From 4ce2e2d76c66ddecd6903f4ffce98eaba675c685 Mon Sep 17 00:00:00 2001
From: hnluo <haoneng.lhn@alibaba-inc.com>
Date: 星期三, 31 五月 2023 18:42:38 +0800
Subject: [PATCH] Merge pull request #571 from alibaba-damo-academy/dev_lhn
---
funasr/build_utils/build_asr_model.py | 5
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/infer.sh | 104 +++++++
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/finetune.py | 37 ++
funasr/models/e2e_asr_paraformer.py | 485 +++++++++++++++++++++++++++++++++-
funasr/models/encoder/sanm_encoder.py | 55 +++
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/demo.py | 37 --
funasr/models/decoder/sanm_decoder.py | 7
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py | 32 ++
funasr/bin/asr_infer.py | 4
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/README.md | 1
funasr/bin/build_trainer.py | 2
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/demo_online.py | 40 ++
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/utils | 1
funasr/bin/asr_inference_launch.py | 4
14 files changed, 760 insertions(+), 54 deletions(-)
diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/README.md b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/README.md
new file mode 120000
index 0000000..bb55ab5
--- /dev/null
+++ b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/README.md
@@ -0,0 +1 @@
+../../TEMPLATE/README.md
\ No newline at end of file
diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/demo.py b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/demo.py
index bc511bb..283f529 100644
--- a/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/demo.py
+++ b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/demo.py
@@ -1,39 +1,12 @@
-import os
-import logging
-import torch
-import soundfile
-
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
-from modelscope.utils.logger import get_logger
-logger = get_logger(log_level=logging.CRITICAL)
-logger.setLevel(logging.CRITICAL)
-
-os.environ["MODELSCOPE_CACHE"] = "./"
inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online',
- model_revision='v1.0.4'
+ model_revision='v1.0.6',
+ mode="paraformer_fake_streaming"
)
-
-model_dir = os.path.join(os.environ["MODELSCOPE_CACHE"], "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online")
-speech, sample_rate = soundfile.read(os.path.join(model_dir, "example/asr_example.wav"))
-speech_length = speech.shape[0]
-
-sample_offset = 0
-chunk_size = [5, 10, 5] #[5, 10, 5] 600ms, [8, 8, 4] 480ms
-stride_size = chunk_size[1] * 960
-param_dict = {"cache": dict(), "is_final": False, "chunk_size": chunk_size}
-final_result = ""
-
-for sample_offset in range(0, speech_length, min(stride_size, speech_length - sample_offset)):
- if sample_offset + stride_size >= speech_length - 1:
- stride_size = speech_length - sample_offset
- param_dict["is_final"] = True
- rec_result = inference_pipeline(audio_in=speech[sample_offset: sample_offset + stride_size],
- param_dict=param_dict)
- if len(rec_result) != 0:
- final_result += rec_result['text']
- print(rec_result)
-print(final_result)
+audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav'
+rec_result = inference_pipeline(audio_in=audio_in)
+print(rec_result)
diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/demo_online.py b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/demo_online.py
new file mode 100644
index 0000000..9d49d7d
--- /dev/null
+++ b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/demo_online.py
@@ -0,0 +1,40 @@
+import os
+import logging
+import torch
+import soundfile
+
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+from modelscope.utils.logger import get_logger
+
+logger = get_logger(log_level=logging.CRITICAL)
+logger.setLevel(logging.CRITICAL)
+
+os.environ["MODELSCOPE_CACHE"] = "./"
+inference_pipeline = pipeline(
+ task=Tasks.auto_speech_recognition,
+ model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online',
+ model_revision='v1.0.6',
+ mode="paraformer_streaming"
+)
+
+model_dir = os.path.join(os.environ["MODELSCOPE_CACHE"], "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online")
+speech, sample_rate = soundfile.read(os.path.join(model_dir, "example/asr_example.wav"))
+speech_length = speech.shape[0]
+
+sample_offset = 0
+chunk_size = [5, 10, 5] #[5, 10, 5] 600ms, [8, 8, 4] 480ms
+stride_size = chunk_size[1] * 960
+param_dict = {"cache": dict(), "is_final": False, "chunk_size": chunk_size}
+final_result = ""
+
+for sample_offset in range(0, speech_length, min(stride_size, speech_length - sample_offset)):
+ if sample_offset + stride_size >= speech_length - 1:
+ stride_size = speech_length - sample_offset
+ param_dict["is_final"] = True
