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/transformer/model.py | 257 ++++++++++++++++++++++++++-------------------------
1 files changed, 132 insertions(+), 125 deletions(-)
diff --git a/funasr/models/transformer/model.py b/funasr/models/transformer/model.py
index e2367a7..adfd525 100644
--- a/funasr/models/transformer/model.py
+++ b/funasr/models/transformer/model.py
@@ -10,6 +10,7 @@
from funasr.models.ctc.ctc import CTC
from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
from funasr.metrics.compute_acc import th_accuracy
+
# from funasr.models.e2e_asr_common import ErrorCalculator
from funasr.train_utils.device_funcs import force_gatherable
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
@@ -17,25 +18,23 @@
from funasr.utils.datadir_writer import DatadirWriter
from funasr.register import tables
+
@tables.register("model_classes", "Transformer")
class Transformer(nn.Module):
"""CTC-attention hybrid Encoder-Decoder model"""
-
def __init__(
self,
- frontend: Optional[str] = None,
- frontend_conf: Optional[Dict] = None,
- specaug: Optional[str] = None,
- specaug_conf: Optional[Dict] = None,
+ specaug: str = None,
+ specaug_conf: dict = None,
normalize: str = None,
- normalize_conf: Optional[Dict] = None,
+ normalize_conf: dict = None,
encoder: str = None,
- encoder_conf: Optional[Dict] = None,
+ encoder_conf: dict = None,
decoder: str = None,
- decoder_conf: Optional[Dict] = None,
+ decoder_conf: dict = None,
ctc: str = None,
- ctc_conf: Optional[Dict] = None,
+ ctc_conf: dict = None,
ctc_weight: float = 0.5,
interctc_weight: float = 0.0,
input_size: int = 80,
@@ -59,41 +58,35 @@
super().__init__()
- if frontend is not None:
- frontend_class = tables.frontend_classes.get_class(frontend)
- frontend = frontend_class(**frontend_conf)
if specaug is not None:
- specaug_class = tables.specaug_classes.get_class(specaug)
+ specaug_class = tables.specaug_classes.get(specaug)
specaug = specaug_class(**specaug_conf)
if normalize is not None:
- normalize_class = tables.normalize_classes.get_class(normalize)
+ normalize_class = tables.normalize_classes.get(normalize)
normalize = normalize_class(**normalize_conf)
- encoder_class = tables.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()
if decoder is not None:
- decoder_class = tables.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,
)
if ctc_weight > 0.0:
-
+
if ctc_conf is None:
ctc_conf = {}
-
- ctc = CTC(
- odim=vocab_size, encoder_output_size=encoder_output_size, **ctc_conf
- )
-
+
+ ctc = CTC(odim=vocab_size, encoder_output_size=encoder_output_size, **ctc_conf)
+
self.blank_id = blank_id
self.sos = sos if sos is not None else vocab_size - 1
self.eos = eos if eos is not None else vocab_size - 1
self.vocab_size = vocab_size
self.ignore_id = ignore_id
self.ctc_weight = ctc_weight
- self.frontend = frontend
self.specaug = specaug
self.normalize = normalize
self.encoder = encoder
@@ -111,7 +104,7 @@
self.decoder = None
else:
self.decoder = decoder
-
+
self.criterion_att = LabelSmoothingLoss(
size=vocab_size,
padding_idx=ignore_id,
@@ -124,18 +117,19 @@
# token_list, sym_space, sym_blank, report_cer, report_wer
# )
#
+ self.error_calculator = None
if ctc_weight == 0.0:
self.ctc = None
else:
self.ctc = ctc
-
+
self.share_embedding = share_embedding
if self.share_embedding:
self.decoder.embed = None
-
+
self.length_normalized_loss = length_normalized_loss
self.beam_search = None
-
+
def forward(
self,
speech: torch.Tensor,
@@ -151,36 +145,34 @@
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)
intermediate_outs = None
if isinstance(encoder_out, tuple):
intermediate_outs = encoder_out[1]
encoder_out = encoder_out[0]
-
+
loss_att, acc_att, cer_att, wer_att = None, None, None, None
loss_ctc, cer_ctc = None, None
stats = dict()
-
+
# decoder: 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
-
+
# Intermediate CTC (optional)
loss_interctc = 0.0
if self.interctc_weight != 0.0 and intermediate_outs is not None:
@@ -191,25 +183,23 @@
intermediate_out, encoder_out_lens, text, text_lengths
)
loss_interctc = loss_interctc + loss_ic
-
+
# Collect Intermedaite CTC stats
stats["loss_interctc_layer{}".format(layer_idx)] = (
loss_ic.detach() if loss_ic is not None else None
)
