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 | 540 +++++++++++++++++++++++++++++++++++++++++++++++++----------
1 files changed, 443 insertions(+), 97 deletions(-)
diff --git a/funasr/models/frontend/wav_frontend.py b/funasr/models/frontend/wav_frontend.py
index c0b28ff..ca5aed6 100644
--- a/funasr/models/frontend/wav_frontend.py
+++ b/funasr/models/frontend/wav_frontend.py
@@ -1,22 +1,43 @@
# Copyright (c) Alibaba, Inc. and its affiliates.
# Part of the implementation is borrowed from espnet/espnet.
+from typing import Tuple
-import copy
-from typing import Optional, Tuple, Union
-
-import humanfriendly
import numpy as np
import torch
import torchaudio.compliance.kaldi as kaldi
+from torch.nn.utils.rnn import pad_sequence
+
+import funasr.models.frontend.eend_ola_feature as eend_ola_feature
from funasr.models.frontend.abs_frontend import AbsFrontend
-from funasr.layers.log_mel import LogMel
-from funasr.layers.stft import Stft
-from funasr.utils.get_default_kwargs import get_default_kwargs
-from funasr.modules.frontends.frontend import Frontend
-from typeguard import check_argument_types
-def apply_cmvn(inputs, mvn): # noqa
+def load_cmvn(cmvn_file):
+ with open(cmvn_file, 'r', encoding='utf-8') as f:
+ lines = f.readlines()
+ means_list = []
+ vars_list = []
+ for i in range(len(lines)):
+ line_item = lines[i].split()
+ if line_item[0] == '<AddShift>':
+ line_item = lines[i + 1].split()
+ if line_item[0] == '<LearnRateCoef>':
+ add_shift_line = line_item[3:(len(line_item) - 1)]
+ means_list = list(add_shift_line)
+ continue
+ elif line_item[0] == '<Rescale>':
+ line_item = lines[i + 1].split()
+ if line_item[0] == '<LearnRateCoef>':
+ rescale_line = line_item[3:(len(line_item) - 1)]
+ vars_list = list(rescale_line)
+ continue
+ 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, dtype=torch.float32)
+ return cmvn
+
+
+def apply_cmvn(inputs, cmvn): # noqa
"""
Apply CMVN with mvn data
"""
@@ -25,10 +46,10 @@
dtype = inputs.dtype
frame, dim = inputs.shape
- meams = np.tile(mvn[0:1, :dim], (frame, 1))
- vars = np.tile(mvn[1:2, :dim], (frame, 1))
- inputs += torch.from_numpy(meams).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)
@@ -56,100 +77,425 @@
class WavFrontend(AbsFrontend):
"""Conventional frontend structure for ASR.
"""
- def __init__(
- self,
- fs: Union[int, str] = 16000,
- n_fft: int = 512,
- win_length: int = 400,
- hop_length: int = 160,
- window: Optional[str] = 'hamming',
- center: bool = True,
- normalized: bool = False,
- onesided: bool = True,
- n_mels: int = 80,
- fmin: int = None,
- fmax: int = None,
- lfr_m: int = 1,
- lfr_n: int = 1,
- htk: bool = False,
- mvn_data=None,
- frontend_conf: Optional[dict] = get_default_kwargs(Frontend),
- apply_stft: bool = True,
- ):
- assert check_argument_types()
- super().__init__()
- if isinstance(fs, str):
- fs = humanfriendly.parse_size(fs)
- # Deepcopy (In general, dict shouldn't be used as default arg)
- frontend_conf = copy.deepcopy(frontend_conf)
- self.hop_length = hop_length
- self.win_length = win_length
- self.window = window
+ def __init__(
+ self,
+ cmvn_file: str = None,
+ fs: int = 16000,
+ window: str = 'hamming',
+ n_mels: int = 80,
+ frame_length: int = 25,
+ frame_shift: int = 10,
+ filter_length_min: int = -1,
+ filter_length_max: int = -1,
+ lfr_m: int = 1,
+ lfr_n: int = 1,
+ dither: float = 1.0,
+ snip_edges: bool = True,
+ upsacle_samples: bool = True,
+ ):
+ super().__init__()
self.fs = fs
- self.mvn_data = mvn_data
+ self.window = window
+ self.n_mels = n_mels
+ self.frame_length = frame_length
+ self.frame_shift = frame_shift
+ self.filter_length_min = filter_length_min
+ self.filter_length_max = filter_length_max
self.lfr_m = lfr_m
self.lfr_n = lfr_n
-
- if apply_stft:
- self.stft = Stft(
- n_fft=n_fft,
- win_length=win_length,
- hop_length=hop_length,
- center=center,
- window=window,
- normalized=normalized,
- onesided=onesided,
- )
- else:
- self.stft = None
- self.apply_stft = apply_stft
-
- if frontend_conf is not None:
- self.frontend = Frontend(idim=n_fft // 2 + 1, **frontend_conf)
- else:
- self.frontend = None
-
- self.logmel = LogMel(
- fs=fs,
- n_fft=n_fft,
- n_mels=n_mels,
- fmin=fmin,
- fmax=fmax,
