chore: import upstream snapshot with attribution

.github/ISSUE_TEMPLATE/migration_benchmark.md

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+---
+name: 📊 Migration Benchmark Report
+about: Share FunASR results when comparing against Whisper, OpenAI audio APIs, or cloud ASR
+labels: 'benchmark, showcase, needs triage'
+---
+
+Thanks for benchmarking FunASR on your own audio. Migration reports help new users decide whether FunASR fits their language, domain, hardware, and deployment constraints.
+
+If you need help debugging a failure, please use Bug Report or Deployment Help instead.
+
+## Summary
+
+<!-- One or two sentences: what did you compare, and what was the headline result? -->
+
+## Baseline
+
+- Baseline ASR (`Whisper`, `Whisper large-v3`, cloud provider, internal system, other):
+- Baseline runtime or API:
+- Baseline hardware or pricing tier:
+
+## FunASR setup
+
+- FunASR version:
+- Model(s):
+- Runtime path (`Python API`, `funasr-server`, `OpenAI API`, `Docker`, `WebSocket`, `vLLM`, other):
+- Device (`cuda`, `cpu`, `mps`):
+- GPU / CPU:
+- CUDA / PyTorch versions:
+- Command or script used:
+
+```bash
+
+```
+
+## Audio set
+
+- Number of files:
+- Total audio duration:
+- Language(s) / dialect(s):
+- Domain (`meeting`, `call-center`, `subtitle`, `lecture`, `media`, `noisy field audio`, other):
+- Speaker count range:
+- Sample rate / format:
+- Can any sample be shared publicly? yes/no
+
+## Results
+
+<!-- Paste the aggregate section from examples/migration/benchmark_funasr.py summary.md, or summarize your own measurement. -->
+
+```text
+
+```
+
+## Quality notes
+
+<!-- Share WER/CER if available, or human-review notes about names, numbers, punctuation, timestamps, speaker labels, and domain terms. -->
+
+## Operational notes
+
+- Model download / warmup time:
+- Steady-state throughput:
+- Memory usage:
+- Failed files or error rate:
+- Deployment blockers:
+
+## Links or artifacts
+
+<!-- Public repo, demo, blog, benchmark sheet, screenshot, anonymized transcript snippet, or architecture diagram. Do not include private audio, credentials, or customer data. -->
+
+## What should FunASR improve next?
+
+<!-- Missing docs, rough edges, model gaps, deployment friction, benchmark needs, etc. -->