+ rec_result = inference_pipeline(audio_in=speech[sample_offset: sample_offset + stride_size],
+ param_dict=param_dict)
+ if len(rec_result) != 0:
+ final_result += rec_result['text']
+ print(rec_result)
+print(final_result)
diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/finetune.py b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/finetune.py
new file mode 100644
index 0000000..a72321a
--- /dev/null
+++ b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/finetune.py
@@ -0,0 +1,37 @@
+import os
+
+from modelscope.metainfo import Trainers
+from modelscope.trainers import build_trainer
+
+from funasr.datasets.ms_dataset import MsDataset
+from funasr.utils.modelscope_param import modelscope_args
+
+
+def modelscope_finetune(params):
+ if not os.path.exists(params.output_dir):
+ os.makedirs(params.output_dir, exist_ok=True)
+ # dataset split ["train", "validation"]
+ ds_dict = MsDataset.load(params.data_path)
+ kwargs = dict(
+ model=params.model,
+ model_revision='v1.0.6',
+ data_dir=ds_dict,
+ dataset_type=params.dataset_type,
+ work_dir=params.output_dir,
+ batch_bins=params.batch_bins,
+ max_epoch=params.max_epoch,
+ lr=params.lr)
+ trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
+ trainer.train()
+
+
+if __name__ == '__main__':
+ params = modelscope_args(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", data_path="./data")
+ params.output_dir = "./checkpoint" # m妯″瀷淇濆瓨璺緞
+ params.data_path = "./example_data/" # 鏁版嵁璺緞
+ params.dataset_type = "small" # 灏忔暟鎹噺璁剧疆small锛岃嫢鏁版嵁閲忓ぇ浜�1000灏忔椂锛岃浣跨敤large
+ params.batch_bins = 1000 # batch size锛屽鏋渄ataset_type="small"锛宐atch_bins鍗曚綅涓篺bank鐗瑰緛甯ф暟锛屽鏋渄ataset_type="large"锛宐atch_bins鍗曚綅涓烘绉掞紝
+ params.max_epoch = 20 # 鏈�澶ц缁冭疆鏁�
+ params.lr = 0.00005 # 璁剧疆瀛︿範鐜�
+
+ modelscope_finetune(params)
diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py
new file mode 100644
index 0000000..4a823aa
--- /dev/null
+++ b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py
@@ -0,0 +1,32 @@
+import os
+import shutil
+import argparse
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+def modelscope_infer(args):
+ os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpuid)
+ inference_pipeline = pipeline(
+ task=Tasks.auto_speech_recognition,
+ model=args.model,
+ output_dir=args.output_dir,
+ batch_size=args.batch_size,
+ model_revision='v1.0.6',
+ mode="paraformer_fake_streaming",
+ param_dict={"decoding_model": args.decoding_mode, "hotword": args.hotword_txt}
+ )
+ inference_pipeline(audio_in=args.audio_in)
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--model', type=str, default="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
+ parser.add_argument('--audio_in', type=str, default="./data/test/wav.scp")
+ parser.add_argument('--output_dir', type=str, default="./results/")
+ parser.add_argument('--decoding_mode', type=str, default="normal")
+ parser.add_argument('--model_revision', type=str, default=None)
+ parser.add_argument('--mode', type=str, default=None)
+ parser.add_argument('--hotword_txt', type=str, default=None)
+ parser.add_argument('--batch_size', type=int, default=64)
+ parser.add_argument('--gpuid', type=str, default="0")
+ args = parser.parse_args()
+ modelscope_infer(args)
diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/infer.sh b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/infer.sh
new file mode 100644
index 0000000..6d4d625
--- /dev/null
+++ b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/infer.sh
@@ -0,0 +1,104 @@
+#!/usr/bin/env bash
+
+set -e
+set -u
+set -o pipefail
+
+stage=1
+stop_stage=2
+model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online"
+data_dir="./data/test"
+output_dir="./results"
+batch_size=32
+gpu_inference=true # whether to perform gpu decoding
+gpuid_list="0,1" # set gpus, e.g., gpuid_list="0,1"
+njob=32 # the number of jobs for CPU decoding, if gpu_inference=false, use CPU decoding, please set njob
+checkpoint_dir=
+checkpoint_name="valid.cer_ctc.ave.pb"
+
+. utils/parse_options.sh || exit 1;
+
+if ${gpu_inference} == "true"; then
+ nj=$(echo $gpuid_list | awk -F "," '{print NF}')
+else
+ nj=$njob
+ batch_size=1
+ gpuid_list=""
+ for JOB in $(seq ${nj}); do
+ gpuid_list=$gpuid_list"-1,"
+ done
+fi
+
+mkdir -p $output_dir/split
+split_scps=""
+for JOB in $(seq ${nj}); do
+ split_scps="$split_scps $output_dir/split/wav.$JOB.scp"
+done
+perl utils/split_scp.pl ${data_dir}/wav.scp ${split_scps}
+
+if [ -n "${checkpoint_dir}" ]; then
+ python utils/prepare_checkpoint.py ${model} ${checkpoint_dir} ${checkpoint_name}
+ model=${checkpoint_dir}/${model}
+fi
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ];then
+ echo "Decoding ..."
