Initial commit

2026-02-06 20:31:14 +08:00
parent a0b51be095
commit c589bcb837
145 changed files with 28773 additions and 0 deletions
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+from skimage.transform import resize
+import struct
+import webrtcvad
+from scipy.ndimage.morphology import binary_dilation
+import librosa
+import numpy as np
+import pyloudnorm as pyln
+import warnings
+
+warnings.filterwarnings("ignore", message="Possible clipped samples in output")
+
+int16_max = (2 ** 15) - 1
+
+
+def trim_long_silences(path, sr=None, return_raw_wav=False, norm=True, vad_max_silence_length=12):
+    """
+    Ensures that segments without voice in the waveform remain no longer than a
+    threshold determined by the VAD parameters in params.py.
+    :param wav: the raw waveform as a numpy array of floats
+    :param vad_max_silence_length: Maximum number of consecutive silent frames a segment can have.
+    :return: the same waveform with silences trimmed away (length <= original wav length)
+    """
+
+    ## Voice Activation Detection
+    # Window size of the VAD. Must be either 10, 20 or 30 milliseconds.
+    # This sets the granularity of the VAD. Should not need to be changed.
+    sampling_rate = 16000
+    wav_raw, sr = librosa.core.load(path, sr=sr)
+
+    if norm:
+        meter = pyln.Meter(sr)  # create BS.1770 meter
+        loudness = meter.integrated_loudness(wav_raw)
+        wav_raw = pyln.normalize.loudness(wav_raw, loudness, -20.0)
+        if np.abs(wav_raw).max() > 1.0:
+            wav_raw = wav_raw / np.abs(wav_raw).max()
+
+    wav = librosa.resample(wav_raw, sr, sampling_rate, res_type='kaiser_best')
+
+    vad_window_length = 30  # In milliseconds
+    # Number of frames to average together when performing the moving average smoothing.
+    # The larger this value, the larger the VAD variations must be to not get smoothed out.
+    vad_moving_average_width = 8
+
+    # Compute the voice detection window size
+    samples_per_window = (vad_window_length * sampling_rate) // 1000
+
+    # Trim the end of the audio to have a multiple of the window size
+    wav = wav[:len(wav) - (len(wav) % samples_per_window)]
+
+    # Convert the float waveform to 16-bit mono PCM
+    pcm_wave = struct.pack("%dh" % len(wav), *(np.round(wav * int16_max)).astype(np.int16))
+
+    # Perform voice activation detection
+    voice_flags = []
+    vad = webrtcvad.Vad(mode=3)
+    for window_start in range(0, len(wav), samples_per_window):
+        window_end = window_start + samples_per_window
+        voice_flags.append(vad.is_speech(pcm_wave[window_start * 2:window_end * 2],
+                                         sample_rate=sampling_rate))
+    voice_flags = np.array(voice_flags)
+
+    # Smooth the voice detection with a moving average
+    def moving_average(array, width):
+        array_padded = np.concatenate((np.zeros((width - 1) // 2), array, np.zeros(width // 2)))
+        ret = np.cumsum(array_padded, dtype=float)
+        ret[width:] = ret[width:] - ret[:-width]
+        return ret[width - 1:] / width
+
+    audio_mask = moving_average(voice_flags, vad_moving_average_width)
+    audio_mask = np.round(audio_mask).astype(np.bool)
+
+    # Dilate the voiced regions
+    audio_mask = binary_dilation(audio_mask, np.ones(vad_max_silence_length + 1))
+    audio_mask = np.repeat(audio_mask, samples_per_window)
+    audio_mask = resize(audio_mask, (len(wav_raw),)) > 0
+    if return_raw_wav:
+        return wav_raw, audio_mask, sr
+    return wav_raw[audio_mask], audio_mask, sr