import os
|
import re
|
import time
|
import copy
|
import torch
|
import codecs
|
import logging
|
import tempfile
|
import requests
|
import numpy as np
|
from typing import Dict
|
from typing import List
|
from typing import Tuple
|
from typing import Union
|
from typing import Optional
|
from contextlib import contextmanager
|
from distutils.version import LooseVersion
|
|
from funasr.losses.label_smoothing_loss import (
|
LabelSmoothingLoss, # noqa: H301
|
)
|
from funasr.models.paraformer.cif_predictor import mae_loss
|
from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
|
from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
|
from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
|
from funasr.metrics.compute_acc import th_accuracy
|
from funasr.train_utils.device_funcs import force_gatherable
|
from funasr.models.paraformer.search import Hypothesis
|
|
|
if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
|
from torch.cuda.amp import autocast
|
else:
|
# Nothing to do if torch<1.6.0
|
@contextmanager
|
def autocast(enabled=True):
|
yield
|
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
|
from funasr.utils import postprocess_utils
|
from funasr.utils.datadir_writer import DatadirWriter
|
|
from funasr.models.paraformer.model import Paraformer
|
from funasr.models.bicif_paraformer.model import BiCifParaformer
|
from funasr.register import tables
|
|
|
@tables.register("model_classes", "SeacoParaformer")
|
class SeacoParaformer(BiCifParaformer, Paraformer):
|
"""
|
Author: Speech Lab of DAMO Academy, Alibaba Group
|
SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability
|
https://arxiv.org/abs/2308.03266
|
"""
|
|
def __init__(
|
self,
|
*args,
|
**kwargs,
|
):
|
super().__init__(*args, **kwargs)
|
|
self.inner_dim = kwargs.get("inner_dim", 256)
|
self.bias_encoder_type = kwargs.get("bias_encoder_type", "lstm")
|
bias_encoder_dropout_rate = kwargs.get("bias_encoder_dropout_rate", 0.0)
|
bias_encoder_bid = kwargs.get("bias_encoder_bid", False)
|
seaco_lsm_weight = kwargs.get("seaco_lsm_weight", 0.0)
|
seaco_length_normalized_loss = kwargs.get("seaco_length_normalized_loss", True)
|
|
# bias encoder
|
if self.bias_encoder_type == 'lstm':
|
logging.warning("enable bias encoder sampling and contextual training")
|
self.bias_encoder = torch.nn.LSTM(self.inner_dim,
|
self.inner_dim,
|
2,
|
batch_first=True,
|
dropout=bias_encoder_dropout_rate,
|
bidirectional=bias_encoder_bid)
|
if bias_encoder_bid:
|
self.lstm_proj = torch.nn.Linear(self.inner_dim*2, self.inner_dim)
|
else:
|
self.lstm_proj = None
|
self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim)
|
elif self.bias_encoder_type == 'mean':
|
logging.warning("enable bias encoder sampling and contextual training")
|
self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim)
|
else:
|
logging.error("Unsupport bias encoder type: {}".format(self.bias_encoder_type))
|
|
# seaco decoder
|
seaco_decoder = kwargs.get("seaco_decoder", None)
|
if seaco_decoder is not None:
|
seaco_decoder_conf = kwargs.get("seaco_decoder_conf")
|
seaco_decoder_class = tables.decoder_classes.get(seaco_decoder.lower())
|
self.seaco_decoder = seaco_decoder_class(
|
vocab_size=self.vocab_size,
|
encoder_output_size=self.inner_dim,
|
**seaco_decoder_conf,
|
)
|
self.hotword_output_layer = torch.nn.Linear(self.inner_dim, self.vocab_size)
|
self.criterion_seaco = LabelSmoothingLoss(
|
size=self.vocab_size,
|
padding_idx=self.ignore_id,
|
smoothing=seaco_lsm_weight,
|
normalize_length=seaco_length_normalized_loss,
|
)
|
self.train_decoder = kwargs.get("train_decoder", False)
|
self.NO_BIAS = kwargs.get("NO_BIAS", 8377)
|
|
def forward(
|
self,
|
speech: torch.Tensor,
|
speech_lengths: torch.Tensor,
|
text: torch.Tensor,
|
text_lengths: torch.Tensor,
|
**kwargs,
|
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
|
"""Frontend + Encoder + Decoder + Calc loss
|
|
Args:
|
speech: (Batch, Length, ...)
