From 523e902edbc47f3b9feaadd49cd971e8aac627c7 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 27 十二月 2023 15:53:06 +0800
Subject: [PATCH] funasr1.0
---
funasr/models/transformer/model.py | 71 +++++++++--------------------------
1 files changed, 19 insertions(+), 52 deletions(-)
diff --git a/funasr/models/transformer/model.py b/funasr/models/transformer/model.py
index 107d62f..b710c97 100644
--- a/funasr/models/transformer/model.py
+++ b/funasr/models/transformer/model.py
@@ -1,56 +1,23 @@
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
+from typing import Union, Dict, List, Tuple, Optional
+
+import time
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 torch.cuda.amp import autocast
+
+from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
+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.models.encoder.abs_encoder import AbsEncoder
-# from funasr.models.frontend.abs_frontend import AbsFrontend
-# from funasr.models.postencoder.abs_postencoder import AbsPostEncoder
-from funasr.models.predictor.cif import mae_loss
-# from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
-# from funasr.models.specaug.abs_specaug import AbsSpecAug
-from funasr.models.transformer.add_sos_eos import add_sos_eos
-from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
-from funasr.models.transformer.utils.nets_utils import th_accuracy
from funasr.train_utils.device_funcs import force_gatherable
-# from funasr.models.base_model import FunASRModel
-# from funasr.models.predictor.cif import CifPredictorV3
-from funasr.models.paraformer.search import Hypothesis
-
-from funasr.models.model_class_factory import *
-
-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.fun_datasets.load_audio_extract_fbank import load_audio, extract_fbank
+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.register import tables
-
+@tables.register("model_classes", "Transformer")
class Transformer(nn.Module):
"""CTC-attention hybrid Encoder-Decoder model"""
@@ -93,19 +60,19 @@
super().__init__()
if frontend is not None:
- frontend_class = frontend_choices.get_class(frontend)
+ frontend_class = tables.frontend_classes.get_class(frontend.lower())
frontend = frontend_class(**frontend_conf)
if specaug is not None:
- specaug_class = specaug_choices.get_class(specaug)
+ specaug_class = tables.specaug_classes.get_class(specaug.lower())
specaug = specaug_class(**specaug_conf)
if normalize is not None:
- normalize_class = normalize_choices.get_class(normalize)
+ normalize_class = tables.normalize_classes.get_class(normalize.lower())
normalize = normalize_class(**normalize_conf)
- encoder_class = encoder_choices.get_class(encoder)
+ encoder_class = tables.encoder_classes.get_class(encoder.lower())
encoder = encoder_class(input_size=input_size, **encoder_conf)
encoder_output_size = encoder.output_size()
if decoder is not None:
- decoder_class = decoder_choices.get_class(decoder)
+ decoder_class = tables.decoder_classes.get_class(decoder.lower())
decoder = decoder_class(
vocab_size=vocab_size,
encoder_output_size=encoder_output_size,
@@ -239,7 +206,7 @@
) * loss_ctc + self.interctc_weight * loss_interctc
# decoder: Attention decoder branch
- loss_att, acc_att, cer_att, wer_att, loss_pre, pre_loss_att = self._calc_att_loss(
+ loss_att, acc_att, cer_att, wer_att = self._calc_att_loss(
encoder_out, encoder_out_lens, text, text_lengths
)
@@ -428,7 +395,7 @@
audio_sample_list = load_audio(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, date_type=kwargs.get("date_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
--
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