wangjia
25acf9db6e
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>
75 lines
2.1 KiB
YAML
75 lines
2.1 KiB
YAML
# 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。
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# 跑法：rbw get dashscope-api-key | python -m asr_eval run --config config.yaml
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output_dir: results
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audio_cache: datasets/_audio_cache
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sample_limit: null # null=全量；整数=每个数据集采样上限（冒烟用小值）
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# ---------------- 数据集 ----------------
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# 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。
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datasets:
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- name: ascend # 中英混说金标准（headline）
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type: hf
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hf_id: CAiRE/ASCEND
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split: test
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text_field: transcription
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lang: zh-en
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domain: general
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- name: aishell1 # 纯普通话
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type: hf
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hf_id: AISHELL/AISHELL-1
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split: test
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text_field: text
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lang: zh
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domain: general
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trust_remote_code: true
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- name: librispeech # 纯英文
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type: hf
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hf_id: openslr/librispeech_asr
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hf_config: clean
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split: test
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text_field: text
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lang: en
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domain: general
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trust_remote_code: true
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- name: custom # 你的自录/业务场景音频
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type: custom
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manifest: manifests/custom.example.jsonl
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# ---------------- 引擎 ----------------
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# 云端 gummy 与本地开源横向对比。本地引擎需先装 extras：pip install '.[whisper]' 或 '.[funasr]'
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engines:
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- name: gummy
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type: gummy
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model: gummy-realtime-v1
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realtime_factor: 2.0 # 推流倍速（同 gummycheck）；影响延迟口径，不影响准确率
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cost_per_min: 0.09 # ⚠️占位：按 dudu 实付 DashScope 单价改（此处暂用售价 ¥9/100min）
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enabled: true
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- name: whisper-small
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type: whisper
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model_size: small
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device: cpu
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compute_type: int8
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enabled: true
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- name: whisper-large-v3
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type: whisper
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model_size: large-v3
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device: cpu
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compute_type: int8
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enabled: false # 体积大(~1.5GB)，按需开
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- name: sensevoice
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type: sensevoice # 多语，中英混说强
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device: cpu
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enabled: false
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- name: paraformer-zh
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type: funasr # 纯中 SOTA，英文/混说较弱
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device: cpu
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enabled: false
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