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Fix #2793: bug: utils.load_utils.extract_fbank 函數當遇到 雙聲道音檔時會錯誤 #2819

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LauraGPT merged 1 commit into from
Feb 27, 2026
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Fix #2793: bug: utils.load_utils.extract_fbank 函數當遇到 雙聲道音檔時會錯誤 #2819

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4 changes: 4 additions & 0 deletions funasr/utils/load_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -200,10 +200,14 @@ def extract_fbank(data, data_len=None, data_type: str = "sound", frontend=None,
data = torch.from_numpy(data)
if len(data.shape) < 2:
data = data[None, :] # data: [batch, N]
elif data.shape[0] > 1:
data = data.mean(dim=0, keepdim=True) # convert stereo/multi-channel to mono
Comment on lines +203 to +204
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@gemini-code-assist gemini-code-assist bot Feb 21, 2026

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medium

此處新增的處理邏輯與下方 torch.Tensor 分支中的代碼完全重複。建議重構此部分，在將 np.ndarray 轉換為 torch.Tensor 後，統一進行維度檢查與多聲道轉單聲道的處理，以減少代碼冗餘並提高可維護性。

data_len = [data.shape[1]] if data_len is None else data_len
elif isinstance(data, torch.Tensor):
if len(data.shape) < 2:
data = data[None, :] # data: [batch, N]
elif data.shape[0] > 1:
data = data.mean(dim=0, keepdim=True) # convert stereo/multi-channel to mono
Comment on lines +209 to +210
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medium

此處邏輯與上方重複。此外，建議檢查 isinstance(data, (list, tuple)) 的分支（第 212 行起）。如果傳入的是包含多聲道音檔的列表，目前的修改並未涵蓋該情況，這會導致在第 218 行計算 data_len 時取得錯誤的數值（聲道數而非時間長度）。建議在處理列表元素的循環中也加入相同的 downmix 邏輯以確保功能完整性。

data_len = [data.shape[1]] if data_len is None else data_len
elif isinstance(data, (list, tuple)):
data_list, data_len = [], []
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