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"""
Test script for MuseTalk WebRTC demo mode
Runs headless Chromium and captures metrics
"""
import asyncio
import json
from playwright.async_api import async_playwright
async def run_demo_test(url: str, timeout: int = 120000):
"""Run demo test and return metrics"""
async with async_playwright() as p:
# Launch headless browser
browser = await p.chromium.launch(headless=True)
context = await browser.new_context(
permissions=['microphone']
)
page = await context.new_page()
# Capture console logs
logs = []
page.on('console', lambda msg: logs.append(f"[{msg.type}] {msg.text}"))
print(f"[Test] Opening {url}")
try:
await page.goto(url, timeout=30000)
print("[Test] Page loaded, waiting for demo to complete...")
# Wait for metrics (up to 2 minutes)
try:
await page.wait_for_selector('#demo-metrics-output', timeout=timeout)
print("[Test] Metrics element found!")
metrics = await page.evaluate('() => window.__DEMO_METRICS__')
if metrics:
def fmt(v):
return f"{v:>6}" if v is not None else " N/A "
# MODEL INFORMATION
print("\n📦 MODELS USED:")
print("┌────────────────────────────────────────────────────────────┐")
print("│ Component Model Size │")
print("├────────────────────────────────────────────────────────────┤")
print("│ STT Whisper distil-large-v3-ptbr ~1.5 GB │")
print("│ LLM google/gemma-3-1b-it ~2.0 GB │")
print("│ TTS espeak-ng / ElevenLabs system │")
print("│ MuseTalk UNet UNet2DConditionModel 3.4 GB │")
print("│ MuseTalk VAE AutoencoderKL (sd-vae) 335 MB │")
print("│ Face Detection DWPose 407 MB │")
print("│ Face Parser BiSeNet 53 MB │")
print("└────────────────────────────────────────────────────────────┘")
# AVALIAÇÃO PRINCIPAL
ev = metrics.get('evaluation', {})
response_lat = ev.get('responseLatency')
ratio = ev.get('latencyRatio')
grade = ev.get('latencyGrade', 'N/A')
print("\n" + "╔" + "═"*60 + "╗")
print("║" + " "*10 + "LATÊNCIA DE RESPOSTA" + " "*30 + "║")
print("║" + " "*10 + "(áudio enviado → resposta começa)" + " "*17 + "║")
print("╠" + "═"*60 + "╣")
print(f"║" + " "*20 + f">>> {response_lat} ms <<<" + " "*22 + "║")
print("║" + " "*60 + "║")
print(f"║ Referência humana: 250 ms" + " "*31 + "║")
print(f"║ Razão: {ratio}x mais lento" + " "*25 + "║")
print(f"║ Nota: {grade}" + " "*37 + "║")
print("╚" + "═"*60 + "╝")
# Latências detalhadas - calcular tempos individuais do servidor
lat = metrics.get('latency', {})
sm = metrics.get('serverMetrics', {})
t = sm.get('timings', {}) if sm else {}
# Tempos individuais (não acumulados)
stt_time = t.get('stt_done', 0)
llm_time = t.get('llm_done', 0) - t.get('stt_done', 0) if t.get('llm_done') else 0
# TTS separado: espeak vs ElevenLabs
espeak_time = t.get('espeak_done', 0) - t.get('llm_done', 0) if t.get('espeak_done') else 0
tts_time = t.get('tts_done', 0) - t.get('llm_done', 0) if t.get('tts_done') else 0
elevenlabs_time = t.get('elevenlabs_done', 0) - t.get('llm_done', 0) if t.get('elevenlabs_done') else 0
musetalk_first = t.get('first_frame', 0) - t.get('tts_done', 0) if t.get('first_frame') else 0
musetalk_total = t.get('all_done', 0) - t.get('tts_done', 0) if t.get('all_done') else 0
print("\n📊 BREAKDOWN DA LATÊNCIA (tempos individuais):")
print(f" 1. STT (Whisper): {stt_time:>6} ms")
print(f" 2. LLM (vLLM/local): {llm_time:>6} ms")
if espeak_time:
print(f" 3. TTS espeak: {espeak_time:>6} ms ← usado para vídeo")
print(f" TTS ElevenLabs: {elevenlabs_time:>6} ms ← paralelo (áudio final)")
else:
print(f" 3. TTS: {tts_time:>6} ms")
print(f" 4. MuseTalk 1ºFrame: {musetalk_first:>6} ms")
print(f" ─────────────────────────────────")
print(f" ⭐ Latência Resposta: {t.get('first_frame', 0):>6} ms")
print(f" ")
print(f" 5. MuseTalk Total: {musetalk_total:>6} ms")
print(f" ─────────────────────────────────")
