From fae856e23d45fd27d5fd55fd036e8e3fc7b24915 Mon Sep 17 00:00:00 2001
From: 雾聪 <wucong.lyb@alibaba-inc.com>
Date: 星期五, 02 六月 2023 23:00:08 +0800
Subject: [PATCH] update funasr-onnx-offline
---
funasr/models/frontend/wav_frontend.py | 37 ++++++++++++++++---------------------
1 files changed, 16 insertions(+), 21 deletions(-)
diff --git a/funasr/models/frontend/wav_frontend.py b/funasr/models/frontend/wav_frontend.py
index ca22175..f16bdd9 100644
--- a/funasr/models/frontend/wav_frontend.py
+++ b/funasr/models/frontend/wav_frontend.py
@@ -11,8 +11,6 @@
import funasr.models.frontend.eend_ola_feature as eend_ola_feature
from funasr.models.frontend.abs_frontend import AbsFrontend
-from modelscope.utils.logger import get_logger
-logger = get_logger()
def load_cmvn(cmvn_file):
with open(cmvn_file, 'r', encoding='utf-8') as f:
@@ -36,11 +34,11 @@
means = np.array(means_list).astype(np.float)
vars = np.array(vars_list).astype(np.float)
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
"""
@@ -49,11 +47,10 @@
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)
@@ -113,6 +110,7 @@
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
@@ -142,8 +140,8 @@
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)
@@ -196,8 +194,8 @@
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)
@@ -397,8 +395,10 @@
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
@@ -425,10 +425,8 @@
reserve_frame_idx = lfr_splice_frame_idxs[0] - minus_frame
# print('reserve_frame_idx: ' + str(reserve_frame_idx))
# print('frame_frame: ' + str(frame_from_waveforms))
- self.reserve_waveforms = self.waveforms[:,
- reserve_frame_idx * self.frame_shift_sample_length:frame_from_waveforms * self.frame_shift_sample_length]
- sample_length = (
- frame_from_waveforms - 1) * self.frame_shift_sample_length + self.frame_sample_length
+ self.reserve_waveforms = self.waveforms[:, reserve_frame_idx * self.frame_shift_sample_length:frame_from_waveforms * self.frame_shift_sample_length]
+ sample_length = (frame_from_waveforms - 1) * self.frame_shift_sample_length + self.frame_sample_length
self.waveforms = self.waveforms[:, :sample_length]
else:
# update self.reserve_waveforms and self.lfr_splice_cache
@@ -487,9 +485,6 @@
batch_size = input.size(0)
feats = []
feats_lens = []
- logger.info("batch_size: {}".format(batch_size))
- logger.info("input: {}".format(input))
- logger.info("input_lengths: {}".format(input_lengths))
for i in range(batch_size):
waveform_length = input_lengths[i]
waveform = input[i][:waveform_length]
--
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