- htk=htk,
- )
- self.n_mels = n_mels
- self.frontend_type = 'default'
+ self.cmvn_file = cmvn_file
+ 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
+ return self.n_mels * self.lfr_m
def forward(
- self, input: torch.Tensor,
+ 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]
+ if self.upsacle_samples:
+ waveform = waveform * (1 << 15)
+ waveform = waveform.unsqueeze(0)
+ mat = kaldi.fbank(waveform,
+ num_mel_bins=self.n_mels,
+ frame_length=self.frame_length,
+ frame_shift=self.frame_shift,
+ dither=self.dither,
+ energy_floor=0.0,
+ window_type=self.window,
+ sample_frequency=self.fs,
+ snip_edges=self.snip_edges)
- sample_frequency = self.fs
- num_mel_bins = self.n_mels
- frame_length = self.win_length * 1000 / sample_frequency
- frame_shift = self.hop_length * 1000 / sample_frequency
+ if self.lfr_m != 1 or self.lfr_n != 1:
+ mat = apply_lfr(mat, self.lfr_m, self.lfr_n)
+ 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)
- waveform = input * (1 << 15)
+ feats_lens = torch.as_tensor(feats_lens)
+ feats_pad = pad_sequence(feats,
+ batch_first=True,
+ padding_value=0.0)
+ return feats_pad, feats_lens
- mat = kaldi.fbank(waveform,
- num_mel_bins=num_mel_bins,
- frame_length=frame_length,
- frame_shift=frame_shift,
- dither=1.0,
- energy_floor=0.0,
- window_type=self.window,
- sample_frequency=sample_frequency)
- if self.lfr_m != 1 or self.lfr_n != 1:
- mat = apply_lfr(mat, self.lfr_m, self.lfr_n)
- if self.mvn_data is not None:
- mat = apply_cmvn(mat, self.mvn_data)
+ def forward_fbank(
+ 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 * (1 << 15)
+ waveform = waveform.unsqueeze(0)
+ mat = kaldi.fbank(waveform,
+ num_mel_bins=self.n_mels,
+ frame_length=self.frame_length,
+ frame_shift=self.frame_shift,
+ dither=self.dither,
+ energy_floor=0.0,
+ window_type=self.window,
+ sample_frequency=self.fs)
- input_feats = mat[None, :]
- feats_lens = torch.randn(1)
- feats_lens.fill_(input_feats.shape[1])
+ feat_length = mat.size(0)
+ feats.append(mat)
+ feats_lens.append(feat_length)
- return input_feats, feats_lens
+ feats_lens = torch.as_tensor(feats_lens)
+ feats_pad = pad_sequence(feats,
+ batch_first=True,
+ padding_value=0.0)
+ return feats_pad, feats_lens
+
+ def forward_lfr_cmvn(
+ 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):
+ 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 is not None:
+ mat = apply_cmvn(mat, self.cmvn)
+ 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
+
+
+class WavFrontendOnline(AbsFrontend):
+ """Conventional frontend structure for streaming ASR/VAD.
+ """
+
+ def __init__(
+ self,
+ cmvn_file: str = None,
+ fs: int = 16000,
+ window: str = 'hamming',
+ n_mels: int = 80,
+ frame_length: int = 25,
+ frame_shift: int = 10,
+ filter_length_min: int = -1,
+ filter_length_max: int = -1,
+ lfr_m: int = 1,
+ lfr_n: int = 1,
+ dither: float = 1.0,
+ snip_edges: bool = True,
+ upsacle_samples: bool = True,
+ ):
+ super().__init__()
+ self.fs = fs
+ self.window = window
+ self.n_mels = n_mels
+ self.frame_length = frame_length
+ self.frame_shift = frame_shift
+ self.frame_sample_length = int(self.frame_length * self.fs / 1000)
+ self.frame_shift_sample_length = int(self.frame_shift * self.fs / 1000)
+ self.filter_length_min = filter_length_min
+ self.filter_length_max = filter_length_max
+ self.lfr_m = lfr_m
+ self.lfr_n = lfr_n
+ self.cmvn_file = cmvn_file
+ 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.cmvn = None if self.cmvn_file is None else load_cmvn(self.cmvn_file)
+ self.input_cache = None
+ self.lfr_splice_cache = []
+
+ def output_size(self) -> int:
+ return self.n_mels * self.lfr_m
+
+ @staticmethod
+ def apply_cmvn(inputs: torch.Tensor, cmvn: torch.Tensor) -> torch.Tensor:
+ """
+ Apply CMVN with mvn data
+ """
+
+ device = inputs.device
+ dtype = inputs.dtype
+ frame, dim = inputs.shape
+
+ 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)
+
+ 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]:
+ """
+ Apply lfr with data
+ """
+
+ LFR_inputs = []
+ # inputs = torch.vstack((inputs_lfr_cache, inputs))
+ T = inputs.shape[0] # include the right context
+ T_lfr = int(np.ceil((T - (lfr_m - 1) // 2) / lfr_n)) # minus the right context: (lfr_m - 1) // 2