+ gpuid_list_array=(${gpuid_list//,/ })
+ for JOB in $(seq ${nj}); do
+ {
+ id=$((JOB-1))
+ gpuid=${gpuid_list_array[$id]}
+ mkdir -p ${output_dir}/output.$JOB
+ python infer.py \
+ --model ${model} \
+ --audio_in ${output_dir}/split/wav.$JOB.scp \
+ --output_dir ${output_dir}/output.$JOB \
+ --batch_size ${batch_size} \
+ --gpuid ${gpuid}
+ --mode "paraformer_fake_streaming"
+ }&
+ done
+ wait
+
+ mkdir -p ${output_dir}/1best_recog
+ for f in token score text; do
+ if [ -f "${output_dir}/output.1/1best_recog/${f}" ]; then
+ for i in $(seq "${nj}"); do
+ cat "${output_dir}/output.${i}/1best_recog/${f}"
+ done | sort -k1 >"${output_dir}/1best_recog/${f}"
+ fi
+ done
+fi
+
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ];then
+ echo "Computing WER ..."
+ cp ${output_dir}/1best_recog/text ${output_dir}/1best_recog/text.proc
+ cp ${data_dir}/text ${output_dir}/1best_recog/text.ref
+ python utils/compute_wer.py ${output_dir}/1best_recog/text.ref ${output_dir}/1best_recog/text.proc ${output_dir}/1best_recog/text.cer
+ tail -n 3 ${output_dir}/1best_recog/text.cer
+fi
+
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ];then
+ echo "SpeechIO TIOBE textnorm"
+ echo "$0 --> Normalizing REF text ..."
+ ./utils/textnorm_zh.py \
+ --has_key --to_upper \
+ ${data_dir}/text \
+ ${output_dir}/1best_recog/ref.txt
+
+ echo "$0 --> Normalizing HYP text ..."
+ ./utils/textnorm_zh.py \
+ --has_key --to_upper \
+ ${output_dir}/1best_recog/text.proc \
+ ${output_dir}/1best_recog/rec.txt
+ grep -v $'\t$' ${output_dir}/1best_recog/rec.txt > ${output_dir}/1best_recog/rec_non_empty.txt
+
+ echo "$0 --> computing WER/CER and alignment ..."
+ ./utils/error_rate_zh \
+ --tokenizer char \
+ --ref ${output_dir}/1best_recog/ref.txt \
+ --hyp ${output_dir}/1best_recog/rec_non_empty.txt \
+ ${output_dir}/1best_recog/DETAILS.txt | tee ${output_dir}/1best_recog/RESULTS.txt
+ rm -rf ${output_dir}/1best_recog/rec.txt ${output_dir}/1best_recog/rec_non_empty.txt
+fi
+
diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/utils b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/utils
new file mode 120000
index 0000000..a961ddc
--- /dev/null
+++ b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/utils
@@ -0,0 +1 @@
+../../TEMPLATE/utils/
\ No newline at end of file
diff --git a/funasr/bin/asr_infer.py b/funasr/bin/asr_infer.py
index 760fd07..0e203c4 100644
--- a/funasr/bin/asr_infer.py
+++ b/funasr/bin/asr_infer.py
@@ -305,6 +305,7 @@
nbest: int = 1,
frontend_conf: dict = None,
hotword_list_or_file: str = None,
+ decoding_ind: int = 0,
**kwargs,
):
assert check_argument_types()
@@ -415,6 +416,7 @@
self.nbest = nbest
self.frontend = frontend
self.encoder_downsampling_factor = 1
+ self.decoding_ind = decoding_ind
if asr_train_args.encoder == "data2vec_encoder" or asr_train_args.encoder_conf["input_layer"] == "conv2d":
self.encoder_downsampling_factor = 4
@@ -452,7 +454,7 @@
batch = to_device(batch, device=self.device)
# b. Forward Encoder
- enc, enc_len = self.asr_model.encode(**batch)
+ enc, enc_len = self.asr_model.encode(**batch, ind=self.decoding_ind)