|
speech_lengths: (Batch, )
|
text: (Batch, Length)
|
text_lengths: (Batch,)
|
"""
|
assert text_lengths.dim() == 1, text_lengths.shape
|
# Check that batch_size is unified
|
assert (
|
speech.shape[0]
|
== speech_lengths.shape[0]
|
== text.shape[0]
|
== text_lengths.shape[0]
|
), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape)
|
|
hotword_pad = kwargs.get("hotword_pad")
|
hotword_lengths = kwargs.get("hotword_lengths")
|
dha_pad = kwargs.get("dha_pad")
|
|
batch_size = speech.shape[0]
|
self.step_cur += 1
|
# for data-parallel
|
text = text[:, : text_lengths.max()]
|
speech = speech[:, :speech_lengths.max()]
|
|
# 1. Encoder
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
if self.predictor_bias == 1:
|
_, ys_pad = add_sos_eos(text, self.sos, self.eos, self.ignore_id)
|
ys_lengths = text_lengths + self.predictor_bias
|
|
stats = dict()
|
loss_seaco = self._calc_seaco_loss(encoder_out,
|
encoder_out_lens,
|
ys_pad,
|
ys_lengths,
|
hotword_pad,
|
hotword_lengths,
|
dha_pad,
|
)
|
if self.train_decoder:
|
loss_att, acc_att = self._calc_att_loss(
|
encoder_out, encoder_out_lens, text, text_lengths
|
)
|
loss = loss_seaco + loss_att
|
stats["loss_att"] = torch.clone(loss_att.detach())
|
stats["acc_att"] = acc_att
|
else:
|
loss = loss_seaco
|
stats["loss_seaco"] = torch.clone(loss_seaco.detach())
|
stats["loss"] = torch.clone(loss.detach())
|
|
# force_gatherable: to-device and to-tensor if scalar for DataParallel
|
if self.length_normalized_loss:
|
batch_size = (text_lengths + self.predictor_bias).sum().type_as(batch_size)
|
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
|
return loss, stats, weight
|
|
def _merge(self, cif_attended, dec_attended):
|
return cif_attended + dec_attended
|
|
def _calc_seaco_loss(
|
self,
|
encoder_out: torch.Tensor,
|
encoder_out_lens: torch.Tensor,
|
ys_pad: torch.Tensor,
|
ys_lengths: torch.Tensor,
|
hotword_pad: torch.Tensor,
|
hotword_lengths: torch.Tensor,
|
dha_pad: torch.Tensor,
|
):
|
# predictor forward
|
encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
|
encoder_out.device)
|
pre_acoustic_embeds, _, _, _ = self.predictor(encoder_out, ys_pad, encoder_out_mask,
|
ignore_id=self.ignore_id)
|
# decoder forward
|
decoder_out, _ = self.decoder(encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_lengths, return_hidden=True)
|
selected = self._hotword_representation(hotword_pad,
|
hotword_lengths)
|
contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device)
|
num_hot_word = contextual_info.shape[1]
|
_contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device)
|
# dha core
|
cif_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, pre_acoustic_embeds, ys_lengths)
|
dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_out, ys_lengths)
|
merged = self._merge(cif_attended, dec_attended)
|
dha_output = self.hotword_output_layer(merged[:, :-1]) # remove the last token in loss calculation
|
loss_att = self.criterion_seaco(dha_output, dha_pad)
|
return loss_att
|
|
def _seaco_decode_with_ASF(self,
|
encoder_out,
|
encoder_out_lens,
|
sematic_embeds,
|
ys_pad_lens,
|
hw_list,
|
nfilter=50,
|
seaco_weight=1.0):
|
# decoder forward
|
decoder_out, decoder_hidden, _ = self.decoder(encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, return_hidden=True, return_both=True)
|
decoder_pred = torch.log_softmax(decoder_out, dim=-1)
|
if hw_list is not None:
|
hw_lengths = [len(i) for i in hw_list]
|
hw_list_ = [torch.Tensor(i).long() for i in hw_list]
|
hw_list_pad = pad_list(hw_list_, 0).to(encoder_out.device)
|
selected = self._hotword_representation(hw_list_pad, torch.Tensor(hw_lengths).int().to(encoder_out.device))
|
contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device)
