From 33d3d2084403fd34b79c835d2f2fe04f6cd8f738 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 13 九月 2023 09:33:54 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add
---
funasr/models/frontend/wav_frontend.py | 34 ++++++++++++++++------------------
1 files changed, 16 insertions(+), 18 deletions(-)
diff --git a/funasr/models/frontend/wav_frontend.py b/funasr/models/frontend/wav_frontend.py
index 475a939..ca5aed6 100644
--- a/funasr/models/frontend/wav_frontend.py
+++ b/funasr/models/frontend/wav_frontend.py
@@ -6,7 +6,6 @@
import torch
import torchaudio.compliance.kaldi as kaldi
from torch.nn.utils.rnn import pad_sequence
-from typeguard import check_argument_types
import funasr.models.frontend.eend_ola_feature as eend_ola_feature
from funasr.models.frontend.abs_frontend import AbsFrontend
@@ -31,14 +30,14 @@
rescale_line = line_item[3:(len(line_item) - 1)]
vars_list = list(rescale_line)
continue
- means = np.array(means_list).astype(np.float)
- vars = np.array(vars_list).astype(np.float)
+ means = np.array(means_list).astype(np.float32)
+ vars = np.array(vars_list).astype(np.float32)
cmvn = np.array([means, vars])
- cmvn = torch.as_tensor(cmvn)
+ cmvn = torch.as_tensor(cmvn, dtype=torch.float32)
return cmvn
-def apply_cmvn(inputs, cmvn_file): # noqa
+def apply_cmvn(inputs, cmvn): # noqa
"""
Apply CMVN with mvn data
"""
@@ -47,11 +46,10 @@
dtype = inputs.dtype
frame, dim = inputs.shape
- cmvn = load_cmvn(cmvn_file)
- means = np.tile(cmvn[0:1, :dim], (frame, 1))
- vars = np.tile(cmvn[1:2, :dim], (frame, 1))
- inputs += torch.from_numpy(means).type(dtype).to(device)
- inputs *= torch.from_numpy(vars).type(dtype).to(device)
+ means = cmvn[0:1, :dim]
+ vars = cmvn[1:2, :dim]
+ inputs += means.to(device)
+ inputs *= vars.to(device)
return inputs.type(torch.float32)
@@ -96,7 +94,6 @@
snip_edges: bool = True,
upsacle_samples: bool = True,
):
- assert check_argument_types()
super().__init__()
self.fs = fs
self.window = window
@@ -111,6 +108,7 @@
self.dither = dither
self.snip_edges = snip_edges
self.upsacle_samples = upsacle_samples
+ self.cmvn = None if self.cmvn_file is None else load_cmvn(self.cmvn_file)
def output_size(self) -> int:
return self.n_mels * self.lfr_m
@@ -140,8 +138,8 @@
if self.lfr_m != 1 or self.lfr_n != 1:
mat = apply_lfr(mat, self.lfr_m, self.lfr_n)
- if self.cmvn_file is not None:
- mat = apply_cmvn(mat, self.cmvn_file)
+ if self.cmvn is not None:
+ mat = apply_cmvn(mat, self.cmvn)
feat_length = mat.size(0)
feats.append(mat)
feats_lens.append(feat_length)
@@ -194,8 +192,8 @@
mat = input[i, :input_lengths[i], :]
if self.lfr_m != 1 or self.lfr_n != 1:
mat = apply_lfr(mat, self.lfr_m, self.lfr_n)
- if self.cmvn_file is not None:
- mat = apply_cmvn(mat, self.cmvn_file)
+ if self.cmvn is not None:
+ mat = apply_cmvn(mat, self.cmvn)
feat_length = mat.size(0)
feats.append(mat)
feats_lens.append(feat_length)
@@ -227,7 +225,6 @@
snip_edges: bool = True,
upsacle_samples: bool = True,
):
- assert check_argument_types()
super().__init__()
self.fs = fs
self.window = window
@@ -395,8 +392,10 @@
return feats_pad, feats_lens, lfr_splice_frame_idxs
def forward(
- self, input: torch.Tensor, input_lengths: torch.Tensor, is_final: bool = False
+ self, input: torch.Tensor, input_lengths: torch.Tensor, is_final: bool = False, reset: bool = False
) -> Tuple[torch.Tensor, torch.Tensor]:
+ if reset:
+ self.cache_reset()
batch_size = input.shape[0]
assert batch_size == 1, 'we support to extract feature online only when the batch size is equal to 1 now'
waveforms, feats, feats_lengths = self.forward_fbank(input, input_lengths) # input shape: B T D
@@ -464,7 +463,6 @@
lfr_m: int = 1,
lfr_n: int = 1,
):
- assert check_argument_types()
super().__init__()
self.fs = fs
self.frame_length = frame_length
--
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