if isinstance(enc, tuple):
enc = enc[0]
# assert len(enc) == 1, len(enc)
diff --git a/funasr/bin/asr_inference_launch.py b/funasr/bin/asr_inference_launch.py
index ec5e175..f84212d 100644
--- a/funasr/bin/asr_inference_launch.py
+++ b/funasr/bin/asr_inference_launch.py
@@ -1638,6 +1638,8 @@
return inference_uniasr(**kwargs)
elif mode == "paraformer":
return inference_paraformer(**kwargs)
+ elif mode == "paraformer_fake_streaming":
+ return inference_paraformer(**kwargs)
elif mode == "paraformer_streaming":
return inference_paraformer_online(**kwargs)
elif mode.startswith("paraformer_vad"):
@@ -1920,4 +1922,4 @@
if __name__ == "__main__":
- main()
\ No newline at end of file
+ main()
diff --git a/funasr/bin/build_trainer.py b/funasr/bin/build_trainer.py
index 94f7262..267e405 100644
--- a/funasr/bin/build_trainer.py
+++ b/funasr/bin/build_trainer.py
@@ -23,6 +23,8 @@
from funasr.tasks.asr import ASRTask as ASRTask
elif mode == "paraformer":
from funasr.tasks.asr import ASRTaskParaformer as ASRTask
+ elif mode == "paraformer_streaming":
+ from funasr.tasks.asr import ASRTaskParaformer as ASRTask
elif mode == "paraformer_vad_punc":
from funasr.tasks.asr import ASRTaskParaformer as ASRTask
elif mode == "uniasr":
diff --git a/funasr/build_utils/build_asr_model.py b/funasr/build_utils/build_asr_model.py
index ddc827f..46c11b0 100644
--- a/funasr/build_utils/build_asr_model.py
+++ b/funasr/build_utils/build_asr_model.py
@@ -23,7 +23,7 @@
from funasr.models.joint_net.joint_network import JointNetwork
from funasr.models.e2e_asr import ASRModel
from funasr.models.e2e_asr_mfcca import MFCCA
-from funasr.models.e2e_asr_paraformer import Paraformer, ParaformerBert, BiCifParaformer, ContextualParaformer
+from funasr.models.e2e_asr_paraformer import Paraformer, ParaformerOnline, ParaformerBert, BiCifParaformer, ContextualParaformer
from funasr.models.e2e_tp import TimestampPredictor
from funasr.models.e2e_uni_asr import UniASR
from funasr.models.e2e_asr_transducer import TransducerModel, UnifiedTransducerModel
@@ -82,6 +82,7 @@
asr=ASRModel,
uniasr=UniASR,
paraformer=Paraformer,
+ paraformer_online=ParaformerOnline,
paraformer_bert=ParaformerBert,
bicif_paraformer=BiCifParaformer,
contextual_paraformer=ContextualParaformer,
@@ -293,7 +294,7 @@
token_list=token_list,
**args.model_conf,
)
- elif args.model in ["paraformer", "paraformer_bert", "bicif_paraformer", "contextual_paraformer"]:
+ elif args.model in ["paraformer", "paraformer_online", "paraformer_bert", "bicif_paraformer", "contextual_paraformer"]:
# predictor
predictor_class = predictor_choices.get_class(args.predictor)
predictor = predictor_class(**args.predictor_conf)
diff --git a/funasr/models/decoder/sanm_decoder.py b/funasr/models/decoder/sanm_decoder.py
index 18cd343..ed920bf 100644
--- a/funasr/models/decoder/sanm_decoder.py
+++ b/funasr/models/decoder/sanm_decoder.py
@@ -935,6 +935,7 @@
hlens: torch.Tensor,
ys_in_pad: torch.Tensor,
ys_in_lens: torch.Tensor,
+ chunk_mask: torch.Tensor = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Forward decoder.
