From e9d2cfc3a134b00f4e98271fbee3838d1ccecbcc Mon Sep 17 00:00:00 2001
From: VirtuosoQ <2416050435@qq.com>
Date: 星期五, 26 四月 2024 14:59:30 +0800
Subject: [PATCH] FunASR java http client
---
funasr/models/lcbnet/model.py | 81 ++++++++++++++++++++++++++++++----------
1 files changed, 61 insertions(+), 20 deletions(-)
diff --git a/funasr/models/lcbnet/model.py b/funasr/models/lcbnet/model.py
index c68ccd7..3ac319c 100644
--- a/funasr/models/lcbnet/model.py
+++ b/funasr/models/lcbnet/model.py
@@ -1,3 +1,8 @@
+#!/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 logging
from typing import Union, Dict, List, Tuple, Optional
@@ -17,10 +22,14 @@
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"""
-
+import pdb
+@tables.register("model_classes", "LCBNet")
+class LCBNet(nn.Module):
+ """
+ Author: Speech Lab of DAMO Academy, Alibaba Group
+ LCB-NET: LONG-CONTEXT BIASING FOR AUDIO-VISUAL SPEECH RECOGNITION
+ https://arxiv.org/abs/2401.06390
+ """
def __init__(
self,
@@ -32,10 +41,19 @@
encoder_conf: dict = None,
decoder: str = None,
decoder_conf: dict = None,
+ text_encoder: str = None,
+ text_encoder_conf: dict = None,
+ bias_predictor: str = None,
+ bias_predictor_conf: dict = None,
+ fusion_encoder: str = None,
+ fusion_encoder_conf: dict = None,
ctc: str = None,
ctc_conf: dict = None,
ctc_weight: float = 0.5,
interctc_weight: float = 0.0,
+ select_num: int = 2,
+ select_length: int = 3,
+ insert_blank: bool = True,
input_size: int = 80,
vocab_size: int = -1,
ignore_id: int = -1,
@@ -66,6 +84,16 @@
encoder_class = tables.encoder_classes.get(encoder)
encoder = encoder_class(input_size=input_size, **encoder_conf)
encoder_output_size = encoder.output_size()
+
+ # lcbnet modules: text encoder, fusion encoder and bias predictor
+ text_encoder_class = tables.encoder_classes.get(text_encoder)
+ text_encoder = text_encoder_class(input_size=vocab_size, **text_encoder_conf)
+ fusion_encoder_class = tables.encoder_classes.get(fusion_encoder)
+ fusion_encoder = fusion_encoder_class(**fusion_encoder_conf)
+ bias_predictor_class = tables.encoder_classes.get(bias_predictor)
+ bias_predictor = bias_predictor_class(**bias_predictor_conf)
+
+
if decoder is not None:
decoder_class = tables.decoder_classes.get(decoder)
decoder = decoder_class(
@@ -83,14 +111,21 @@
)
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.sos = vocab_size - 1
+ self.eos = vocab_size - 1
self.vocab_size = vocab_size
self.ignore_id = ignore_id
self.ctc_weight = ctc_weight
self.specaug = specaug
self.normalize = normalize
self.encoder = encoder
+ # lcbnet
+ self.text_encoder = text_encoder
+ self.fusion_encoder = fusion_encoder
+ self.bias_predictor = bias_predictor
+ self.select_num = select_num
+ self.select_length = select_length
+ self.insert_blank = insert_blank
if not hasattr(self.encoder, "interctc_use_conditioning"):
self.encoder.interctc_use_conditioning = False
@@ -146,8 +181,7 @@
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:
@@ -239,15 +273,12 @@
ind: int
"""
with autocast(False):
-
# 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)
@@ -264,7 +295,6 @@
if intermediate_outs is not None:
return (encoder_out, intermediate_outs), encoder_out_lens
-
return encoder_out, encoder_out_lens
def _calc_att_loss(
@@ -344,14 +374,14 @@
scorers["ngram"] = ngram
weights = dict(
- decoder=1.0 - kwargs.get("decoding_ctc_weight", 0.5),
- ctc=kwargs.get("decoding_ctc_weight", 0.5),
+ decoder=1.0 - kwargs.get("decoding_ctc_weight", 0.3),
+ ctc=kwargs.get("decoding_ctc_weight", 0.3),
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", 10),
+ beam_size=kwargs.get("beam_size", 20),
weights=weights,
scorers=scorers,
sos=self.sos,
@@ -391,16 +421,22 @@
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),
+ 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}"
+ audio_sample_list = sample_list[0]
+ if len(sample_list) >1:
+ ocr_sample_list = sample_list[1]
+ else:
+ ocr_sample_list = [[294, 0]]
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
+ frame_shift = 10
+ meta_data["batch_data_time"] = speech_lengths.sum().item() * frame_shift / 1000
speech = speech.to(device=kwargs["device"])
speech_lengths = speech_lengths.to(device=kwargs["device"])
@@ -408,14 +444,19 @@
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
if isinstance(encoder_out, tuple):
encoder_out = encoder_out[0]
-
+
+ ocr_list_new = [[x + 1 if x != 0 else x for x in sublist] for sublist in ocr_sample_list]
+ ocr = torch.tensor(ocr_list_new).to(device=kwargs["device"])
+ ocr_lengths = ocr.new_full([1], dtype=torch.long, fill_value=ocr.size(1)).to(device=kwargs["device"])
+ ocr, ocr_lens, _ = self.text_encoder(ocr, ocr_lengths)
+ fusion_out, _, _, _ = self.fusion_encoder(encoder_out,None, ocr, None)
+ encoder_out = encoder_out + fusion_out
# 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]
-
results = []
b, n, d = encoder_out.size()
@@ -441,7 +482,7 @@
# 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_postprocessed}
results.append(result_i)
--
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