From d80ac2fd2df4e7fb8a28acfa512bb11472b5cc99 Mon Sep 17 00:00:00 2001
From: liugz18 <57401541+liugz18@users.noreply.github.com>
Date: 星期四, 18 七月 2024 21:34:55 +0800
Subject: [PATCH] Rename 'res' in line 514 to avoid with naming conflict with line 365
---
funasr/models/transducer/model.py | 349 +++++++++++++++++++++++++--------------------------------
1 files changed, 154 insertions(+), 195 deletions(-)
diff --git a/funasr/models/transducer/model.py b/funasr/models/transducer/model.py
index 906aa60..37925fc 100644
--- a/funasr/models/transducer/model.py
+++ b/funasr/models/transducer/model.py
@@ -1,42 +1,26 @@
+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
+
+import time
+import torch
import logging
from contextlib import contextmanager
+from typing import Dict, Optional, Tuple
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.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
-from funasr.models.model_class_factory import *
+from funasr.register import tables
+from funasr.utils import postprocess_utils
+from funasr.utils.datadir_writer import DatadirWriter
+from funasr.train_utils.device_funcs import force_gatherable
+from funasr.models.transformer.scorers.ctc import CTCPrefixScorer
+from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
+from funasr.models.transformer.scorers.length_bonus import LengthBonus
+from funasr.models.transformer.utils.nets_utils import get_transducer_task_io
+from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
+from funasr.models.transducer.beam_search_transducer import BeamSearchTransducer
+
if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
from torch.cuda.amp import autocast
@@ -45,16 +29,10 @@
@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.transformer.utils.nets_utils import get_transducer_task_io
-class Transducer(nn.Module):
- """ESPnet2ASRTransducerModel module definition."""
-
-
+@tables.register("model_classes", "Transducer")
+class Transducer(torch.nn.Module):
def __init__(
self,
frontend: Optional[str] = None,
@@ -96,53 +74,48 @@
super().__init__()
- if frontend is not None:
- frontend_class = frontend_classes.get_class(frontend)
- frontend = frontend_class(**frontend_conf)
if specaug is not None:
- specaug_class = specaug_classes.get_class(specaug)
+ specaug_class = tables.specaug_classes.get(specaug)
specaug = specaug_class(**specaug_conf)
if normalize is not None:
- normalize_class = normalize_classes.get_class(normalize)
+ normalize_class = tables.normalize_classes.get(normalize)
normalize = normalize_class(**normalize_conf)
- encoder_class = encoder_classes.get_class(encoder)
+ encoder_class = tables.encoder_classes.get(encoder)
encoder = encoder_class(input_size=input_size, **encoder_conf)
encoder_output_size = encoder.output_size()
- decoder_class = decoder_classes.get_class(decoder)
+ decoder_class = tables.decoder_classes.get(decoder)
decoder = decoder_class(
vocab_size=vocab_size,
- encoder_output_size=encoder_output_size,
**decoder_conf,
)
decoder_output_size = decoder.output_size
- joint_network_class = joint_network_classes.get_class(decoder)
+ joint_network_class = tables.joint_network_classes.get(joint_network)
joint_network = joint_network_class(
vocab_size,
encoder_output_size,
decoder_output_size,
**joint_network_conf,
)
-
-
+
self.criterion_transducer = None
self.error_calculator = None
-
+
self.use_auxiliary_ctc = auxiliary_ctc_weight > 0
self.use_auxiliary_lm_loss = auxiliary_lm_loss_weight > 0
-
+
if self.use_auxiliary_ctc:
self.ctc_lin = torch.nn.Linear(encoder.output_size(), vocab_size)
self.ctc_dropout_rate = auxiliary_ctc_dropout_rate
-
+
if self.use_auxiliary_lm_loss:
self.lm_lin = torch.nn.Linear(decoder.output_size, vocab_size)
self.lm_loss_smoothing = auxiliary_lm_loss_smoothing
-
+
self.transducer_weight = transducer_weight
self.fastemit_lambda = fastemit_lambda
-
+
self.auxiliary_ctc_weight = auxiliary_ctc_weight
self.auxiliary_lm_loss_weight = auxiliary_lm_loss_weight
self.blank_id = blank_id
