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