import os
|
import logging
|
from contextlib import contextmanager
|
from distutils.version import LooseVersion
|
from typing import Dict
|
from typing import List
|
from typing import Optional
|
from typing import Tuple
|
from typing import Union
|
import tempfile
|
import codecs
|
import requests
|
import re
|
import copy
|
import torch
|
import torch.nn as nn
|
import random
|
import numpy as np
|
import time
|
# from funasr.layers.abs_normalize import AbsNormalize
|
from funasr.losses.label_smoothing_loss import (
|
LabelSmoothingLoss, # noqa: H301
|
)
|
# from funasr.models.ctc import CTC
|
# from funasr.models.decoder.abs_decoder import AbsDecoder
|
# from funasr.models.e2e_asr_common import ErrorCalculator
|
# from funasr.models.encoder.abs_encoder import AbsEncoder
|
# from funasr.frontends.abs_frontend import AbsFrontend
|
# from funasr.models.postencoder.abs_postencoder import AbsPostEncoder
|
from funasr.models.paraformer.cif_predictor import mae_loss
|
# from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
|
# from funasr.models.specaug.abs_specaug import AbsSpecAug
|
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.metrics.compute_acc import th_accuracy
|
from funasr.train_utils.device_funcs import force_gatherable
|
# from funasr.models.base_model import FunASRModel
|
# from funasr.models.paraformer.cif_predictor import CifPredictorV3
|
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.datasets.audio_datasets.load_audio_extract_fbank import load_audio, extract_fbank
|
from funasr.utils import postprocess_utils
|
from funasr.utils.datadir_writer import DatadirWriter
|
|
|
from funasr.models.paraformer.model import Paraformer
|
|
from funasr.register import tables
|
|
@tables.register("model_classes", "ContextualParaformer")
|
class ContextualParaformer(Paraformer):
|
"""
|
Author: Speech Lab of DAMO Academy, Alibaba Group
|
Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
|
https://arxiv.org/abs/2206.08317
|
"""
|
|
def __init__(
|
self,
|
*args,
|
**kwargs,
|
):
|
super().__init__(*args, **kwargs)
|
|
self.target_buffer_length = kwargs.get("target_buffer_length", -1)
|
inner_dim = kwargs.get("inner_dim", 256)
|
bias_encoder_type = kwargs.get("bias_encoder_type", "lstm")
|
use_decoder_embedding = kwargs.get("use_decoder_embedding", False)
|
crit_attn_weight = kwargs.get("crit_attn_weight", 0.0)
|
crit_attn_smooth = kwargs.get("crit_attn_smooth", 0.0)
|
bias_encoder_dropout_rate = kwargs.get("bias_encoder_dropout_rate", 0.0)
|
|
|
if bias_encoder_type == 'lstm':
|
logging.warning("enable bias encoder sampling and contextual training")
|
self.bias_encoder = torch.nn.LSTM(inner_dim, inner_dim, 1, batch_first=True, dropout=bias_encoder_dropout_rate)
|
self.bias_embed = torch.nn.Embedding(self.vocab_size, inner_dim)
|
elif bias_encoder_type == 'mean':
|
logging.warning("enable bias encoder sampling and contextual training")
|
self.bias_embed = torch.nn.Embedding(self.vocab_size, inner_dim)
|
else:
|
logging.error("Unsupport bias encoder type: {}".format(bias_encoder_type))
|
|
if self.target_buffer_length > 0:
|
self.hotword_buffer = None
|
self.length_record = []
|
self.current_buffer_length = 0
|
self.use_decoder_embedding = use_decoder_embedding
|
self.crit_attn_weight = crit_attn_weight
|
if self.crit_attn_weight > 0:
|
self.attn_loss = torch.nn.L1Loss()
|
self.crit_attn_smooth = crit_attn_smooth
|
|
|
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,)
|
"""
|
if len(text_lengths.size()) > 1:
|
text_lengths = text_lengths[:, 0]
|
if len(speech_lengths.size()) > 1:
|
speech_lengths = speech_lengths[:, 0]
|
|
batch_size = speech.shape[0]
|
|
hotword_pad = kwargs.get("hotword_pad")
|
hotword_lengths = kwargs.get("hotword_lengths")
|
dha_pad = kwargs.get("dha_pad")
|
|
# 1. Encoder
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
|
|
loss_ctc, cer_ctc = None, None
|
|
stats = dict()
|
|
# 1. CTC branch
|
if self.ctc_weight != 0.0:
|
