speech_recognition/FunASR
1
0
Fork 0
mirror of https://github.com/modelscope/FunASR synced 2025-09-15 14:48:36 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

onnx

Browse Source
This commit is contained in:
游雁 2023-02-25 17:45:41 +08:00
parent 4daea37110
commit 14a1b5eb20
1 changed files with 128 additions and 13 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

141
funasr/export/models/predictor/cif.py
View File

@ -16,6 +16,11 @@ def sequence_mask(lengths, maxlen=None, dtype=torch.float32, device=None):
return mask.type(dtype).to(device) if device is not None else mask.type(dtype)
def sequence_mask_scripts(lengths, maxlen:int):
row_vector = torch.arange(0, maxlen, 1).type(lengths.dtype).to(lengths.device)
matrix = torch.unsqueeze(lengths, dim=-1)
mask = row_vector < matrix
return mask.type(torch.float32).to(lengths.device)
class CifPredictorV2(nn.Module):
def __init__(self, model):
@ -71,28 +76,131 @@ class CifPredictorV2(nn.Module):
return hidden, alphas, token_num_floor
# @torch.jit.script
# def cif(hidden, alphas, threshold: float):
# batch_size, len_time, hidden_size = hidden.size()
# threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
#
# # loop varss
# integrate = torch.zeros([batch_size], device=hidden.device)
# frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
# # intermediate vars along time
# list_fires = []
# list_frames = []
#
# for t in range(len_time):
# alpha = alphas[:, t]
# distribution_completion = torch.ones([batch_size], device=hidden.device) - integrate
#
# integrate += alpha
# list_fires.append(integrate)
#
# fire_place = integrate >= threshold
# integrate = torch.where(fire_place,
# integrate - torch.ones([batch_size], device=hidden.device),
# integrate)
# cur = torch.where(fire_place,
# distribution_completion,
# alpha)
# remainds = alpha - cur
#
# frame += cur[:, None] * hidden[:, t, :]
# list_frames.append(frame)
# frame = torch.where(fire_place[:, None].repeat(1, hidden_size),
# remainds[:, None] * hidden[:, t, :],
# frame)
#
# fires = torch.stack(list_fires, 1)
# frames = torch.stack(list_frames, 1)
# list_ls = []
# len_labels = torch.round(alphas.sum(-1)).int()
# max_label_len = len_labels.max().item()
# # print("type: {}".format(type(max_label_len)))
# for b in range(batch_size):
# fire = fires[b, :]
# l = torch.index_select(frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze())
# pad_l = torch.zeros([int(max_label_len - l.size(0)), int(hidden_size)], dtype=l.dtype, device=hidden.device)
# list_ls.append(torch.cat([l, pad_l], 0))
# return torch.stack(list_ls, 0), fires
# @torch.jit.script
# def cif(hidden, alphas, threshold: float):
# batch_size, len_time, hidden_size = hidden.size()
# threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
#
# # loop varss
# integrate = torch.zeros([batch_size], dtype=alphas.dtype, device=hidden.device)
# frame = torch.zeros([batch_size, hidden_size], dtype=hidden.dtype, device=hidden.device)
# # intermediate vars along time
# list_fires = []
# list_frames = []
#
# for t in range(len_time):
# alpha = alphas[:, t]
# distribution_completion = torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device) - integrate
#
# integrate += alpha
# list_fires.append(integrate)
#
# fire_place = integrate >= threshold
# integrate = torch.where(fire_place,
# integrate - torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device),
# integrate)
# cur = torch.where(fire_place,
# distribution_completion,
# alpha)
# remainds = alpha - cur
#
# frame += cur[:, None] * hidden[:, t, :]
# list_frames.append(frame)
# frame = torch.where(fire_place[:, None].repeat(1, hidden_size),
# remainds[:, None] * hidden[:, t, :],
# frame)
#
# fires = torch.stack(list_fires, 1)
# frames = torch.stack(list_frames, 1)
# len_labels = torch.floor(torch.sum(alphas, dim=1)).int()
# max_label_len = torch.max(len_labels)
# pad_num = max_label_len - len_labels
# pad_num_max = torch.max(pad_num).item()
# frames_pad_tensor = torch.zeros([int(batch_size), int(pad_num_max), int(hidden_size)], dtype=frames.dtype,
# device=frames.device)
# fires_pad_tensor = torch.ones([int(batch_size), int(pad_num_max)], dtype=fires.dtype, device=fires.device)
# fires_pad_tensor_mask = sequence_mask_scripts(pad_num, maxlen=int(pad_num_max))
# fires_pad_tensor *= fires_pad_tensor_mask
# frames_pad = torch.cat([frames, frames_pad_tensor], dim=1)
# fires_pad = torch.cat([fires, fires_pad_tensor], dim=1)
# index_bool = fires_pad >= threshold
# frames_fire = frames_pad[index_bool]
# frames_fire = torch.reshape(frames_fire, (int(batch_size), -1, int(hidden_size)))
# frames_fire_mask = sequence_mask_scripts(len_labels, maxlen=int(max_label_len))
# frames_fire *= frames_fire_mask[:, :, None]
#
# return frames_fire, fires
@torch.jit.script
def cif(hidden, alphas, threshold: float):
batch_size, len_time, hidden_size = hidden.size()
threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)
# loop varss
integrate = torch.zeros([batch_size], device=hidden.device)
frame = torch.zeros([batch_size, hidden_size], device=hidden.device)
integrate = torch.zeros([batch_size], dtype=alphas.dtype, device=hidden.device)
frame = torch.zeros([batch_size, hidden_size], dtype=hidden.dtype, device=hidden.device)
# intermediate vars along time
list_fires = []
list_frames = []
for t in range(len_time):
alpha = alphas[:, t]
distribution_completion = torch.ones([batch_size], device=hidden.device) - integrate
distribution_completion = torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device) - integrate
integrate += alpha
list_fires.append(integrate)
fire_place = integrate >= threshold
integrate = torch.where(fire_place,
integrate - torch.ones([batch_size], device=hidden.device),
integrate - torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device),
integrate)
cur = torch.where(fire_place,
distribution_completion,
@ -107,13 +215,20 @@ def cif(hidden, alphas, threshold: float):
fires = torch.stack(list_fires, 1)
frames = torch.stack(list_frames, 1)
list_ls = []
len_labels = torch.round(alphas.sum(-1)).int()
max_label_len = len_labels.max().item()
print("type: {}".format(type(max_label_len)))
# list_ls = []
len_labels = torch.round(alphas.sum(-1)).type(torch.int32)
# max_label_len = int(torch.max(len_labels).item())
# print("type: {}".format(type(max_label_len)))
fire_idxs = fires >= threshold
frame_fires = torch.zeros_like(hidden)
max_label_len = frames[0, fire_idxs[0]].size(0)
for b in range(batch_size):
fire = fires[b, :]
l = torch.index_select(frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze())
pad_l = torch.zeros([int(max_label_len - l.size(0)), int(hidden_size)], device=hidden.device)
list_ls.append(torch.cat([l, pad_l], 0))
return torch.stack(list_ls, 0), fires
# fire = fires[b, :]
frame_fire = frames[b, fire_idxs[b]]
frame_len = frame_fire.size(0)
frame_fires[b, :frame_len, :] = frame_fire
if frame_len >= max_label_len:
max_label_len = frame_len
frame_fires = frame_fires[:, :max_label_len, :]
return frame_fires, fires