wehub-resource-sync
89 lines
3.1 KiB
Python
89 lines
3.1 KiB
Python
import torch
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from torch.nn.utils.rnn import pad_sequence
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def slice_padding_fbank(speech, speech_lengths, vad_segments):
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"""Slice padding fbank.
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Args:
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speech: Speech audio tensor, shape (batch, time).
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speech_lengths: Length of each speech sample.
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vad_segments: TODO.
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"""
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speech_list = []
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speech_lengths_list = []
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for i, segment in enumerate(vad_segments):
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bed_idx = int(segment[0][0] * 16)
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end_idx = min(int(segment[0][1] * 16), speech_lengths[0])
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speech_i = speech[0, bed_idx:end_idx]
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speech_lengths_i = end_idx - bed_idx
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speech_list.append(speech_i)
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speech_lengths_list.append(speech_lengths_i)
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feats_pad = pad_sequence(speech_list, batch_first=True, padding_value=0.0)
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speech_lengths_pad = torch.Tensor(speech_lengths_list).int()
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return feats_pad, speech_lengths_pad
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def slice_padding_audio_samples(speech, speech_lengths, vad_segments):
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"""Slice audio into VAD segments with proper padding.
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Args:
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speech (Tensor): Full audio tensor.
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speech_lengths (int): Total audio length.
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vad_segments (list): List of (segment_info, original_index) tuples,
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where segment_info is [start_ms, end_ms].
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Returns:
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tuple: (speech_list, speech_lengths_list) - lists of numpy arrays and their lengths.
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"""
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speech_list = []
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speech_lengths_list = []
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for i, segment in enumerate(vad_segments):
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bed_idx = int(segment[0][0] * 16)
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end_idx = min(int(segment[0][1] * 16), speech_lengths)
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speech_i = speech[bed_idx:end_idx]
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speech_lengths_i = end_idx - bed_idx
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speech_list.append(speech_i)
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speech_lengths_list.append(speech_lengths_i)
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return speech_list, speech_lengths_list
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def merge_vad(vad_result, max_length=15000, min_length=0):
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"""Merge short VAD segments to reduce fragmentation.
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Args:
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vad_result (list): VAD segments [[start_ms, end_ms], ...].
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max_length (int): Maximum merged segment length in ms (default 15000).
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min_length (int): Minimum segment length; shorter ones get merged (default 0).
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Returns:
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list: Merged VAD segments [[start_ms, end_ms], ...].
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"""
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new_result = []
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if len(vad_result) <= 1:
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return vad_result
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time_step = [t[0] for t in vad_result] + [t[1] for t in vad_result]
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time_step = sorted(list(set(time_step)))
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if len(time_step) == 0:
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return []
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bg = 0
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for i in range(len(time_step) - 1):
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time = time_step[i]
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if time_step[i + 1] - bg < max_length:
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continue
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if time - bg > min_length:
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new_result.append([bg, time])
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# if time - bg < max_length * 1.5:
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# new_result.append([bg, time])
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# else:
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# split_num = int(time - bg) // max_length + 1
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# spl_l = int(time - bg) // split_num
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# for j in range(split_num):
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# new_result.append([bg + j * spl_l, bg + (j + 1) * spl_l])
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bg = time
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new_result.append([bg, time_step[-1]])
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return new_result |