From 1028a8a036cabd6091fc1a040bbddd565fd3e911 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 10 一月 2024 17:42:53 +0800
Subject: [PATCH] funasr1.0 paraformer_streaming WavFrontendOnline
---
funasr/frontends/wav_frontend.py | 116 ++++++++++++++++++++++++++++++----------------------------
1 files changed, 60 insertions(+), 56 deletions(-)
diff --git a/funasr/frontends/wav_frontend.py b/funasr/frontends/wav_frontend.py
index 746bf82..fe22335 100644
--- a/funasr/frontends/wav_frontend.py
+++ b/funasr/frontends/wav_frontend.py
@@ -1,7 +1,7 @@
# Copyright (c) Alibaba, Inc. and its affiliates.
# Part of the implementation is borrowed from espnet/espnet.
from typing import Tuple
-
+import copy
import numpy as np
import torch
import torch.nn as nn
@@ -119,7 +119,9 @@
def forward(
self,
input: torch.Tensor,
- input_lengths) -> Tuple[torch.Tensor, torch.Tensor]:
+ input_lengths,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
batch_size = input.size(0)
feats = []
feats_lens = []
@@ -249,13 +251,13 @@
self.dither = dither
self.snip_edges = snip_edges
self.upsacle_samples = upsacle_samples
- self.waveforms = None
- self.reserve_waveforms = None
- self.fbanks = None
- self.fbanks_lens = None
+ # self.waveforms = None
+ # self.reserve_waveforms = None
+ # self.fbanks = None
+ # self.fbanks_lens = None
self.cmvn = None if self.cmvn_file is None else load_cmvn(self.cmvn_file)
- self.input_cache = None
- self.lfr_splice_cache = []
+ # self.input_cache = None
+ # self.lfr_splice_cache = []
def output_size(self) -> int:
return self.n_mels * self.lfr_m
@@ -278,9 +280,6 @@
return inputs.type(torch.float32)
@staticmethod
- # inputs tensor has catted the cache tensor
- # def apply_lfr(inputs: torch.Tensor, lfr_m: int, lfr_n: int, inputs_lfr_cache: torch.Tensor = None,
- # is_final: bool = False) -> Tuple[torch.Tensor, torch.Tensor, int]:
def apply_lfr(inputs: torch.Tensor, lfr_m: int, lfr_n: int, is_final: bool = False) -> Tuple[
torch.Tensor, torch.Tensor, int]:
"""
@@ -319,15 +318,16 @@
def forward_fbank(
self,
input: torch.Tensor,
- input_lengths: torch.Tensor
+ input_lengths: torch.Tensor,
+ cache: dict = {},
+ **kwargs,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
batch_size = input.size(0)
- if self.input_cache is None:
- self.input_cache = torch.empty(0)
- input = torch.cat((self.input_cache, input), dim=1)
+
+ input = torch.cat((cache["input_cache"], input), dim=1)
frame_num = self.compute_frame_num(input.shape[-1], self.frame_sample_length, self.frame_shift_sample_length)
# update self.in_cache
- self.input_cache = input[:, -(input.shape[-1] - frame_num * self.frame_shift_sample_length):]
+ cache["input_cache"] = input[:, -(input.shape[-1] - frame_num * self.frame_shift_sample_length):]
waveforms = torch.empty(0)
feats_pad = torch.empty(0)
feats_lens = torch.empty(0)
@@ -360,20 +360,19 @@
feats_pad = pad_sequence(feats,
batch_first=True,
padding_value=0.0)
- self.fbanks = feats_pad
- import copy
- self.fbanks_lens = copy.deepcopy(feats_lens)
+ cache["fbanks"] = feats_pad
+ cache["fbanks_lens"]= copy.deepcopy(feats_lens)
return waveforms, feats_pad, feats_lens
- def get_fbank(self) -> Tuple[torch.Tensor, torch.Tensor]:
- return self.fbanks, self.fbanks_lens
def forward_lfr_cmvn(
self,
input: torch.Tensor,
input_lengths: torch.Tensor,
- is_final: bool = False
- ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ is_final: bool = False,
+ cache: dict = {},
+ **kwargs,
+ ):
batch_size = input.size(0)
feats = []
feats_lens = []
@@ -383,7 +382,7 @@
if self.lfr_m != 1 or self.lfr_n != 1:
# update self.lfr_splice_cache in self.apply_lfr
# mat, self.lfr_splice_cache[i], lfr_splice_frame_idx = self.apply_lfr(mat, self.lfr_m, self.lfr_n, self.lfr_splice_cache[i],
- mat, self.lfr_splice_cache[i], lfr_splice_frame_idx = self.apply_lfr(mat, self.lfr_m, self.lfr_n,
+ mat, cache["lfr_splice_cache"][i], lfr_splice_frame_idx = self.apply_lfr(mat, self.lfr_m, self.lfr_n,
is_final)
if self.cmvn_file is not None:
mat = self.apply_cmvn(mat, self.cmvn)
@@ -400,63 +399,68 @@
return feats_pad, feats_lens, lfr_splice_frame_idxs
def forward(
- 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()
