From 5e7a8d1ccae80e54f2e2ecfffdf8e4294800b5c3 Mon Sep 17 00:00:00 2001
From: Legend <me@liux.pro>
Date: 星期日, 15 十二月 2024 01:47:12 +0800
Subject: [PATCH] Update readme_zh.md (#2312)
---
funasr/models/paraformer/cif_predictor.py | 37 +++++++++++++++++++++++++++----------
1 files changed, 27 insertions(+), 10 deletions(-)
diff --git a/funasr/models/paraformer/cif_predictor.py b/funasr/models/paraformer/cif_predictor.py
index 0856eed..d597050 100644
--- a/funasr/models/paraformer/cif_predictor.py
+++ b/funasr/models/paraformer/cif_predictor.py
@@ -80,7 +80,7 @@
hidden, alphas, token_num, mask=mask
)
- acoustic_embeds, cif_peak = cif_v1(hidden, alphas, self.threshold)
+ acoustic_embeds, cif_peak = cif(hidden, alphas, self.threshold)
if target_length is None and self.tail_threshold > 0.0:
token_num_int = torch.max(token_num).type(torch.int32).item()
@@ -506,7 +506,10 @@
frames = torch.zeros(batch_size, len_time, hidden_size, dtype=dtype, device=device)
fires = torch.zeros(batch_size, len_time, dtype=dtype, device=device)
- prefix_sum = torch.cumsum(alphas, dim=1)
+ # prefix_sum = torch.cumsum(alphas, dim=1)
+ prefix_sum = torch.cumsum(alphas, dim=1, dtype=torch.float64).to(
+ torch.float32
+ ) # cumsum precision degradation cause wrong result in extreme
prefix_sum_floor = torch.floor(prefix_sum)
dislocation_prefix_sum = torch.roll(prefix_sum, 1, dims=1)
dislocation_prefix_sum_floor = torch.floor(dislocation_prefix_sum)
@@ -518,8 +521,8 @@
fires[fire_idxs] = 1
fires = fires + prefix_sum - prefix_sum_floor
- prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1)
-
+ # prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1)
+ prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).repeat((1, 1, hidden_size)) * hidden, dim=1)
frames = prefix_sum_hidden[fire_idxs]
shift_frames = torch.roll(frames, 1, dims=0)
@@ -530,15 +533,19 @@
shift_frames[shift_batch_idxs] = 0
remains = fires - torch.floor(fires)
- remain_frames = remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]
+ # remain_frames = remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]
+ remain_frames = remains[fire_idxs].unsqueeze(-1).repeat((1, hidden_size)) * hidden[fire_idxs]
shift_remain_frames = torch.roll(remain_frames, 1, dims=0)
shift_remain_frames[shift_batch_idxs] = 0
frames = frames - shift_frames + shift_remain_frames - remain_frames
- max_label_len = batch_len.max()
+ # max_label_len = batch_len.max()
+ max_label_len = alphas.sum(dim=-1)
+ max_label_len = torch.floor(max_label_len).max().to(dtype=torch.int64)
+ # frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)
frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)
indices = torch.arange(max_label_len, device=device).expand(batch_size, -1)
frame_fires_idxs = indices < batch_len.unsqueeze(1)
@@ -667,7 +674,10 @@
fires = torch.zeros(batch_size, len_time, dtype=dtype, device=device)
- prefix_sum = torch.cumsum(alphas, dim=1)
+ # prefix_sum = torch.cumsum(alphas, dim=1)
+ prefix_sum = torch.cumsum(alphas, dim=1, dtype=torch.float64).to(
+ torch.float32
+ ) # cumsum precision degradation cause wrong result in extreme
prefix_sum_floor = torch.floor(prefix_sum)
dislocation_prefix_sum = torch.roll(prefix_sum, 1, dims=1)
dislocation_prefix_sum_floor = torch.floor(dislocation_prefix_sum)
@@ -689,8 +699,10 @@
device = hidden.device
dtype = hidden.dtype
batch_size, len_time, hidden_size = hidden.size()
+ # frames = torch.zeros(batch_size, len_time, hidden_size, dtype=dtype, device=device)
+ # prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1)
frames = torch.zeros(batch_size, len_time, hidden_size, dtype=dtype, device=device)
- prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).tile((1, 1, hidden_size)) * hidden, dim=1)
+ prefix_sum_hidden = torch.cumsum(alphas.unsqueeze(-1).repeat((1, 1, hidden_size)) * hidden, dim=1)
frames = prefix_sum_hidden[fire_idxs]
shift_frames = torch.roll(frames, 1, dims=0)
@@ -702,15 +714,20 @@
shift_frames[shift_batch_idxs] = 0
remains = fires - torch.floor(fires)
- remain_frames = remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]
+ # remain_frames = remains[fire_idxs].unsqueeze(-1).tile((1, hidden_size)) * hidden[fire_idxs]
+ remain_frames = remains[fire_idxs].unsqueeze(-1).repeat((1, hidden_size)) * hidden[fire_idxs]
shift_remain_frames = torch.roll(remain_frames, 1, dims=0)
shift_remain_frames[shift_batch_idxs] = 0
frames = frames - shift_frames + shift_remain_frames - remain_frames
- max_label_len = batch_len.max()
+ # max_label_len = batch_len.max()
+ max_label_len = (
+ torch.round(alphas.sum(-1)).int().max()
+ ) # torch.round to calculate the max length
+ # frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)
frame_fires = torch.zeros(batch_size, max_label_len, hidden_size, dtype=dtype, device=device)
indices = torch.arange(max_label_len, device=device).expand(batch_size, -1)
frame_fires_idxs = indices < batch_len.unsqueeze(1)
--
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