stats["cer_interctc_layer{}".format(layer_idx)] = cer_ic
-
+
loss_interctc = loss_interctc / len(intermediate_outs)
-
+
# calculate whole encoder loss
- loss_ctc = (
- 1 - self.interctc_weight
- ) * loss_ctc + self.interctc_weight * loss_interctc
-
+ loss_ctc = (1 - self.interctc_weight) * loss_ctc + self.interctc_weight * loss_interctc
+
# decoder: Attention decoder branch
loss_att, acc_att, cer_att, wer_att = self._calc_att_loss(
encoder_out, encoder_out_lens, text, text_lengths
)
-
+
# 3. CTC-Att loss definition
if self.ctc_weight == 0.0:
loss = loss_att
@@ -217,25 +207,27 @@
loss = loss_ctc
else:
loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att
-
+
# 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
-
+
# Collect total loss stats
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 + 1).sum())
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:
@@ -248,30 +240,28 @@
# 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
- )
+ encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths, ctc=self.ctc)
else:
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_att_loss(
self,
encoder_out: torch.Tensor,
@@ -281,12 +271,10 @@
):
ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
ys_in_lens = ys_pad_lens + 1
-
+
# 1. Forward decoder
- decoder_out, _ = self.decoder(
- encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens
- )
-
+ decoder_out, _ = self.decoder(encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens)
+
# 2. Compute attention loss
loss_att = self.criterion_att(decoder_out, ys_out_pad)
acc_att = th_accuracy(
@@ -294,16 +282,16 @@
ys_out_pad,
ignore_label=self.ignore_id,
)
-
+
# 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.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
-
+
def _calc_ctc_loss(
self,
encoder_out: torch.Tensor,
@@ -313,49 +301,48 @@
):
# Calc CTC loss
loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
-
+
# Calc CER using CTC
cer_ctc = None
if not self.training and self.error_calculator is not None:
ys_hat = self.ctc.argmax(encoder_out).data
cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
return loss_ctc, cer_ctc
-
- def init_beam_search(self,
- **kwargs,
- ):
+
+ 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
-
+
# 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(
+ decoder=self.decoder,
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),
+ decoder=1.0 - kwargs.get("decoding_ctc_weight", 0.5),
+ ctc=kwargs.get("decoding_ctc_weight", 0.5),
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),
+ beam_size=kwargs.get("beam_size", 10),
weights=weights,
scorers=scorers,
sos=self.sos,
@@ -364,57 +351,73 @@
token_list=token_list,
pre_beam_score_key=None if self.ctc_weight == 1.0 else "full",
)
- # 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,
+ data_lengths=None,
+ key: list = None,
+ tokenizer=None,
+ frontend=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):
+ if self.beam_search is None:
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))
- 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)
- 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
-
+ if (
+ isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank"
+ ): # fbank
+ speech, speech_lengths = data_in, data_lengths
+ if len(speech.shape) < 3:
+ speech = speech[None, :, :]
+ if speech_lengths is None:
+ speech_lengths = speech.shape[1]
+ else:
+ # 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),
+ data_type=kwargs.get("data_type", "sound"),
+ tokenizer=tokenizer,
+ )
+ 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"])
-
# Encoder
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)
+ x=encoder_out[0],
+ maxlenratio=kwargs.get("maxlenratio", 0.0),
+ minlenratio=kwargs.get("minlenratio", 0.0),
)
-
- nbest_hyps = nbest_hyps[: self.nbest]
+ nbest_hyps = nbest_hyps[: self.nbest]
results = []
b, n, d = encoder_out.size()
@@ -422,31 +425,35 @@
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]
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))
-
+ 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_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
+ ibest_writer["text"][key[i]] = text_postprocessed
+ return results, meta_data
--
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