| import tempfile |
| import traceback |
| from pathlib import Path |
| import gradio as gr |
| from pipeline import get_pipeline, to_json, to_csv, to_abab_text |
|
|
| UPLOAD_DIR = Path(tempfile.gettempdir()) / "speech_annotation" |
| UPLOAD_DIR.mkdir(parents=True, exist_ok=True) |
| _last_segments = [] |
|
|
| SPEAKER_COLORS = [ |
| ("#4F46E5", "#EEF2FF"), |
| ("#059669", "#ECFDF5"), |
| ("#DC2626", "#FEF2F2"), |
| ("#D97706", "#FFFBEB"), |
| ("#7C3AED", "#F5F3FF"), |
| ("#0891B2", "#ECFEFF"), |
| ("#DB2777", "#FDF2F8"), |
| ("#65A30D", "#F7FEE7"), |
| ("#EA580C", "#FFF7ED"), |
| ("#0284C7", "#F0F9FF"), |
| ] |
|
|
| def make_conversation_html(segments): |
| if not segments: |
| return "" |
| speaker_list = list(dict.fromkeys(s.speaker for s in segments)) |
| color_map = {spk: SPEAKER_COLORS[i % len(SPEAKER_COLORS)] for i, spk in enumerate(speaker_list)} |
| legend_items = "".join( |
| f"<span style='display:inline-flex;align-items:center;gap:6px;margin-right:16px'>" |
| f"<span style='width:12px;height:12px;border-radius:50%;background:{color_map[spk][0]}'></span>" |
| f"<span style='font-weight:600;color:{color_map[spk][0]}'>Speaker {spk}</span></span>" |
| for spk in speaker_list |
| ) |
| legend = f"<div style='padding:12px 16px;border-bottom:1px solid #e5e7eb;display:flex;flex-wrap:wrap;gap:4px'>{legend_items}</div>" |
| bubbles = "" |
| for seg in segments: |
| fg, bg = color_map[seg.speaker] |
| align = "flex-end" if speaker_list.index(seg.speaker) % 2 == 1 else "flex-start" |
| radius = "4px 16px 16px 16px" if align == "flex-start" else "16px 4px 16px 16px" |
| ta = "text-align:right;" if align == "flex-end" else "" |
| bubbles += ( |
| f"<div style='display:flex;justify-content:{align};margin:6px 12px'>" |
| f"<div style='max-width:75%'>" |
| f"<div style='font-size:11px;color:#6b7280;margin-bottom:3px;{ta}'>" |
| f"<span style='font-weight:600;color:{fg}'>Speaker {seg.speaker}</span>" |
| f" · {seg.start_fmt} → {seg.end_fmt}</div>" |
| f"<div style='background:{bg};border:1px solid {fg}30;color:#111827;" |
| f"padding:10px 14px;border-radius:{radius};font-size:14px;line-height:1.5'>" |
| f"{seg.text}</div></div></div>" |
| ) |
| return ( |
| f"<div style='border:1px solid #e5e7eb;border-radius:12px;overflow:hidden;" |
| f"font-family:system-ui,sans-serif;background:white'>" |
| f"{legend}" |
| f"<div style='height:480px;overflow-y:auto;padding:8px 0;background:#f9fafb'>" |
| f"{bubbles}</div></div>" |
| ) |
|
|
| def make_table_html(segments): |
| if not segments: |
| return "" |
| speaker_list = list(dict.fromkeys(s.speaker for s in segments)) |
| color_map = {spk: SPEAKER_COLORS[i % len(SPEAKER_COLORS)] for i, spk in enumerate(speaker_list)} |
| rows = "".join( |
| f"<tr style='border-bottom:1px solid #f3f4f6'>" |
| f"<td style='padding:8px 12px'><span style='background:{color_map[s.speaker][1]};" |
| f"color:{color_map[s.speaker][0]};padding:3px 10px;border-radius:99px;" |
| f"font-weight:700;font-size:13px'>{s.speaker}</span></td>" |
| f"<td style='padding:8px 12px;color:#6b7280;font-size:13px'>{s.start_fmt}</td>" |
| f"<td style='padding:8px 12px;color:#6b7280;font-size:13px'>{s.end_fmt}</td>" |
