From 4d2bf9fe3cc385b441e94c3000b34a44cac8a8db Mon Sep 17 00:00:00 2001
From: speech_asr <wangjiaming.wjm@alibaba-inc.com>
Date: 星期二, 14 三月 2023 17:13:25 +0800
Subject: [PATCH] update
---
funasr/models/frontend/wav_frontend.py | 51 +++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 51 insertions(+), 0 deletions(-)
diff --git a/funasr/models/frontend/wav_frontend.py b/funasr/models/frontend/wav_frontend.py
index 445efca..c4b7910 100644
--- a/funasr/models/frontend/wav_frontend.py
+++ b/funasr/models/frontend/wav_frontend.py
@@ -7,6 +7,7 @@
import torch
import torchaudio.compliance.kaldi as kaldi
from funasr.models.frontend.abs_frontend import AbsFrontend
+import funasr.models.frontend.eend_ola_feature as eend_ola_feature
from typeguard import check_argument_types
from torch.nn.utils.rnn import pad_sequence
@@ -444,3 +445,53 @@
self.reserve_waveforms = None
self.input_cache = None
self.lfr_splice_cache = []
+
+
+class WavFrontendMel23(AbsFrontend):
+ """Conventional frontend structure for ASR.
+ """
+
+ def __init__(
+ self,
+ fs: int = 16000,
+ frame_length: int = 25,
+ frame_shift: int = 10,
+ lfr_m: int = 1,
+ lfr_n: int = 1,
+ ):
+ assert check_argument_types()
+ super().__init__()
+ self.fs = fs
+ self.frame_length = frame_length
+ self.frame_shift = frame_shift
+ self.lfr_m = lfr_m
+ self.lfr_n = lfr_n
+
+ def output_size(self) -> int:
+ return self.n_mels * self.lfr_m
+
+ def forward(
+ self,
+ input: torch.Tensor,
+ input_lengths: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+ batch_size = input.size(0)
+ feats = []
+ feats_lens = []
+ for i in range(batch_size):
+ waveform_length = input_lengths[i]
+ waveform = input[i][:waveform_length]
+ waveform = waveform.unsqueeze(0).numpy()
+ mat = eend_ola_feature.stft(waveform, self.frame_length, self.frame_shift)
+ mat = eend_ola_feature.transform(mat)
+ mat = mat.splice(mat, context_size=self.lfr_m)
+ mat = mat[::self.lfr_n]
+ mat = torch.from_numpy(mat)
+ feat_length = mat.size(0)
+ feats.append(mat)
+ feats_lens.append(feat_length)
+
+ feats_lens = torch.as_tensor(feats_lens)
+ feats_pad = pad_sequence(feats,
+ batch_first=True,
+ padding_value=0.0)
+ return feats_pad, feats_lens
\ No newline at end of file
--
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