print(f" Total Pipeline: {t.get('all_done', 0):>6} ms")
# Qualidade
vq = metrics.get('videoQuality', {})
print(f"\n🎬 QUALIDADE DO VÍDEO:")
print(f" Frames: {vq.get('totalFrames', 0)}/{vq.get('expectedFrames', 0)}")
print(f" Frames perdidos: {vq.get('droppedFrames', 0)} ({vq.get('dropRate', 'N/A')})")
print(f" Travamentos: {vq.get('stutterCount', 0)}")
print(f" Max gap: {vq.get('maxFrameGap', 0)} ms")
print(f" Avg gap: {vq.get('avgFrameGap', 0)} ms")
smooth = "✓ SIM" if vq.get('isSmooth') else "✗ NÃO"
print(f" Fluido: {smooth}")
# Sincronização
sync = metrics.get('sync', {})
print(f"\n🔊 SINCRONIZAÇÃO ÁUDIO/VÍDEO:")
print(f" Offset A/V: {sync.get('audioVideoOffset', 'N/A')} ms")
synced = "✓ SIM" if sync.get('isSynced') else "✗ NÃO"
print(f" Sincronizado: {synced}")
# Server metrics
sm = metrics.get('serverMetrics', {})
if sm and sm.get('timings'):
print(f"\n🖥️ SERVIDOR (tempos acumulados):")
t = sm['timings']
stt = t.get('stt_done', 0)
llm = t.get('llm_done', 0)
espeak = t.get('espeak_done', 0)
tts = t.get('tts_done', 0)
elevenlabs = t.get('elevenlabs_done', 0)
first_frame = t.get('first_frame', 0)
all_done = t.get('all_done', 0)
print(f" STT: {stt:>6} ms")
print(f" LLM: {llm:>6} ms (+{llm - stt} ms)")
if espeak:
print(f" espeak: {espeak:>6} ms (+{espeak - llm} ms)")
print(f" TTS ready: {tts:>6} ms (+{tts - espeak} ms)")
else:
print(f" TTS: {tts:>6} ms (+{tts - llm} ms)")
print(f" First Frame: {first_frame:>6} ms")
if elevenlabs:
print(f" ElevenLabs: {elevenlabs:>6} ms (paralelo)")
print(f" Total: {all_done:>6} ms")
# MuseTalk specific timing
if tts and first_frame and all_done:
musetalk_first = first_frame - tts
musetalk_total = all_done - tts
total_frames = vq.get('totalFrames', 0)
print(f"\n🎭 MUSETALK (UNet2D + VAE inference):")
print(f" Models: UNet2DConditionModel (3.4GB) + AutoencoderKL (335MB)")
print(f" 1º Frame: {musetalk_first:>6} ms")
print(f" Total: {musetalk_total:>6} ms")
if total_frames and musetalk_total > 0:
fps = total_frames / (musetalk_total / 1000)
# Calculate realtime factor (assume 25fps output)
audio_duration_sec = total_frames / 25.0
realtime_factor = (musetalk_total / 1000) / audio_duration_sec if audio_duration_sec > 0 else 0
print(f" Frames gerados: {total_frames:>6}")
print(f" Audio duration: {audio_duration_sec:>6.2f} s")
print(f" Gen FPS: {fps:>6.1f} fps")
print(f" Realtime factor: {realtime_factor:>6.2f}x")
# Avaliação MuseTalk
if realtime_factor <= 1.0:
mt_grade = f"A+ ({1/realtime_factor:.1f}x faster than realtime)"
elif fps >= 25:
mt_grade = "A (tempo real)"
elif fps >= 15:
mt_grade = "B (aceitável)"
elif fps >= 10:
mt_grade = "C (lento)"
else:
mt_grade = "F (muito lento)"
print(f" Avaliação: {mt_grade}")
# Resultado final
print("\n" + "="*60)
fluid = metrics.get('conversationFluid', False)
if fluid:
print("✅ CONVERSA FLUIDA: SIM")
else:
print("❌ CONVERSA FLUIDA: NÃO")
if metrics.get('error'):
print(f"⚠️ ERRO: {metrics['error']}")
print("="*60)
# Print full JSON
print("\n📋 JSON Completo:")
print(json.dumps(metrics, indent=2))
return metrics
except Exception as e:
print(f"[Test] Timeout: {e}")
metrics = await page.evaluate('() => window.__DEMO_METRICS__ || null')
if metrics:
print("\n[Test] Métricas parciais:")
print(json.dumps(metrics, indent=2))
return metrics
print("\n[Test] Console logs:")
for log in logs[-30:]:
print(f" {log}")
return {"error": str(e), "logs": logs[-30:]}
except Exception as e:
print(f"[Test] Error: {e}")
return {"error": str(e)}
finally:
await browser.close()
if __name__ == "__main__":
import sys
url = sys.argv[1] if len(sys.argv) > 1 else "http://localhost:3000/?demo=true"
print(f"[Test] Running demo test on: {url}")
result = asyncio.run(run_demo_test(url))
if result and result.get('error'):
sys.exit(1)
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