@@ -955,9 +956,13 @@
"""
tgt = ys_in_pad
tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
-
+
memory = hs_pad
memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
+ if chunk_mask is not None:
+ memory_mask = memory_mask * chunk_mask
+ if tgt_mask.size(1) != memory_mask.size(1):
+ memory_mask = torch.cat((memory_mask, memory_mask[:, -2:-1, :]), dim=1)
x = tgt
x, tgt_mask, memory, memory_mask, _ = self.decoders(
diff --git a/funasr/models/e2e_asr_paraformer.py b/funasr/models/e2e_asr_paraformer.py
index 82acef2..686038e 100644
--- a/funasr/models/e2e_asr_paraformer.py
+++ b/funasr/models/e2e_asr_paraformer.py
@@ -153,6 +153,7 @@
speech_lengths: torch.Tensor,
text: torch.Tensor,
text_lengths: torch.Tensor,
+ decoding_ind: int = None,
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
"""Frontend + Encoder + Decoder + Calc loss
Args:
@@ -160,6 +161,7 @@
speech_lengths: (Batch, )
text: (Batch, Length)
text_lengths: (Batch,)
+ decoding_ind: int
"""
assert text_lengths.dim() == 1, text_lengths.shape
# Check that batch_size is unified
@@ -176,7 +178,11 @@
speech = speech[:, :speech_lengths.max()]
# 1. Encoder
- encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
+ if hasattr(self.encoder, "overlap_chunk_cls"):
+ ind = self.encoder.overlap_chunk_cls.random_choice(self.training, decoding_ind)
+ encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind)
+ else:
+ encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
intermediate_outs = None
if isinstance(encoder_out, tuple):
intermediate_outs = encoder_out[1]
@@ -272,12 +278,13 @@
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, ind: int = 0,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Frontend + Encoder. Note that this method is used by asr_inference.py
Args:
speech: (Batch, Length, ...)
speech_lengths: (Batch, )
+ ind: int
"""
with autocast(False):
# 1. Extract feats
@@ -299,11 +306,25 @@
# feats: (Batch, Length, Dim)
# -> encoder_out: (Batch, Length2, Dim2)
if self.encoder.interctc_use_conditioning:
- encoder_out, encoder_out_lens, _ = self.encoder(
- feats, feats_lengths, ctc=self.ctc
- )
+ if hasattr(self.encoder, "overlap_chunk_cls"):
+ encoder_out, encoder_out_lens, _ = self.encoder(
+ feats, feats_lengths, ctc=self.ctc, ind=ind
+ )
+ encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out,
+ encoder_out_lens,
+ chunk_outs=None)
+ else:
+ encoder_out, encoder_out_lens, _ = self.encoder(
+ feats, feats_lengths, ctc=self.ctc
+ )
else:
- encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths)
+ if hasattr(self.encoder, "overlap_chunk_cls"):
+ encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths, ind=ind)
+ encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out,
+ encoder_out_lens,
+ chunk_outs=None)
+ else:
+ encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths)
intermediate_outs = None
if isinstance(encoder_out, tuple):
intermediate_outs = encoder_out[1]
@@ -592,9 +613,137 @@
"""
def __init__(
- self, *args, **kwargs,
+ 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,
+ ctc_weight: float = 0.5,
+ interctc_weight: float = 0.0,
+ ignore_id: int = -1,
+ blank_id: int = 0,
+ sos: int = 1,
+ eos: int = 2,
+ lsm_weight: float = 0.0,
+ length_normalized_loss: bool = False,
+ report_cer: bool = True,
+ report_wer: bool = True,
+ sym_space: str = "<space>",
+ sym_blank: str = "<blank>",
+ extract_feats_in_collect_stats: bool = True,
+ predictor=None,
+ predictor_weight: float = 0.0,
+ predictor_bias: int = 0,
+ sampling_ratio: float = 0.2,
+ decoder_attention_chunk_type: str = 'chunk',
+ share_embedding: bool = False,
+ preencoder: Optional[AbsPreEncoder] = None,
+ postencoder: Optional[AbsPostEncoder] = None,
+ use_1st_decoder_loss: bool = False,
):
- super().__init__(*args, **kwargs)
+ assert check_argument_types()
+ assert 0.0 <= ctc_weight <= 1.0, ctc_weight
+ assert 0.0 <= interctc_weight < 1.0, interctc_weight
+
+ super().__init__(
+ vocab_size=vocab_size,
