粵語 ASR 模型橫向評測

目前（2026 上半年）市面上有以下號稱最準嘅粵語語音識別模型：

• FunAudioLLM/SenseVoiceSmall
• FireRedTeam/FireRedASR2-AED
• Qwen/Qwen3-ASR-0.6B
• Qwen/Qwen3-ASR-1.7B
• zai-org/GLM-ASR-Nano-2512

本倉庫採用張悦楷講古語音數據集 CanCLID/zoengjyutgaai 中每部作品（《三國演義》、《水滸傳》、《走進毛澤東的最後歲月》、《鹿鼎記》）嘅前 5 集，共 20 集作為測試數據，評測每隻模型嘅CER。

Folder convention

• input/: input audio files (.opus, .wav, .mp3, etc.)
• reference/: golden SRT with matching stem names
• predicted/: generated outputs (default)

Example:

• input/001.opus
• reference/001.srt

點開始評測

確保已經安裝

• uv
• ffmpeg
• NVIDIA driver

然後跑：

# 安裝依賴
uv venv
uv pip install -r requirements.txt
# 開始評測
uv run python scripts/sensevoice_srt_cer.py --output-dir predicted/sensevoicesmall
uv run python scripts/fireredasr2_aed_srt_cer.py --output-dir predicted/fireredasr2
uv run python scripts/qwen3_asr_srt_cer.py --output-dir predicted/qwen3asr_0_6b
uv run python scripts/qwen3_asr_1_7b_srt_cer.py --output-dir predicted/qwen3asr_1_7b
uv run python scripts/glm_asr_nano_2512_srt_cer.py --output-dir predicted/glmasr

如果想跑單個文件：

uv run python scripts/sensevoice_srt_cer.py \
  --audio input/001.opus \
  --golden-srt reference/001.srt \
  --output-srt predicted/sensevoicesmall/001.sensevoice.srt

Notes:

• First run downloads models from Hugging Face / ModelScope and may clone model source repos to .cache/.
• Device defaults to auto (uses cuda:0 if available, otherwise cpu).
• Qwen3-ASR scripts should use transformers==4.57.6 (same as upstream qwen-asr pin).
• Using newer transformers versions for Qwen may cause decode failure / repetitive hallucination outputs.
• GLM-ASR-Nano-2512 currently needs transformers from GitHub source.
• Qwen and GLM have conflicting transformers requirements; run them in separate virtual environments.

輸出結果都喺

• <output-dir>/<stem>.<model>.srt
• <output-dir>/<stem>.<model>.analysis.md (error analysis report)

模型總結

• <summary-dir>/<summary-name>.md (default: summary/<model>.md)
• 可以用 --summary-dir 同 --summary-name 覆蓋預設位置同檔名。

指標

• CER（含標點符號）
• CER（唔含標點符號）
• Micro CER: 所有音頻加埋嘅 CER (sum(edit_distance) / sum(reference_chars))
• Macro CER: 單個音頻文件 CER 嘅平均數（唔同長度嘅音頻都相同權重）
• Total runtime (s) (batch wall-clock for evaluated files)
• End-to-end RTF (runtime / audio_duration, lower is faster)

Benchmark setup note:

• All benchmark scripts default to fsmn-vad (FunASR/ModelScope).
• All benchmark scripts use the same default max VAD segment length: 20s (--vad-max-segment-ms=20000).
• scripts/fireredasr2_aed_srt_cer.py and scripts/qwen3_asr_1_7b_srt_cer.py support VAD ablation via --vad-backend {fsmn,firered}.

評測結果

20 集數據

• 分段設定：20s（--vad-max-segment-ms=20000）

• FireRed 20s benchmark was run with --segment-batch-size 64 to avoid CUDA OOM at batch size 128.

Qwen3-ASR-1.7B VAD 對比測試

用相同嘅20集，同樣係 Qwen3-ASR-1.7B，對比 fsmn-vad 同 FireRedVAD

• In this ablation, fsmn-vad is better than FireRedVAD for Qwen 1.7B (lower CER and faster runtime).
• Treat this table as a VAD backend A/B result (relative comparison between two backends under the same run conditions).
• To keep both ablation summaries, run with --summary-name qwen3_asr_1_7b_cmp_fsmn and --summary-name qwen3_asr_1_7b_cmp_fireredvad.

Current optimizations

• Auto device selection (cuda:0 first, then CPU fallback)
• VAD segmentation enabled with default max segment length 20s
• Batched segment inference for ASR (--segment-batch-size, auto-tuned by device)
• Cantonese post-processing with OpenCC s2hk and custom regex corrections
• Detailed markdown analysis with top substitutions/deletions/insertions, mismatch examples, and aggregated error patterns across files