|
num_hot_word = contextual_info.shape[1]
|
_contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device)
|
|
# ASF Core
|
if nfilter > 0 and nfilter < num_hot_word:
|
for dec in self.seaco_decoder.decoders:
|
dec.reserve_attn = True
|
# cif_attended, _ = self.decoder2(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens)
|
dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens)
|
# cif_filter = torch.topk(self.decoder2.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1], min(nfilter, num_hot_word-1))[1].tolist()
|
hotword_scores = self.seaco_decoder.decoders[-1].attn_mat[0][0].sum(0).sum(0)[:-1]
|
# hotword_scores /= torch.sqrt(torch.tensor(hw_lengths)[:-1].float()).to(hotword_scores.device)
|
dec_filter = torch.topk(hotword_scores, min(nfilter, num_hot_word-1))[1].tolist()
|
add_filter = dec_filter
|
add_filter.append(len(hw_list_pad)-1)
|
# filter hotword embedding
|
selected = selected[add_filter]
|
# again
|
contextual_info = selected.squeeze(0).repeat(encoder_out.shape[0], 1, 1).to(encoder_out.device)
|
num_hot_word = contextual_info.shape[1]
|
_contextual_length = torch.Tensor([num_hot_word]).int().repeat(encoder_out.shape[0]).to(encoder_out.device)
|
for dec in self.seaco_decoder.decoders:
|
dec.attn_mat = []
|
dec.reserve_attn = False
|
|
# SeACo Core
|
cif_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, sematic_embeds, ys_pad_lens)
|
dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_hidden, ys_pad_lens)
|
merged = self._merge(cif_attended, dec_attended)
|
|
dha_output = self.hotword_output_layer(merged) # remove the last token in loss calculation
|
dha_pred = torch.log_softmax(dha_output, dim=-1)
|
# import pdb; pdb.set_trace()
|
def _merge_res(dec_output, dha_output):
|
lmbd = torch.Tensor([seaco_weight] * dha_output.shape[0])
|
dha_ids = dha_output.max(-1)[-1][0]
|
dha_mask = (dha_ids == 8377).int().unsqueeze(-1)
|
a = (1 - lmbd) / lmbd
|
b = 1 / lmbd
|
a, b = a.to(dec_output.device), b.to(dec_output.device)
|
dha_mask = (dha_mask + a.reshape(-1, 1, 1)) / b.reshape(-1, 1, 1)
|
# logits = dec_output * dha_mask + dha_output[:,:,:-1] * (1-dha_mask)
|
logits = dec_output * dha_mask + dha_output[:,:,:] * (1-dha_mask)
|
return logits
|
merged_pred = _merge_res(decoder_pred, dha_pred)
|
return merged_pred
|
else:
|
return decoder_pred
|
|
def _hotword_representation(self,
|
hotword_pad,
|
hotword_lengths):
|
if self.bias_encoder_type != 'lstm':
|
logging.error("Unsupported bias encoder type")
|
hw_embed = self.decoder.embed(hotword_pad)
|
hw_embed, (_, _) = self.bias_encoder(hw_embed)
|
if self.lstm_proj is not None:
|
hw_embed = self.lstm_proj(hw_embed)
|
_ind = np.arange(0, hw_embed.shape[0]).tolist()
|
selected = hw_embed[_ind, [i-1 for i in hotword_lengths.detach().cpu().tolist()]]
|
return selected
|
|
'''
|
def calc_predictor(self, encoder_out, encoder_out_lens):
|
encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
|
encoder_out.device)
|
pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index, pre_token_length2 = self.predictor(encoder_out,
|
None,
|
encoder_out_mask,
|
ignore_id=self.ignore_id)
|
return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index
|
|
|
def calc_predictor_timestamp(self, encoder_out, encoder_out_lens, token_num):
|
encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
|
encoder_out.device)
|
ds_alphas, ds_cif_peak, us_alphas, us_peaks = self.predictor.get_upsample_timestamp(encoder_out,
|
encoder_out_mask,
|
token_num)
|
return ds_alphas, ds_cif_peak, us_alphas, us_peaks
|
'''
|
|
def generate(self,
|
data_in,
|
data_lengths=None,
|
key: list = None,
|
tokenizer=None,
|
frontend=None,
|
**kwargs,
|
):
|
|
# init beamsearch
|