@@ -157,24 +130,18 @@
self.decoder = decoder
self.joint_network = joint_network
-
-
self.criterion_att = LabelSmoothingLoss(
size=vocab_size,
padding_idx=ignore_id,
smoothing=lsm_weight,
normalize_length=length_normalized_loss,
)
- #
- # if report_cer or report_wer:
- # self.error_calculator = ErrorCalculator(
- # token_list, sym_space, sym_blank, report_cer, report_wer
- # )
- #
self.length_normalized_loss = length_normalized_loss
self.beam_search = None
-
+ self.ctc = None
+ self.ctc_weight = 0.0
+
def forward(
self,
speech: torch.Tensor,
@@ -190,35 +157,35 @@
text: (Batch, Length)
text_lengths: (Batch,)
"""
- # import pdb;
- # pdb.set_trace()
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]
# 1. Encoder
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
- if hasattr(self.encoder, 'overlap_chunk_cls') and self.encoder.overlap_chunk_cls is not None:
- encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out, encoder_out_lens,
- chunk_outs=None)
+ if (
+ hasattr(self.encoder, "overlap_chunk_cls")
+ and self.encoder.overlap_chunk_cls is not None
+ ):
+ encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(
+ encoder_out, encoder_out_lens, chunk_outs=None
+ )
# 2. Transducer-related I/O preparation
decoder_in, target, t_len, u_len = get_transducer_task_io(
text,
encoder_out_lens,
ignore_id=self.ignore_id,
)
-
+
# 3. Decoder
self.decoder.set_device(encoder_out.device)
decoder_out = self.decoder(decoder_in, u_len)
-
+
# 4. Joint Network
- joint_out = self.joint_network(
- encoder_out.unsqueeze(2), decoder_out.unsqueeze(1)
- )
-
+ joint_out = self.joint_network(encoder_out.unsqueeze(2), decoder_out.unsqueeze(1))
+
# 5. Losses
loss_trans, cer_trans, wer_trans = self._calc_transducer_loss(
encoder_out,
@@ -227,9 +194,9 @@
t_len,
u_len,
)
-
+
loss_ctc, loss_lm = 0.0, 0.0
-
+
if self.use_auxiliary_ctc:
loss_ctc = self._calc_ctc_loss(
encoder_out,
@@ -237,16 +204,16 @@
t_len,
u_len,
)
-
+
if self.use_auxiliary_lm_loss:
loss_lm = self._calc_lm_loss(decoder_out, target)
-
+
loss = (
self.transducer_weight * loss_trans
+ self.auxiliary_ctc_weight * loss_ctc
+ self.auxiliary_lm_loss_weight * loss_lm
)
-
+
stats = dict(
loss=loss.detach(),
loss_transducer=loss_trans.detach(),
@@ -255,14 +222,17 @@
cer_transducer=cer_trans,
wer_transducer=wer_trans,
)
-
+
# force_gatherable: to-device and to-tensor if scalar for DataParallel
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
-
+
return loss, stats, weight
def encode(
- self, speech: torch.Tensor, speech_lengths: torch.Tensor, **kwargs,
+ self,
+ speech: torch.Tensor,
+ speech_lengths: torch.Tensor,
+ **kwargs,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Frontend + Encoder. Note that this method is used by asr_inference.py
Args:
@@ -275,30 +245,25 @@
# Data augmentation
if self.specaug is not None and self.training:
speech, speech_lengths = self.specaug(speech, speech_lengths)
-
+
# Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
if self.normalize is not None:
speech, speech_lengths = self.normalize(speech, speech_lengths)
-
+
# Forward encoder
# feats: (Batch, Length, Dim)
# -> encoder_out: (Batch, Length2, Dim2)
- if self.encoder.interctc_use_conditioning:
- encoder_out, encoder_out_lens, _ = self.encoder(
- speech, speech_lengths, ctc=self.ctc
- )
- else:
- encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths)
+ encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths)
intermediate_outs = None
if isinstance(encoder_out, tuple):
intermediate_outs = encoder_out[1]
encoder_out = encoder_out[0]
-
+
if intermediate_outs is not None:
return (encoder_out, intermediate_outs), encoder_out_lens
-
+
return encoder_out, encoder_out_lens
-
+
def _calc_transducer_loss(
self,
encoder_out: torch.Tensor,
@@ -325,17 +290,17 @@
if self.criterion_transducer is None:
try:
from warp_rnnt import rnnt_loss as RNNTLoss
+
self.criterion_transducer = RNNTLoss
-
+
except ImportError:
logging.error(
- "warp-rnnt was not installed."
