", row['content'], row['script']] |
|
|
for idx, row in enumerate(results) |
|
|
] |
|
|
|
|
|
progress(1.0, desc="Complete") |
|
|
return display_data, audio_path, "Processing complete" |
|
|
|
|
|
except Exception as e: |
|
|
return None, None, f"Error: {str(e)}" |
|
|
|
|
|
with gr.Blocks(theme=gr.themes.Base()) as demo: |
|
|
gr.Markdown( |
|
|
""" |
|
|
<div> |
|
|
<img src="https://raw.githubusercontent.com/pixeltable/pixeltable/main/docs/source/data/pixeltable-logo-large.png" alt="Pixeltable" style="max-width: 200px; margin-bottom: 20px;" /> |
|
|
<h1 style="margin-bottom: 0.5em;">📄 Document to Audio Synthesis 🎧</h1> |
|
|
</div> |
|
|
""" |
|
|
) |
|
|
|
|
|
with gr.Row(): |
|
|
with gr.Column(): |
|
|
with gr.Accordion("🎯 What does it do?", open=False): |
|
|
gr.Markdown(""" |
|
|
1. 📄 **Document Processing:** PDF extraction and token-based chunking |
|
|
2. 🤖 **Content Pipeline:** LLM-powered text optimization and script generation |
|
|
3. 🔊 **Audio Generation:** Neural TTS synthesis with voice modulation |
|
|
""") |
|
|
with gr.Column(): |
|
|
with gr.Accordion("⚡ How does it work?", open=False): |
|
|
gr.Markdown(""" |
|
|
1. 📑 **Segmentation:** Token-based document chunking with configurable limits |
|
|
2. 🔍 **Transformation:** Dual-pass LLM processing with temperature control |
|
|
3. 🎵 **Synthesis:** OpenAI TTS with multi-voice capability |
|
|
""") |
|
|
|
|
|
gr.HTML( |
|
|
""" |
|
|
<div style="background-color: #FFF3CD; border: 1px solid #FFEEBA; padding: 1rem; margin: 1rem 0; border-radius: 4px;"> |
|
|
<p style="margin: 0; color: #856404;"> |
|
|
⚠️ <strong>API Cost Notice:</strong> This application uses OpenAI's Text-to-Speech API which incurs costs per use. |
|
|
See <a href="https://platform.openai.com/docs/guides/text-to-speech" target="_blank" style="color: #856404; text-decoration: underline;">OpenAI's TTS Documentation</a> |
|
|
for current pricing information. |
|
|
</p> |
|
|
</div> |
|
|
""" |
|
|
) |
|
|
|
|
|
with gr.Row(): |
|
|
with gr.Column(): |
|
|
with gr.Accordion("🔑 Input & Voice", open=True): |
|
|
api_key = gr.Textbox( |
|
|
label="OpenAI API Key", |
|
|
placeholder="sk-...", |
|
|
type="password" |
|
|
) |
|
|
file_input = gr.File( |
|
|
label="PDF Document", |
|
|
file_types=[".pdf"] |
|
|
) |
|
|
|
|
|
with gr.Column(): |
|
|
with gr.Accordion("⚙️ Processing Configuration", open=True): |
|
|
style_select = gr.Radio( |
|
|
choices=["Technical", "Narrative", "Instructional", "Descriptive"], |
|
|
value="Technical", |
|
|
label="💫 Style" |
|
|
) |
|
|
with gr.Row(): |
|
|
voice_select = gr.Radio( |
|
|
choices=["alloy", "echo", "fable", "onyx", "nova", "shimmer"], |
|
|
value="onyx", |
|
|
label="🎙️ Voice Model" |
|
|
) |
|
|
with gr.Row(): |
|
|
chunk_size = gr.Slider( |
|
|
minimum=100, maximum=1000, value=300, step=50, |
|
|
label="📏 Chunk Size" |
|
|
) |
|
|
temperature = gr.Slider( |
|
|
minimum=0, maximum=1, value=0.7, step=0.1, |
|
|
label="🌡️ Temperature" |
|
|
) |
|
|
max_tokens = gr.Slider( |
|
|
minimum=100, maximum=1000, value=300, step=50, |
|
|
label="📊 Tokens" |
|
|
) |
|
|
with gr.Row(): |
|
|
process_btn = gr.Button("🚀 Generate Audio", variant="primary", scale=2) |
|
|
|
|
|
with gr.Tabs(): |
|
|
with gr.TabItem("📝 Content"): |
|
|
output_table = gr.Dataframe( |
|
|
headers=["🔍 Segment", "📄 Content", "🎭 Script"], |
|
|
wrap=True |
|
|
) |
|
|
with gr.TabItem("🎧 Audio"): |
|
|
with gr.Row(): |
|
|
with gr.Column(scale=2): |
|
|
audio_output = gr.Audio( |
|
|
label="🔊 Generated Audio", |
|
|
type="filepath", |
|
|
show_download_button=True |
|
|
) |
|
|
with gr.Column(scale=1): |
|
|
with gr.Accordion("📚 Technical Notes", open=True): |
|
|
gr.Markdown(""" |
|
|
- 🎯 Temperature < 0.5: Deterministic output |
|
|
- 📏 Chunk size affects token context |
|
|
- 🎙️ Voice models vary in prosody |
|
|
- 💰 API usage is billed per character |
|
|
""") |
|
|
|
|
|
gr.HTML( |
|
|
""" |
|
|
<div style="text-align: center; margin-top: 1rem; padding-top: 1rem; border-top: 1px solid #ccc;"> |
|
|
<p style="margin: 0; color: #666; font-size: 0.8em;"> |
|
|
🚀 Powered by <a href="https://github.com/pixeltable/pixeltable" target="_blank" style="color: #F25022; text-decoration: none;">Pixeltable</a> |
|
|
| 📚 <a href="https://docs.pixeltable.com" target="_blank" style="color: #666;">Docs</a> |
|
|
| 🤗 <a href="https://huggingface.co/spaces/Pixeltable" target="_blank" style="color: #666;">HF Space</a> |
|
|
</p> |
|
|
</div> |
|
|
""" |
|
|
) |
|
|
|
|
|
def update_interface(pdf_file, api_key, voice, style, chunk_size, temperature, max_tokens): |
|
|
return process_document( |
|
|
pdf_file, api_key, voice, style, chunk_size, temperature, max_tokens |
|
|
) |
|
|
|
|
|
process_btn.click( |
|
|
update_interface, |
|
|
inputs=[ |
|
|
file_input, api_key, voice_select, style_select, |
|
|
chunk_size, temperature, max_tokens |
|
|
], |
|
|
outputs=[output_table, audio_output] |
|
|
) |
|
|
|
|
|
if __name__ == "__main__": |
|
|
demo.launch() |
