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/mfcca/e2e_asr_mfcca.py | 124 ++++++++++++++++++++---------------------
1 files changed, 61 insertions(+), 63 deletions(-)
diff --git a/funasr/models/mfcca/e2e_asr_mfcca.py b/funasr/models/mfcca/e2e_asr_mfcca.py
index 5ec9e94..681d1f6 100644
--- a/funasr/models/mfcca/e2e_asr_mfcca.py
+++ b/funasr/models/mfcca/e2e_asr_mfcca.py
@@ -9,15 +9,15 @@
import torch
from funasr.metrics import ErrorCalculator
-from funasr.models.transformer.utils.nets_utils import th_accuracy
-from funasr.models.transformer.add_sos_eos import add_sos_eos
+from funasr.metrics.compute_acc import th_accuracy
+from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
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.encoder.abs_encoder import AbsEncoder
-from funasr.models.frontend.abs_frontend import AbsFrontend
+from funasr.frontends.abs_frontend import AbsFrontend
from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
from funasr.models.specaug.abs_specaug import AbsSpecAug
from funasr.layers.abs_normalize import AbsNormalize
@@ -31,9 +31,12 @@
@contextmanager
def autocast(enabled=True):
yield
+
+
import pdb
import random
import math
+
class MFCCA(FunASRModel):
"""
@@ -43,26 +46,26 @@
"""
def __init__(
- self,
- vocab_size: int,
- token_list: Union[Tuple[str, ...], List[str]],
- frontend: Optional[AbsFrontend],
- specaug: Optional[AbsSpecAug],
- normalize: Optional[AbsNormalize],
- encoder: AbsEncoder,
- decoder: AbsDecoder,
- ctc: CTC,
- rnnt_decoder: None = None,
- ctc_weight: float = 0.5,
- ignore_id: int = -1,
- lsm_weight: float = 0.0,
- mask_ratio: float = 0.0,
- length_normalized_loss: bool = False,
- report_cer: bool = True,
- report_wer: bool = True,
- sym_space: str = "<space>",
- sym_blank: str = "<blank>",
- preencoder: Optional[AbsPreEncoder] = None,
+ self,
+ vocab_size: int,
+ token_list: Union[Tuple[str, ...], List[str]],
+ frontend: Optional[AbsFrontend],
+ specaug: Optional[AbsSpecAug],
+ normalize: Optional[AbsNormalize],
+ encoder: AbsEncoder,
+ decoder: AbsDecoder,
+ ctc: CTC,
+ rnnt_decoder: None = None,
+ ctc_weight: float = 0.5,
+ ignore_id: int = -1,
+ lsm_weight: float = 0.0,
+ mask_ratio: float = 0.0,
+ length_normalized_loss: bool = False,
+ report_cer: bool = True,
+ report_wer: bool = True,
+ sym_space: str = "<space>",
+ sym_blank: str = "<blank>",
+ preencoder: Optional[AbsPreEncoder] = None,
):
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
assert rnnt_decoder is None, "Not implemented"
@@ -111,11 +114,11 @@
self.error_calculator = None
def forward(
- self,
- speech: torch.Tensor,
- speech_lengths: torch.Tensor,
- text: torch.Tensor,
- text_lengths: torch.Tensor,
+ self,
+ speech: torch.Tensor,
+ speech_lengths: torch.Tensor,
+ text: torch.Tensor,
+ text_lengths: torch.Tensor,
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
"""Frontend + Encoder + Decoder + Calc loss
Args:
@@ -127,18 +130,15 @@
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[0] == speech_lengths.shape[0] == text.shape[0] == text_lengths.shape[0]
), (speech.shape, speech_lengths.shape, text.shape, text_lengths.shape)
# pdb.set_trace()
- if (speech.dim() == 3 and speech.size(2) == 8 and self.mask_ratio != 0):
+ if speech.dim() == 3 and speech.size(2) == 8 and self.mask_ratio != 0:
rate_num = random.random()
# rate_num = 0.1
- if (rate_num <= self.mask_ratio):
+ if rate_num <= self.mask_ratio:
retain_channel = math.ceil(random.random() * 8)
- if (retain_channel > 1):
+ if retain_channel > 1:
speech = speech[:, :, torch.randperm(8)[0:retain_channel].sort().values]
else:
speech = speech[:, :, torch.randperm(8)[0]]
@@ -192,17 +192,17 @@
return loss, stats, weight
def collect_feats(
- self,
- speech: torch.Tensor,
- speech_lengths: torch.Tensor,
- text: torch.Tensor,
- text_lengths: torch.Tensor,
+ self,
+ speech: torch.Tensor,
+ speech_lengths: torch.Tensor,
+ text: torch.Tensor,
+ text_lengths: torch.Tensor,
) -> Dict[str, torch.Tensor]:
feats, feats_lengths, channel_size = self._extract_feats(speech, speech_lengths)
return {"feats": feats, "feats_lengths": feats_lengths}
def encode(
- self, speech: torch.Tensor, speech_lengths: torch.Tensor
+ self, speech: torch.Tensor, speech_lengths: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Frontend + Encoder. Note that this method is used by asr_inference.py
Args:
@@ -230,7 +230,7 @@
encoder_out.size(),
speech.size(0),
)
- if (encoder_out.dim() == 4):
+ if encoder_out.dim() == 4:
assert encoder_out.size(2) <= encoder_out_lens.max(), (
encoder_out.size(),
encoder_out_lens.max(),
@@ -244,7 +244,7 @@
return encoder_out, encoder_out_lens
def _extract_feats(
- self, speech: torch.Tensor, speech_lengths: torch.Tensor
+ self, speech: torch.Tensor, speech_lengths: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
assert speech_lengths.dim() == 1, speech_lengths.shape
# for data-parallel
@@ -262,19 +262,17 @@
return feats, feats_lengths, channel_size
def _calc_att_loss(
- self,
- encoder_out: torch.Tensor,
- encoder_out_lens: torch.Tensor,
- ys_pad: torch.Tensor,
- ys_pad_lens: torch.Tensor,
+ self,
+ encoder_out: torch.Tensor,
+ encoder_out_lens: torch.Tensor,
+ ys_pad: torch.Tensor,
+ ys_pad_lens: torch.Tensor,
):
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)
@@ -294,14 +292,14 @@
return loss_att, acc_att, cer_att, wer_att
def _calc_ctc_loss(
- self,
- encoder_out: torch.Tensor,
- encoder_out_lens: torch.Tensor,
- ys_pad: torch.Tensor,
- ys_pad_lens: torch.Tensor,
+ self,
+ encoder_out: torch.Tensor,
+ encoder_out_lens: torch.Tensor,
+ ys_pad: torch.Tensor,
+ ys_pad_lens: torch.Tensor,
):
# Calc CTC loss
- if (encoder_out.dim() == 4):
+ if encoder_out.dim() == 4:
encoder_out = encoder_out.mean(1)
loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
@@ -313,10 +311,10 @@
return loss_ctc, cer_ctc
def _calc_rnnt_loss(
- self,
- encoder_out: torch.Tensor,
- encoder_out_lens: torch.Tensor,
- ys_pad: torch.Tensor,
- ys_pad_lens: torch.Tensor,
+ self,
+ encoder_out: torch.Tensor,
+ encoder_out_lens: torch.Tensor,
+ ys_pad: torch.Tensor,
+ ys_pad_lens: torch.Tensor,
):
- raise NotImplementedError
\ No newline at end of file
+ raise NotImplementedError
--
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