- "Please consult the installation documentation."
+ "warp-rnnt was not installed." "Please consult the installation documentation."
)
exit(1)
-
+
log_probs = torch.log_softmax(joint_out, dim=-1)
-
+
loss_transducer = self.criterion_transducer(
log_probs,
target,
@@ -346,11 +311,11 @@
fastemit_lambda=self.fastemit_lambda,
gather=True,
)
-
+
if not self.training and (self.report_cer or self.report_wer):
if self.error_calculator is None:
from funasr.metrics import ErrorCalculatorTransducer as ErrorCalculator
-
+
self.error_calculator = ErrorCalculator(
self.decoder,
self.joint_network,
@@ -360,13 +325,13 @@
report_cer=self.report_cer,
report_wer=self.report_wer,
)
-
+
cer_transducer, wer_transducer = self.error_calculator(encoder_out, target, t_len)
-
+
return loss_transducer, cer_transducer, wer_transducer
-
+
return loss_transducer, None, None
-
+
def _calc_ctc_loss(
self,
encoder_out: torch.Tensor,
@@ -386,14 +351,12 @@
loss_ctc: CTC loss value.
"""
- ctc_in = self.ctc_lin(
- torch.nn.functional.dropout(encoder_out, p=self.ctc_dropout_rate)
- )
+ ctc_in = self.ctc_lin(torch.nn.functional.dropout(encoder_out, p=self.ctc_dropout_rate))
ctc_in = torch.log_softmax(ctc_in.transpose(0, 1), dim=-1)
-
+
target_mask = target != 0
ctc_target = target[target_mask].cpu()
-
+
with torch.backends.cudnn.flags(deterministic=True):
loss_ctc = torch.nn.functional.ctc_loss(
ctc_in,
@@ -404,9 +367,9 @@
reduction="sum",
)
loss_ctc /= target.size(0)
-
+
return loss_ctc
-
+
def _calc_lm_loss(
self,
decoder_out: torch.Tensor,
@@ -424,107 +387,98 @@
"""
lm_loss_in = self.lm_lin(decoder_out[:, :-1, :]).view(-1, self.vocab_size)
lm_target = target.view(-1).type(torch.int64)
-
+
with torch.no_grad():
true_dist = lm_loss_in.clone()
true_dist.fill_(self.lm_loss_smoothing / (self.vocab_size - 1))
-
+
# Ignore blank ID (0)
ignore = lm_target == 0
lm_target = lm_target.masked_fill(ignore, 0)
-
+
true_dist.scatter_(1, lm_target.unsqueeze(1), (1 - self.lm_loss_smoothing))
-
+
loss_lm = torch.nn.functional.kl_div(
torch.log_softmax(lm_loss_in, dim=1),
true_dist,
reduction="none",
)
- loss_lm = loss_lm.masked_fill(ignore.unsqueeze(1), 0).sum() / decoder_out.size(
- 0
- )
-
+ loss_lm = loss_lm.masked_fill(ignore.unsqueeze(1), 0).sum() / decoder_out.size(0)
+
return loss_lm
-
- def init_beam_search(self,
- **kwargs,
- ):
- from funasr.models.transformer.search import BeamSearch
- from funasr.models.transformer.scorers.ctc import CTCPrefixScorer
- from funasr.models.transformer.scorers.length_bonus import LengthBonus
-
+
+ def init_beam_search(
+ self,
+ **kwargs,
+ ):
+
# 1. Build ASR model
scorers = {}
-
+
if self.ctc != None:
ctc = CTCPrefixScorer(ctc=self.ctc, eos=self.eos)
- scorers.update(
- ctc=ctc
- )
+ scorers.update(ctc=ctc)
token_list = kwargs.get("token_list")
scorers.update(
length_bonus=LengthBonus(len(token_list)),
)
-
# 3. Build ngram model
# ngram is not supported now
ngram = None
scorers["ngram"] = ngram
-
- weights = dict(
- decoder=1.0 - kwargs.get("decoding_ctc_weight"),
- ctc=kwargs.get("decoding_ctc_weight", 0.0),
- lm=kwargs.get("lm_weight", 0.0),
- ngram=kwargs.get("ngram_weight", 0.0),
- length_bonus=kwargs.get("penalty", 0.0),
- )
- beam_search = BeamSearch(
- beam_size=kwargs.get("beam_size", 2),
