huggingface/transformers · error · ValueError

Length of the window ({window_length}) may not be larger tha

Error message

Length of the window ({window_length}) may not be larger than frame_length ({frame_length})

What it means

Thrown by `window_function` when `frame_length` is provided and `window_length > frame_length`. The function zero-pads the window into a frame_length-sized buffer; a window longer than the target frame cannot fit, so the combination is rejected. Without frame_length the window is returned unpadded.

Source

Thrown at src/transformers/audio_utils.py:798

    if name == "boxcar":
        window = np.ones(length)
    elif name in ["hamming", "hamming_window"]:
        window = np.hamming(length)
    elif name in ["hann", "hann_window"]:
        window = np.hanning(length)
    elif name == "povey":
        window = np.power(np.hanning(length), 0.85)
    else:
        raise ValueError(f"Unknown window function '{name}'")

    if periodic:
        window = window[:-1]

    if frame_length is None:
        return window

    if window_length > frame_length:
        raise ValueError(
            f"Length of the window ({window_length}) may not be larger than frame_length ({frame_length})"
        )

    padded_window = np.zeros(frame_length)
    offset = (frame_length - window_length) // 2 if center else 0
    padded_window[offset : offset + window_length] = window
    return padded_window


# Note: This method processes a single waveform. For batch processing, use spectrogram_batch().
def spectrogram(
    waveform: np.ndarray,
    window: np.ndarray,
    frame_length: int,
    hop_length: int,
    fft_length: int | None = None,
    power: float | None = 1.0,
    center: bool = True,

View on GitHub (pinned to a597f97485)

Solutions

1. Make window_length <= frame_length (usually window_length == frame_length for STFT)
2. If you need the window centered inside a larger FFT buffer, keep frame_length larger than window_length on purpose
3. Verify the config values feeding both parameters if the call comes from a processor

Example fix

// before
window = window_function(window_length=512, frame_length=400)  # ValueError

// after
window = window_function(window_length=400, frame_length=512)  # centered, zero-padded

if frame_length is not None:
    assert window_length <= frame_length, f"window_length ({window_length}) must be <= frame_length ({frame_length})"
window = window_function(window_length, name="hann", frame_length=frame_length)

def window_fits_frame(window_length: int, frame_length) -> bool:
    return frame_length is None or window_length <= frame_length

try:
    window = window_function(window_length, frame_length=frame_length)
except ValueError as e:
    if "may not be larger than frame_length" in str(e):
        window_length, frame_length = min(window_length, frame_length), max(window_length, frame_length)
        window = window_function(window_length, frame_length=frame_length)
    else:
        raise

When it happens

Trigger: Calling `window_function(window_length=512, frame_length=400, ...)` or any call where the two lengths are inverted; typically triggered by feature extractors that request a window shorter than the frame but receive mismatched config values.

Common situations: Feature-extractor configs where n_fft/window size and frame length were edited independently; porting setups from other toolkits where window_length includes padding semantics differently; swapping the two positional arguments.

Related errors

• Unknown window function '{name}'
• frame_length ({frame_length}) may not be larger than fft_len
• Length of the window ({window_length}) must equal frame_leng
• hop_length must be greater than zero
• mel_scale should be one of "htk", "slaney" or "kaldi".

AI-assisted analysis of huggingface/transformers@a597f97485 (2026-08-14). Data as JSON: /api/errors/1d5b666cec79fc83. Report an issue: GitHub.