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| | | from torch import nn
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| | | import logging
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| | | import numpy as np
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| | | from funasr.torch_utils.device_funcs import to_device
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| | | from funasr.modules.nets_utils import make_pad_mask
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| | | from funasr.modules.streaming_utils.utils import sequence_mask
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| | | acoustic_embeds = acoustic_embeds[:, :token_num_int, :]
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| | | return acoustic_embeds, token_num, alphas, cif_peak
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| | | def forward_chunk(self, hidden, cache=None):
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| | | batch_size, len_time, hidden_size = hidden.shape
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| | | h = hidden
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| | | context = h.transpose(1, 2)
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| | | queries = self.pad(context)
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| | | output = torch.relu(self.cif_conv1d(queries))
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| | | output = output.transpose(1, 2)
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| | | output = self.cif_output(output)
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| | | alphas = torch.sigmoid(output)
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| | | alphas = torch.nn.functional.relu(alphas * self.smooth_factor - self.noise_threshold)
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| | | alphas = alphas.squeeze(-1)
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| | | token_length = []
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| | | list_fires = []
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| | | list_frames = []
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| | | cache_alphas = []
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| | | cache_hiddens = []
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| | | if cache is not None and "chunk_size" in cache:
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| | | alphas[:, :cache["chunk_size"][0]] = 0.0
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| | | alphas[:, sum(cache["chunk_size"][:2]):] = 0.0
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| | | if cache is not None and "cif_alphas" in cache and "cif_hidden" in cache:
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| | | cache["cif_hidden"] = to_device(cache["cif_hidden"], device=hidden.device)
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| | | cache["cif_alphas"] = to_device(cache["cif_alphas"], device=alphas.device)
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| | | hidden = torch.cat((cache["cif_hidden"], hidden), dim=1)
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| | | alphas = torch.cat((cache["cif_alphas"], alphas), dim=1)
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| | | if cache is not None and "last_chunk" in cache and cache["last_chunk"]:
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| | | tail_hidden = torch.zeros((batch_size, 1, hidden_size), device=hidden.device)
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| | | tail_alphas = torch.tensor([[self.tail_threshold]], device=alphas.device)
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| | | tail_alphas = torch.tile(tail_alphas, (batch_size, 1))
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| | | hidden = torch.cat((hidden, tail_hidden), dim=1)
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| | | alphas = torch.cat((alphas, tail_alphas), dim=1)
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| | | len_time = alphas.shape[1]
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| | | for b in range(batch_size):
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| | | integrate = 0.0
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| | | frames = torch.zeros((hidden_size), device=hidden.device)
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| | | list_frame = []
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| | | list_fire = []
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| | | for t in range(len_time):
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| | | alpha = alphas[b][t]
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| | | if alpha + integrate < self.threshold:
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| | | integrate += alpha
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| | | list_fire.append(integrate)
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| | | frames += alpha * hidden[b][t]
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| | | else:
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| | | frames += (self.threshold - integrate) * hidden[b][t]
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| | | list_frame.append(frames)
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| | | integrate += alpha
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| | | list_fire.append(integrate)
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| | | integrate -= self.threshold
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| | | frames = integrate * hidden[b][t]
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| | | cache_alphas.append(integrate)
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| | | if integrate > 0.0:
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| | | cache_hiddens.append(frames / integrate)
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| | | else:
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| | | cache_hiddens.append(frames)
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| | |
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| | | token_length.append(torch.tensor(len(list_frame), device=alphas.device))
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| | | list_fires.append(list_fire)
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| | | list_frames.append(list_frame)
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| | |
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| | | cache["cif_alphas"] = torch.stack(cache_alphas, axis=0)
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| | | cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0)
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| | | cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0)
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| | | cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0)
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| | |
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| | | max_token_len = max(token_length)
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| | | if max_token_len == 0:
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| | | return hidden, torch.stack(token_length, 0)
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| | | list_ls = []
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| | | for b in range(batch_size):
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| | | pad_frames = torch.zeros((max_token_len - token_length[b], hidden_size), device=alphas.device)
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| | | if token_length[b] == 0:
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| | | list_ls.append(pad_frames)
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| | | else:
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| | | list_frames[b] = torch.stack(list_frames[b])
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| | | list_ls.append(torch.cat((list_frames[b], pad_frames), dim=0))
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| | | cache["cif_alphas"] = torch.stack(cache_alphas, axis=0)
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| | | cache["cif_alphas"] = torch.unsqueeze(cache["cif_alphas"], axis=0)
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| | | cache["cif_hidden"] = torch.stack(cache_hiddens, axis=0)
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| | | cache["cif_hidden"] = torch.unsqueeze(cache["cif_hidden"], axis=0)
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| | | return torch.stack(list_ls, 0), torch.stack(token_length, 0)
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| | | def tail_process_fn(self, hidden, alphas, token_num=None, mask=None):
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| | | b, t, d = hidden.size()
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| | | token_num_int = torch.max(token_num).type(torch.int32).item()
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| | | acoustic_embeds = acoustic_embeds[:, :token_num_int, :]
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| | | return acoustic_embeds, token_num, alphas, cif_peak, token_num2
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| | | |
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| | | def get_upsample_timestamp(self, hidden, target_label=None, mask=None, ignore_id=-1, mask_chunk_predictor=None,
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| | | target_label_length=None, token_num=None):
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| | | def get_upsample_timestamp(self, hidden, mask=None, token_num=None):
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| | | h = hidden
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| | | b = hidden.shape[0]
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| | | context = h.transpose(1, 2)
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| | | mask_2 = torch.cat([ones_t, mask], dim=1)
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| | | mask = mask_2 - mask_1
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| | | tail_threshold = mask * tail_threshold
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| | | alphas = torch.cat([alphas, tail_threshold], dim=1)
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| | | alphas = torch.cat([alphas, zeros_t], dim=1)
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| | | alphas = torch.add(alphas, tail_threshold)
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| | | else:
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| | | tail_threshold = torch.tensor([tail_threshold], dtype=alphas.dtype).to(alphas.device)
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| | | tail_threshold = torch.reshape(tail_threshold, (1, 1))
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