- weights=weights,
- scorers=scorers,
- sos=self.sos,
- eos=self.eos,
- vocab_size=len(token_list),
- token_list=token_list,
- pre_beam_score_key=None if self.ctc_weight == 1.0 else "full",
+
+ beam_search = BeamSearchTransducer(
+ self.decoder,
+ self.joint_network,
+ kwargs.get("beam_size", 2),
+ nbest=1,
)
# beam_search.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
# for scorer in scorers.values():
# if isinstance(scorer, torch.nn.Module):
# scorer.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
self.beam_search = beam_search
-
- def generate(self,
- data_in: list,
- data_lengths: list=None,
- key: list=None,
- tokenizer=None,
- **kwargs,
- ):
-
+
+ def inference(
+ self,
+ data_in: list,
+ data_lengths: list = None,
+ key: list = None,
+ tokenizer=None,
+ **kwargs,
+ ):
+
if kwargs.get("batch_size", 1) > 1:
raise NotImplementedError("batch decoding is not implemented")
-
+
# 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)
-
+ 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=self.frontend.fs, audio_fs=kwargs.get("fs", 16000))
+ audio_sample_list = load_audio_text_image_video(
+ data_in, fs=self.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=self.frontend)
+ speech, speech_lengths = extract_fbank(
+ audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=self.frontend
+ )
time3 = time.perf_counter()
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
- meta_data["batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
-
+ meta_data["batch_data_time"] = (
+ speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
+ )
+
speech = speech.to(device=kwargs["device"])
speech_lengths = speech_lengths.to(device=kwargs["device"])
@@ -532,14 +486,10 @@
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
if isinstance(encoder_out, tuple):
encoder_out = encoder_out[0]
-
- # c. Passed the encoder result and the beam search
- nbest_hyps = self.beam_search(
- x=encoder_out[0], maxlenratio=kwargs.get("maxlenratio", 0.0), minlenratio=kwargs.get("minlenratio", 0.0)
- )
-
- nbest_hyps = nbest_hyps[: self.nbest]
+ # c. Passed the encoder result and the beam search
+ nbest_hyps = self.beam_search(encoder_out[0], is_final=True)
+ nbest_hyps = nbest_hyps[: self.nbest]
results = []
b, n, d = encoder_out.size()
@@ -547,31 +497,40 @@
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"]
+ if kwargs.get("output_dir") is not None:
+ if not hasattr(self, "writer"):
+ self.writer = DatadirWriter(kwargs.get("output_dir"))
+ ibest_writer = self.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]
+ token_int = hyp.yseq # [1:last_pos]
else:
- token_int = hyp.yseq[1:last_pos].tolist()
-
+ 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))
-
+ token_int = list(
+ filter(
+ lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int
+ )
+ )
+
# 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], "token": token, "text": text, "text_postprocessed": text_postprocessed}
+ result_i = {
+ "key": key[i],
+ "token": token,
+ "text": text,
+ "text_postprocessed": text_postprocessed,
+ }
results.append(result_i)
-
+
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
-
- return results, meta_data
+ return results, meta_data
--
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