is_use_ctc = kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None
|
is_use_lm = kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
|
if self.beam_search is None and (is_use_lm or is_use_ctc):
|
logging.info("enable beam_search")
|
self.init_beam_search(**kwargs)
|
self.nbest = kwargs.get("nbest", 1)
|
|
meta_data = {}
|
|
# extract fbank feats
|
time1 = time.perf_counter()
|
audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000))
|
time2 = time.perf_counter()
|
meta_data["load_data"] = f"{time2 - time1:0.3f}"
|
speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
|
frontend=frontend)
|
time3 = time.perf_counter()
|
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
|
meta_data[
|
"batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
|
|
speech = speech.to(device=kwargs["device"])
|
speech_lengths = speech_lengths.to(device=kwargs["device"])
|
|
# hotword
|
self.hotword_list = self.generate_hotwords_list(kwargs.get("hotword", None), tokenizer=tokenizer, frontend=frontend)
|
|
# Encoder
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
if isinstance(encoder_out, tuple):
|
encoder_out = encoder_out[0]
|
|
# predictor
|
predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens)
|
pre_acoustic_embeds, pre_token_length, _, _ = predictor_outs[0], predictor_outs[1], \
|
predictor_outs[2], predictor_outs[3]
|
pre_token_length = pre_token_length.round().long()
|
if torch.max(pre_token_length) < 1:
|
return []
|
|
|
decoder_out = self._seaco_decode_with_ASF(encoder_out, encoder_out_lens,
|
pre_acoustic_embeds,
|
pre_token_length,
|
hw_list=self.hotword_list)
|
# decoder_out, _ = decoder_outs[0], decoder_outs[1]
|
_, _, us_alphas, us_peaks = self.calc_predictor_timestamp(encoder_out, encoder_out_lens,
|
pre_token_length)
|
|
results = []
|
b, n, d = decoder_out.size()
|
for i in range(b):
|
x = encoder_out[i, :encoder_out_lens[i], :]
|
am_scores = decoder_out[i, :pre_token_length[i], :]
|
if self.beam_search is not None:
|
nbest_hyps = self.beam_search(
|
x=x, am_scores=am_scores, maxlenratio=kwargs.get("maxlenratio", 0.0),
|
minlenratio=kwargs.get("minlenratio", 0.0)
|
)
|
|
nbest_hyps = nbest_hyps[: self.nbest]
|
else:
|
|
yseq = am_scores.argmax(dim=-1)
|
score = am_scores.max(dim=-1)[0]
|
score = torch.sum(score, dim=-1)
|
# pad with mask tokens to ensure compatibility with sos/eos tokens
|
yseq = torch.tensor(
|
[self.sos] + yseq.tolist() + [self.eos], device=yseq.device
|
)
|
nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
|
for nbest_idx, hyp in enumerate(nbest_hyps):
|
ibest_writer = None
|
if ibest_writer is None and kwargs.get("output_dir") is not None:
|
writer = DatadirWriter(kwargs.get("output_dir"))
|
ibest_writer = writer[f"{nbest_idx + 1}best_recog"]
|
# remove sos/eos and get results
|
last_pos = -1
|
if isinstance(hyp.yseq, list):
|
token_int = hyp.yseq[1:last_pos]
|
else:
|
token_int = hyp.yseq[1:last_pos].tolist()
|
|
# remove blank symbol id, which is assumed to be 0
|
token_int = list(
|
filter(lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int))
|
|
if tokenizer is not None:
|
# Change integer-ids to tokens
|
token = tokenizer.ids2tokens(token_int)
|
text = tokenizer.tokens2text(token)
|
|
_, timestamp = ts_prediction_lfr6_standard(us_alphas[i][:encoder_out_lens[i] * 3],
|
us_peaks[i][:encoder_out_lens[i] * 3],
|
copy.copy(token),
|
vad_offset=kwargs.get("begin_time", 0))
|
|
text_postprocessed, time_stamp_postprocessed, word_lists = postprocess_utils.sentence_postprocess(
|
token, timestamp)
|
|
result_i = {"key": key[i], "text": text_postprocessed,
|
"timestamp": time_stamp_postprocessed,
|
}
|
|
if ibest_writer is not None:
|
ibest_writer["token"][key[i]] = " ".join(token)
|
