From 3cd3473bf7a3b41484baa86d9092248d78e7af39 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 21 四月 2023 17:17:37 +0800
Subject: [PATCH] docs
---
funasr/models/predictor/cif.py | 16 ++++++++++++----
1 files changed, 12 insertions(+), 4 deletions(-)
diff --git a/funasr/models/predictor/cif.py b/funasr/models/predictor/cif.py
index 74f3e68..a5273f8 100644
--- a/funasr/models/predictor/cif.py
+++ b/funasr/models/predictor/cif.py
@@ -200,6 +200,7 @@
return acoustic_embeds, token_num, alphas, cif_peak
def forward_chunk(self, hidden, cache=None):
+ b, t, d = hidden.size()
h = hidden
context = h.transpose(1, 2)
queries = self.pad(context)
@@ -220,6 +221,8 @@
alphas = alphas * mask_chunk_predictor
if cache is not None:
+ if cache["is_final"]:
+ alphas[:, cache["stride"] + cache["pad_left"] - 1] += 0.45
if cache["cif_hidden"] is not None:
hidden = torch.cat((cache["cif_hidden"], hidden), 1)
if cache["cif_alphas"] is not None:
@@ -231,6 +234,7 @@
last_fire_place = len_time - 1
last_fire_remainds = 0.0
pre_alphas_length = 0
+ last_fire = False
mask_chunk_peak_predictor = None
if cache is not None:
@@ -241,7 +245,6 @@
mask_chunk_peak_predictor[:, :pre_alphas_length] = 1.0
mask_chunk_peak_predictor[:, pre_alphas_length + cache["pad_left"]:pre_alphas_length + cache["stride"] + cache["pad_left"]] = 1.0
-
if mask_chunk_peak_predictor is not None:
cif_peak = cif_peak * mask_chunk_peak_predictor.squeeze(-1)
@@ -249,10 +252,15 @@
if cif_peak[0][len_time - 1 - i] > self.threshold or cif_peak[0][len_time - 1 - i] == self.threshold:
last_fire_place = len_time - 1 - i
last_fire_remainds = cif_peak[0][len_time - 1 - i] - self.threshold
+ last_fire = True
break
- last_fire_remainds = torch.tensor([last_fire_remainds], dtype=alphas.dtype).to(alphas.device)
- cache["cif_hidden"] = hidden[:, last_fire_place:, :]
- cache["cif_alphas"] = torch.cat((last_fire_remainds.unsqueeze(0), alphas[:, last_fire_place+1:]), -1)
+ if last_fire:
+ last_fire_remainds = torch.tensor([last_fire_remainds], dtype=alphas.dtype).to(alphas.device)
+ cache["cif_hidden"] = hidden[:, last_fire_place:, :]
+ cache["cif_alphas"] = torch.cat((last_fire_remainds.unsqueeze(0), alphas[:, last_fire_place+1:]), -1)
+ else:
+ cache["cif_hidden"] = hidden
+ cache["cif_alphas"] = alphas
token_num_int = token_num.floor().type(torch.int32).item()
return acoustic_embeds[:, 0:token_num_int, :], token_num, alphas, cif_peak
--
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