loss_ctc, cer_ctc = self._calc_ctc_loss(
|
encoder_out, encoder_out_lens, text, text_lengths
|
)
|
|
# Collect CTC branch stats
|
stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
|
stats["cer_ctc"] = cer_ctc
|
|
|
# 2b. Attention decoder branch
|
loss_att, acc_att, cer_att, wer_att, loss_pre, loss_ideal = self._calc_att_clas_loss(
|
encoder_out, encoder_out_lens, text, text_lengths, hotword_pad, hotword_lengths
|
)
|
|
# 3. CTC-Att loss definition
|
if self.ctc_weight == 0.0:
|
loss = loss_att + loss_pre * self.predictor_weight
|
else:
|
loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att + loss_pre * self.predictor_weight
|
|
if loss_ideal is not None:
|
loss = loss + loss_ideal * self.crit_attn_weight
|
stats["loss_ideal"] = loss_ideal.detach().cpu()
|
|
# Collect Attn branch stats
|
stats["loss_att"] = loss_att.detach() if loss_att is not None else None
|
stats["acc"] = acc_att
|
stats["cer"] = cer_att
|
stats["wer"] = wer_att
|
stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
|
|
stats["loss"] = torch.clone(loss.detach())
|
# force_gatherable: to-device and to-tensor if scalar for DataParallel
|
if self.length_normalized_loss:
|
batch_size = int((text_lengths + self.predictor_bias).sum())
|
|
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
|
return loss, stats, weight
|
|
|
def _calc_att_clas_loss(
|
self,
|
encoder_out: torch.Tensor,
|
encoder_out_lens: torch.Tensor,
|
ys_pad: torch.Tensor,
|
ys_pad_lens: torch.Tensor,
|
hotword_pad: torch.Tensor,
|
hotword_lengths: torch.Tensor,
|
):
|
encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
|
encoder_out.device)
|
if self.predictor_bias == 1:
|
_, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
|
ys_pad_lens = ys_pad_lens + self.predictor_bias
|
pre_acoustic_embeds, pre_token_length, _, _ = self.predictor(encoder_out, ys_pad, encoder_out_mask,
|
ignore_id=self.ignore_id)
|
|
# -1. bias encoder
|
if self.use_decoder_embedding:
|
hw_embed = self.decoder.embed(hotword_pad)
|
else:
|
hw_embed = self.bias_embed(hotword_pad)
|
hw_embed, (_, _) = self.bias_encoder(hw_embed)
|
_ind = np.arange(0, hotword_pad.shape[0]).tolist()
|
selected = hw_embed[_ind, [i - 1 for i in hotword_lengths.detach().cpu().tolist()]]
|
contextual_info = selected.squeeze(0).repeat(ys_pad.shape[0], 1, 1).to(ys_pad.device)
|
|
# 0. sampler
|
decoder_out_1st = None
|
if self.sampling_ratio > 0.0:
|
if self.step_cur < 2:
|
logging.info("enable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
|
sematic_embeds, decoder_out_1st = self.sampler(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens,
|
pre_acoustic_embeds, contextual_info)
|
else:
|
if self.step_cur < 2:
|
logging.info("disable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
|
sematic_embeds = pre_acoustic_embeds
|
|
# 1. Forward decoder
|
decoder_outs = self.decoder(
|
encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=contextual_info
|
)
|
decoder_out, _ = decoder_outs[0], decoder_outs[1]
|
'''
|
if self.crit_attn_weight > 0 and attn.shape[-1] > 1:
|
ideal_attn = ideal_attn + self.crit_attn_smooth / (self.crit_attn_smooth + 1.0)
|
attn_non_blank = attn[:,:,:,:-1]
|
ideal_attn_non_blank = ideal_attn[:,:,:-1]
|
loss_ideal = self.attn_loss(attn_non_blank.max(1)[0], ideal_attn_non_blank.to(attn.device))
|
else:
|
loss_ideal = None
|
'''
|
loss_ideal = None
|
|
if decoder_out_1st is None:
|
decoder_out_1st = decoder_out
|
# 2. Compute attention loss
|
loss_att = self.criterion_att(decoder_out, ys_pad)
|
acc_att = th_accuracy(
|
decoder_out_1st.view(-1, self.vocab_size),
|
ys_pad,
|
ignore_label=self.ignore_id,
|
)
|
loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
|
|
# Compute cer/wer using attention-decoder
|
if self.training or self.error_calculator is None:
|
cer_att, wer_att = None, None
|
else:
|
ys_hat = decoder_out_1st.argmax(dim=-1)
|
cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
|
|
return loss_att, acc_att, cer_att, wer_att, loss_pre, loss_ideal
|