# ibest_writer["text"][key[i]] = text
|
ibest_writer["timestamp"][key[i]] = time_stamp_postprocessed
|
ibest_writer["text"][key[i]] = text_postprocessed
|
else:
|
result_i = {"key": key[i], "token_int": token_int}
|
results.append(result_i)
|
|
return results, meta_data
|
|
|
def generate_hotwords_list(self, hotword_list_or_file, tokenizer=None, frontend=None):
|
def load_seg_dict(seg_dict_file):
|
seg_dict = {}
|
assert isinstance(seg_dict_file, str)
|
with open(seg_dict_file, "r", encoding="utf8") as f:
|
lines = f.readlines()
|
for line in lines:
|
s = line.strip().split()
|
key = s[0]
|
value = s[1:]
|
seg_dict[key] = " ".join(value)
|
return seg_dict
|
|
def seg_tokenize(txt, seg_dict):
|
pattern = re.compile(r'^[\u4E00-\u9FA50-9]+$')
|
out_txt = ""
|
for word in txt:
|
word = word.lower()
|
if word in seg_dict:
|
out_txt += seg_dict[word] + " "
|
else:
|
if pattern.match(word):
|
for char in word:
|
if char in seg_dict:
|
out_txt += seg_dict[char] + " "
|
else:
|
out_txt += "<unk>" + " "
|
else:
|
out_txt += "<unk>" + " "
|
return out_txt.strip().split()
|
|
seg_dict = None
|
if frontend.cmvn_file is not None:
|
model_dir = os.path.dirname(frontend.cmvn_file)
|
seg_dict_file = os.path.join(model_dir, 'seg_dict')
|
if os.path.exists(seg_dict_file):
|
seg_dict = load_seg_dict(seg_dict_file)
|
else:
|
seg_dict = None
|
# for None
|
if hotword_list_or_file is None:
|
hotword_list = None
|
# for local txt inputs
|
elif os.path.exists(hotword_list_or_file) and hotword_list_or_file.endswith('.txt'):
|
logging.info("Attempting to parse hotwords from local txt...")
|
hotword_list = []
|
hotword_str_list = []
|
with codecs.open(hotword_list_or_file, 'r') as fin:
|
for line in fin.readlines():
|
hw = line.strip()
|
hw_list = hw.split()
|
if seg_dict is not None:
|
hw_list = seg_tokenize(hw_list, seg_dict)
|
hotword_str_list.append(hw)
|
hotword_list.append(tokenizer.tokens2ids(hw_list))
|
hotword_list.append([self.sos])
|
hotword_str_list.append('<s>')
|
logging.info("Initialized hotword list from file: {}, hotword list: {}."
|
.format(hotword_list_or_file, hotword_str_list))
|
# for url, download and generate txt
|
elif hotword_list_or_file.startswith('http'):
|
logging.info("Attempting to parse hotwords from url...")
|
work_dir = tempfile.TemporaryDirectory().name
|
if not os.path.exists(work_dir):
|
os.makedirs(work_dir)
|
text_file_path = os.path.join(work_dir, os.path.basename(hotword_list_or_file))
|
local_file = requests.get(hotword_list_or_file)
|
open(text_file_path, "wb").write(local_file.content)
|
hotword_list_or_file = text_file_path
|
hotword_list = []
|
hotword_str_list = []
|
with codecs.open(hotword_list_or_file, 'r') as fin:
|
for line in fin.readlines():
|
hw = line.strip()
|
hw_list = hw.split()
|
if seg_dict is not None:
|
hw_list = seg_tokenize(hw_list, seg_dict)
|
hotword_str_list.append(hw)
|
hotword_list.append(tokenizer.tokens2ids(hw_list))
|
hotword_list.append([self.sos])
|
hotword_str_list.append('<s>')
|
logging.info("Initialized hotword list from file: {}, hotword list: {}."
|
.format(hotword_list_or_file, hotword_str_list))
|
# for text str input
|
elif not hotword_list_or_file.endswith('.txt'):
|
logging.info("Attempting to parse hotwords as str...")
|
hotword_list = []
|
hotword_str_list = []
|
for hw in hotword_list_or_file.strip().split():
|
hotword_str_list.append(hw)
|
hw_list = hw.strip().split()
|
if seg_dict is not None:
|
hw_list = seg_tokenize(hw_list, seg_dict)
|
hotword_list.append(tokenizer.tokens2ids(hw_list))
|
hotword_list.append([self.sos])
|
hotword_str_list.append('<s>')
|
logging.info("Hotword list: {}.".format(hotword_str_list))
|
else:
|
hotword_list = None
|
return hotword_list
|
