测试环境 Test Environment

• GPU: NVIDIA Tesla V100-32GB
• fast-vc Version: 0.1.0
• Test Date: 2025-06-15

测试参数 Test Parameters

• 采样率 Sample Rate: 16000 Hz
• 音频格式 Audio Format: 16-bit PCM
• 模型 Model: seed-vc v1.0 tiny
• Chunk Size: 500ms
• 扩散步数 Diffusion Steps: 10

性能指标 Performance Metrics

并发数量	Worker	首token延迟	端到端延迟	平均chunk延迟	平均RTF	中位数RTF	P95 RTF
Concurrency	Worker	First Token Latency	End-to-End Latency	Avg Chunk Latency	Avg RTF	Median RTF	P95 RTF
1	6	257.0	264.0	262.1	0.56	0.44	1.20
2	6	200.5	289.0	290.6	0.60	0.48	1.28
3	6	169.3	466.7	291.9	0.60	0.55	1.03

时间单位: 毫秒 (ms)
Time Unit: Milliseconds (ms)
✅ 所有测试场景均达到实时处理要求 (RTF < 1.0)
✅ All test scenarios meet real-time processing requirements (RTF < 1.0)

测试结论 Test Conclusions

• Tesla V100-32GB 推荐生产配置: 可稳定支持2-3并发。
• Tesla V100-32GB Recommended Production Configuration: Can stably support 2-3 concurrent requests.

ps:

• 你可以运行 python examples/websocket/concurrent_ws_client.py 来测试fast-vc在你本地的性能表现。
• You can run python examples/websocket/concurrent_ws_client.py to test the performance of fast-vc on your local machine.