|
|
def sampler(self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds, contextual_info):
|
tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(ys_pad.device)
|
ys_pad = ys_pad * tgt_mask[:, :, 0]
|
if self.share_embedding:
|
ys_pad_embed = self.decoder.output_layer.weight[ys_pad]
|
else:
|
ys_pad_embed = self.decoder.embed(ys_pad)
|
with torch.no_grad():
|
decoder_outs = self.decoder(
|
encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_pad_lens, contextual_info=contextual_info
|
)
|
decoder_out, _ = decoder_outs[0], decoder_outs[1]
|
pred_tokens = decoder_out.argmax(-1)
|
nonpad_positions = ys_pad.ne(self.ignore_id)
|
seq_lens = (nonpad_positions).sum(1)
|
same_num = ((pred_tokens == ys_pad) & nonpad_positions).sum(1)
|
input_mask = torch.ones_like(nonpad_positions)
|
bsz, seq_len = ys_pad.size()
|
for li in range(bsz):
|
target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long()
|
if target_num > 0:
|
input_mask[li].scatter_(dim=0,
|
index=torch.randperm(seq_lens[li])[:target_num].to(pre_acoustic_embeds.device),
|
value=0)
|
input_mask = input_mask.eq(1)
|
input_mask = input_mask.masked_fill(~nonpad_positions, False)
|
input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device)
|
|
sematic_embeds = pre_acoustic_embeds.masked_fill(~input_mask_expand_dim, 0) + ys_pad_embed.masked_fill(
|
input_mask_expand_dim, 0)
|
return sematic_embeds * tgt_mask, decoder_out * tgt_mask
|
|
|
def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, hw_list=None,
|
clas_scale=1.0):
|
if hw_list is None:
|
hw_list = [torch.Tensor([1]).long().to(encoder_out.device)] # empty hotword list
|
hw_list_pad = pad_list(hw_list, 0)
|
if self.use_decoder_embedding:
|
hw_embed = self.decoder.embed(hw_list_pad)
|
else:
|
hw_embed = self.bias_embed(hw_list_pad)
|
hw_embed, (h_n, _) = self.bias_encoder(hw_embed)
|
hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1)
|
else:
|
hw_lengths = [len(i) for i in hw_list]
|
hw_list_pad = pad_list([torch.Tensor(i).long() for i in hw_list], 0).to(encoder_out.device)
|
if self.use_decoder_embedding:
|
hw_embed = self.decoder.embed(hw_list_pad)
|
else:
|
hw_embed = self.bias_embed(hw_list_pad)
|
hw_embed = torch.nn.utils.rnn.pack_padded_sequence(hw_embed, hw_lengths, batch_first=True,
|
enforce_sorted=False)
|
_, (h_n, _) = self.bias_encoder(hw_embed)
|
hw_embed = h_n.repeat(encoder_out.shape[0], 1, 1)
|
|
decoder_outs = self.decoder(
|
encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, contextual_info=hw_embed, clas_scale=clas_scale
|
)
|
decoder_out = decoder_outs[0]
|
decoder_out = torch.log_softmax(decoder_out, dim=-1)
|
return decoder_out, ys_pad_lens
|
|
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(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.to(device=kwargs["device"]), 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, alphas, pre_peak_index = 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_outs = self.cal_decoder_with_predictor(encoder_out, encoder_out_lens,
|
pre_acoustic_embeds,
|
pre_token_length,
|
hw_list=self.hotword_list,
|
clas_scale=kwargs.get("clas_scale", 1.0))
|
decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
|
|
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)
|
|
text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
|
result_i = {"key": key[i], "text": text_postprocessed}
|
|
if ibest_writer is not None:
|
ibest_writer["token"][key[i]] = " ".join(token)
|
ibest_writer["text"][key[i]] = text
|
ibest_writer["text_postprocessed"][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 = []
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with codecs.open(hotword_list_or_file, 'r') as fin:
|
for line in fin.readlines():
|
hw = line.strip()
|
hw_list = hw.split()
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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)
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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
|
