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2026-07-13 13:25:10 +08:00

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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. -->