| f"<td style='padding:8px 12px;font-size:14px;color:#111827'>{s.text}</td>" |
| f"</tr>" |
| for s in segments |
| ) |
| return ( |
| f"<div style='border:1px solid #e5e7eb;border-radius:12px;overflow:hidden;" |
| f"font-family:system-ui,sans-serif'>" |
| f"<table style='width:100%;border-collapse:collapse'>" |
| f"<thead><tr style='background:#1e3a5f;color:white'>" |
| f"<th style='padding:10px 12px;text-align:left'>Speaker</th>" |
| f"<th style='padding:10px 12px;text-align:left'>Start</th>" |
| f"<th style='padding:10px 12px;text-align:left'>End</th>" |
| f"<th style='padding:10px 12px;text-align:left'>Transcript</th>" |
| f"</tr></thead><tbody>{rows}</tbody></table></div>" |
| ) |
|
|
| def process_audio(audio_path, num_speakers): |
| global _last_segments |
| if audio_path is None: |
| return "Please upload an audio file first.", "", "" |
| try: |
| pipeline = get_pipeline() |
| n = int(num_speakers) if num_speakers and int(num_speakers) > 0 else 0 |
| segments = pipeline.process(audio_path, num_speakers=n) |
| except Exception as e: |
| return f"Error: {e}\n{traceback.format_exc()}", "", "" |
| if not segments: |
| return "No speech detected.", "", "" |
| _last_segments = segments |
| unique = len(set(s.speaker for s in segments)) |
| return f"Done - {len(segments)} segments, {unique} speaker(s) detected", make_conversation_html(segments), make_table_html(segments) |
|
|
| def export_json(): |
| if not _last_segments: return None |
| out = str(UPLOAD_DIR / "annotation.json") |
| to_json(_last_segments, out) |
| return out |
|
|
| def export_csv(): |
| if not _last_segments: return None |
| out = str(UPLOAD_DIR / "annotation.csv") |
| to_csv(_last_segments, out) |
| return out |
|
|
| with gr.Blocks(title="Speech Annotation Pipeline") as demo: |
| gr.Markdown("# Speech Annotation Pipeline\n*Upload audio · Detect speakers · Export transcript*") |
| with gr.Row(): |
| with gr.Column(scale=1): |
| audio_input = gr.Audio(label="Upload Audio (.wav / .mp3 / .flac)", type="filepath") |
| num_speakers = gr.Slider(minimum=0, maximum=10, step=1, value=0, |
| label="Number of speakers (0 = auto-detect)") |
| run_btn = gr.Button("Run Annotation", variant="primary") |
| status_box = gr.Textbox(label="Status", value="Ready.", interactive=False) |
| with gr.Row(): |
| json_btn = gr.Button("Export JSON") |
| csv_btn = gr.Button("Export CSV") |
| json_file = gr.File(label="JSON Download") |
| csv_file = gr.File(label="CSV Download") |
| with gr.Column(scale=2): |
| gr.Markdown("### Conversation View") |
| conversation_html = gr.HTML(value="<div style='height:480px;border:1px solid #e5e7eb;border-radius:12px;display:flex;align-items:center;justify-content:center;color:#9ca3af'>Transcript will appear here after processing</div>") |
| gr.Markdown("### Segment Table") |
| table_html = gr.HTML(value="") |
|
|
| run_btn.click(fn=process_audio, inputs=[audio_input, num_speakers], |
| outputs=[status_box, conversation_html, table_html]) |
| json_btn.click(fn=export_json, inputs=[], outputs=[json_file]) |
| csv_btn.click(fn=export_csv, inputs=[], outputs=[csv_file]) |
|
|
| if __name__ == "__main__": |
| demo.launch(server_name="0.0.0.0", server_port=7860) |