+ self, input: torch.Tensor, input_lengths: torch.Tensor, cache: dict = {}, **kwargs
+ ):
+ is_final = kwargs.get("is_final", False)
+ reset = kwargs.get("reset", False)
+ if len(cache) == 0 or reset:
+ self.init_cache(cache)
+
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
+
+ waveforms, feats, feats_lengths = self.forward_fbank(input, input_lengths, cache=cache) # input shape: B T D
+
if feats.shape[0]:
- # if self.reserve_waveforms is None and self.lfr_m > 1:
- # self.reserve_waveforms = waveforms[:, :(self.lfr_m - 1) // 2 * self.frame_shift_sample_length]
- self.waveforms = waveforms if self.reserve_waveforms is None else torch.cat(
- (self.reserve_waveforms, waveforms), dim=1)
- if not self.lfr_splice_cache: # 鍒濆鍖杝plice_cache
+
+ cache["waveforms"] = torch.cat((cache["reserve_waveforms"], waveforms), dim=1)
+
+ if not cache["lfr_splice_cache"]: # 鍒濆鍖杝plice_cache
for i in range(batch_size):
- self.lfr_splice_cache.append(feats[i][0, :].unsqueeze(dim=0).repeat((self.lfr_m - 1) // 2, 1))
+ cache["lfr_splice_cache"].append(feats[i][0, :].unsqueeze(dim=0).repeat((self.lfr_m - 1) // 2, 1))
# need the number of the input frames + self.lfr_splice_cache[0].shape[0] is greater than self.lfr_m
- if feats_lengths[0] + self.lfr_splice_cache[0].shape[0] >= self.lfr_m:
- lfr_splice_cache_tensor = torch.stack(self.lfr_splice_cache) # B T D
+ if feats_lengths[0] + cache["lfr_splice_cache"][0].shape[0] >= self.lfr_m:
+ lfr_splice_cache_tensor = torch.stack(cache["lfr_splice_cache"]) # B T D
feats = torch.cat((lfr_splice_cache_tensor, feats), dim=1)
feats_lengths += lfr_splice_cache_tensor[0].shape[0]
frame_from_waveforms = int(
- (self.waveforms.shape[1] - self.frame_sample_length) / self.frame_shift_sample_length + 1)
- minus_frame = (self.lfr_m - 1) // 2 if self.reserve_waveforms is None else 0
- feats, feats_lengths, lfr_splice_frame_idxs = self.forward_lfr_cmvn(feats, feats_lengths, is_final)
+ (cache["waveforms"].shape[1] - self.frame_sample_length) / self.frame_shift_sample_length + 1)
+ minus_frame = (self.lfr_m - 1) // 2 if cache["reserve_waveforms"].numel() == 0 else 0
+ feats, feats_lengths, lfr_splice_frame_idxs = self.forward_lfr_cmvn(feats, feats_lengths, is_final, cache=cache)
if self.lfr_m == 1:
- self.reserve_waveforms = None
+ cache["reserve_waveforms"] = torch.empty(0)
else:
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]
+ cache["reserve_waveforms"] = cache["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]
+ cache["waveforms"] = cache["waveforms"][:, :sample_length]
else:
# update self.reserve_waveforms and self.lfr_splice_cache
- self.reserve_waveforms = self.waveforms[:,
- :-(self.frame_sample_length - self.frame_shift_sample_length)]
+ cache["reserve_waveforms"] = cache["waveforms"][:, :-(self.frame_sample_length - self.frame_shift_sample_length)]
for i in range(batch_size):
- self.lfr_splice_cache[i] = torch.cat((self.lfr_splice_cache[i], feats[i]), dim=0)
+ cache["lfr_splice_cache"][i] = torch.cat((cache["lfr_splice_cache"][i], feats[i]), dim=0)
return torch.empty(0), feats_lengths
else:
if is_final:
- self.waveforms = waveforms if self.reserve_waveforms is None else self.reserve_waveforms
- feats = torch.stack(self.lfr_splice_cache)
+ cache["waveforms"] = waveforms if cache["reserve_waveforms"].numel() == 0 else cache["reserve_waveforms"]
+ feats = torch.stack(cache["lfr_splice_cache"])
feats_lengths = torch.zeros(batch_size, dtype=torch.int) + feats.shape[1]
- feats, feats_lengths, _ = self.forward_lfr_cmvn(feats, feats_lengths, is_final)
+ feats, feats_lengths, _ = self.forward_lfr_cmvn(feats, feats_lengths, is_final, cache=cache)
if is_final:
- self.cache_reset()
+ self.init_cache(cache)
return feats, feats_lengths
- def get_waveforms(self):
- return self.waveforms
- def cache_reset(self):
- self.reserve_waveforms = None
- self.input_cache = None
- self.lfr_splice_cache = []
+ def init_cache(self, cache: dict = {}):
+ cache["reserve_waveforms"] = torch.empty(0)
+ cache["input_cache"] = torch.empty(0)
+ cache["lfr_splice_cache"] = []
+ cache["waveforms"] = None
+ cache["fbanks"] = None
+ cache["fbanks_lens"] = None
+ return cache
class WavFrontendMel23(nn.Module):
--
Gitblit v1.9.1