+ splice_idx = T_lfr
+ for i in range(T_lfr):
+ if lfr_m <= T - i * lfr_n:
+ LFR_inputs.append((inputs[i * lfr_n:i * lfr_n + lfr_m]).view(1, -1))
+ else: # process last LFR frame
+ if is_final:
+ num_padding = lfr_m - (T - i * lfr_n)
+ frame = (inputs[i * lfr_n:]).view(-1)
+ for _ in range(num_padding):
+ frame = torch.hstack((frame, inputs[-1]))
+ LFR_inputs.append(frame)
+ else:
+ # update splice_idx and break the circle
+ splice_idx = i
+ break
+ splice_idx = min(T - 1, splice_idx * lfr_n)
+ lfr_splice_cache = inputs[splice_idx:, :]
+ LFR_outputs = torch.vstack(LFR_inputs)
+ return LFR_outputs.type(torch.float32), lfr_splice_cache, splice_idx
+
+ @staticmethod
+ def compute_frame_num(sample_length: int, frame_sample_length: int, frame_shift_sample_length: int) -> int:
+ frame_num = int((sample_length - frame_sample_length) / frame_shift_sample_length + 1)
+ return frame_num if frame_num >= 1 and sample_length >= frame_sample_length else 0
+
+ def forward_fbank(
+ self,
+ input: torch.Tensor,
+ input_lengths: torch.Tensor
+ ) -> 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)
+ 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):]
+ waveforms = torch.empty(0)
+ feats_pad = torch.empty(0)
+ feats_lens = torch.empty(0)
+ if frame_num:
+ waveforms = []
+ feats = []
+ feats_lens = []
+ for i in range(batch_size):
+ waveform = input[i]
+ # we need accurate wave samples that used for fbank extracting
+ waveforms.append(
+ waveform[:((frame_num - 1) * self.frame_shift_sample_length + self.frame_sample_length)])
+ waveform = waveform * (1 << 15)
+ waveform = waveform.unsqueeze(0)
+ mat = kaldi.fbank(waveform,
+ num_mel_bins=self.n_mels,
+ frame_length=self.frame_length,
+ frame_shift=self.frame_shift,
+ dither=self.dither,
+ energy_floor=0.0,
+ window_type=self.window,
+ sample_frequency=self.fs)
+
+ feat_length = mat.size(0)
+ feats.append(mat)
+ feats_lens.append(feat_length)
+
+ waveforms = torch.stack(waveforms)
+ feats_lens = torch.as_tensor(feats_lens)
+ 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)
+ 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]:
+ batch_size = input.size(0)
+ feats = []
+ feats_lens = []
+ lfr_splice_frame_idxs = []
+ for i in range(batch_size):
+ mat = input[i, :input_lengths[i], :]
+ 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,
+ is_final)
+ if self.cmvn_file is not None:
+ mat = self.apply_cmvn(mat, self.cmvn)
+ feat_length = mat.size(0)
+ feats.append(mat)
+ feats_lens.append(feat_length)
+ lfr_splice_frame_idxs.append(lfr_splice_frame_idx)
+
+ feats_lens = torch.as_tensor(feats_lens)
+ feats_pad = pad_sequence(feats,
+ batch_first=True,
+ padding_value=0.0)
+ lfr_splice_frame_idxs = torch.as_tensor(lfr_splice_frame_idxs)
+ 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()
+ 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
+ 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
+ for i in range(batch_size):
+ self.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
+ 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)
+ if self.lfr_m == 1:
+ self.reserve_waveforms = None
+ 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]
+ 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
+ self.reserve_waveforms = self.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)
+ 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)
+ feats_lengths = torch.zeros(batch_size, dtype=torch.int) + feats.shape[1]
+ feats, feats_lengths, _ = self.forward_lfr_cmvn(feats, feats_lengths, is_final)
+ if is_final:
+ self.cache_reset()
+ 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 = []
+
+
+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,
+ ):
+ 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
+ self.n_mels = 23
+
+ def output_size(self) -> int:
+ return self.n_mels * (2 * self.lfr_m + 1)
+
+ 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.numpy()
+ mat = eend_ola_feature.stft(waveform, self.frame_length, self.frame_shift)
+ mat = eend_ola_feature.transform(mat)
+ mat = eend_ola_feature.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
--
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