feat(eval): ASR 模型评估框架

横向对比云端 gummy 与本地开源模型(faster-whisper/SenseVoice/Paraformer),
重点覆盖中英混说,产出准确率(CER/WER/MER)/速度(延迟/RTF)/资源(cpu/mem/模型大小)
对比报告。公共集(ASCEND/AISHELL/LibriSpeech)统一走 HF 适配器 + 自定义 JSONL manifest。
gummy 引擎对照 server/internal/asr/gummy.go 协议移植。

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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# dudu ASR 评估配置。改这里即可调整跑哪些引擎/数据集、采样多少。
# 跑法:rbw get dashscope-api-key | python -m asr_eval run --config config.yaml
output_dir: results
audio_cache: datasets/_audio_cache
sample_limit: null # null=全量;整数=每个数据集采样上限(冒烟用小值)
# ---------------- 数据集 ----------------
# 公共集统一走 type=hf(差异全在配置里);自定义集 type=custom 指向 JSONL manifest。
datasets:
- name: ascend # 中英混说金标准(headline
type: hf
hf_id: CAiRE/ASCEND
split: test
text_field: transcription
lang: zh-en
domain: general
- name: aishell1 # 纯普通话
type: hf
hf_id: AISHELL/AISHELL-1
split: test
text_field: text
lang: zh
domain: general
trust_remote_code: true
- name: librispeech # 纯英文
type: hf
hf_id: openslr/librispeech_asr
hf_config: clean
split: test
text_field: text
lang: en
domain: general
trust_remote_code: true
- name: custom # 你的自录/业务场景音频
type: custom
manifest: manifests/custom.example.jsonl
# ---------------- 引擎 ----------------
# 云端 gummy 与本地开源横向对比。本地引擎需先装 extraspip install '.[whisper]' 或 '.[funasr]'
engines:
- name: gummy
type: gummy
model: gummy-realtime-v1
realtime_factor: 2.0 # 推流倍速(同 gummycheck);影响延迟口径,不影响准确率
cost_per_min: 0.09 # ⚠️占位:按 dudu 实付 DashScope 单价改(此处暂用售价 ¥9/100min)
enabled: true
- name: whisper-small
type: whisper
model_size: small
device: cpu
compute_type: int8
enabled: true
- name: whisper-large-v3
type: whisper
model_size: large-v3
device: cpu
compute_type: int8
enabled: false # 体积大(~1.5GB),按需开
- name: sensevoice
type: sensevoice # 多语,中英混说强
device: cpu
enabled: false
- name: paraformer-zh
type: funasr # 纯中 SOTA,英文/混说较弱
device: cpu
enabled: false