+ token_list=token_list,
+ frontend=frontend,
+ specaug=specaug,
+ normalize=normalize,
+ preencoder=preencoder,
+ encoder=encoder,
+ postencoder=postencoder,
+ decoder=decoder,
+ ctc=ctc,
+ ctc_weight=ctc_weight,
+ interctc_weight=interctc_weight,
+ ignore_id=ignore_id,
+ blank_id=blank_id,
+ sos=sos,
+ eos=eos,
+ lsm_weight=lsm_weight,
+ length_normalized_loss=length_normalized_loss,
+ report_cer=report_cer,
+ report_wer=report_wer,
+ sym_space=sym_space,
+ sym_blank=sym_blank,
+ extract_feats_in_collect_stats=extract_feats_in_collect_stats,
+ predictor=predictor,
+ predictor_weight=predictor_weight,
+ predictor_bias=predictor_bias,
+ sampling_ratio=sampling_ratio,
+ )
+ # note that eos is the same as sos (equivalent ID)
+ self.blank_id = blank_id
+ self.sos = vocab_size - 1 if sos is None else sos
+ self.eos = vocab_size - 1 if eos is None else eos
+ self.vocab_size = vocab_size
+ self.ignore_id = ignore_id
+ self.ctc_weight = ctc_weight
+ self.interctc_weight = interctc_weight
+ self.token_list = token_list.copy()
+
+ self.frontend = frontend
+ self.specaug = specaug
+ self.normalize = normalize
+ self.preencoder = preencoder
+ self.postencoder = postencoder
+ self.encoder = encoder
+
+ if not hasattr(self.encoder, "interctc_use_conditioning"):
+ self.encoder.interctc_use_conditioning = False
+ if self.encoder.interctc_use_conditioning:
+ self.encoder.conditioning_layer = torch.nn.Linear(
+ vocab_size, self.encoder.output_size()
+ )
+
+ self.error_calculator = None
+
+ if ctc_weight == 1.0:
+ self.decoder = None
+ else:
+ self.decoder = decoder
+
+ 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
+ )
+
+ if ctc_weight == 0.0:
+ self.ctc = None
+ else:
+ self.ctc = ctc
+
+ self.extract_feats_in_collect_stats = extract_feats_in_collect_stats
+ self.predictor = predictor
+ self.predictor_weight = predictor_weight
+ self.predictor_bias = predictor_bias
+ self.sampling_ratio = sampling_ratio
+ self.criterion_pre = mae_loss(normalize_length=length_normalized_loss)
+ self.step_cur = 0
+ self.scama_mask = None
+ if hasattr(self.encoder, "overlap_chunk_cls") and self.encoder.overlap_chunk_cls is not None:
+ from funasr.modules.streaming_utils.chunk_utilis import build_scama_mask_for_cross_attention_decoder
+ self.build_scama_mask_for_cross_attention_decoder_fn = build_scama_mask_for_cross_attention_decoder
+ self.decoder_attention_chunk_type = decoder_attention_chunk_type
+
+ self.share_embedding = share_embedding
+ if self.share_embedding:
+ self.decoder.embed = None
+
+ self.use_1st_decoder_loss = use_1st_decoder_loss
def forward(
self,
@@ -602,6 +751,7 @@
speech_lengths: torch.Tensor,
text: torch.Tensor,
text_lengths: torch.Tensor,
+ decoding_ind: int = None,
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
"""Frontend + Encoder + Decoder + Calc loss
Args:
@@ -609,6 +759,7 @@
speech_lengths: (Batch, )
text: (Batch, Length)
text_lengths: (Batch,)
+ decoding_ind: int
"""
assert text_lengths.dim() == 1, text_lengths.shape
# Check that batch_size is unified
@@ -625,7 +776,11 @@
speech = speech[:, :speech_lengths.max()]
# 1. Encoder
- encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
+ if hasattr(self.encoder, "overlap_chunk_cls"):
+ ind = self.encoder.overlap_chunk_cls.random_choice(self.training, decoding_ind)
+ encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, ind=ind)
+ else:
+ encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
intermediate_outs = None
if isinstance(encoder_out, tuple):
intermediate_outs = encoder_out[1]
@@ -638,8 +793,12 @@
# 1. CTC branch
if self.ctc_weight != 0.0:
+ if hasattr(self.encoder, "overlap_chunk_cls"):
+ encoder_out_ctc, encoder_out_lens_ctc = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out,
+ encoder_out_lens,
+ chunk_outs=None)
loss_ctc, cer_ctc = self._calc_ctc_loss(
- encoder_out, encoder_out_lens, text, text_lengths
+ encoder_out_ctc, encoder_out_lens_ctc, text, text_lengths
)
# Collect CTC branch stats
@@ -652,8 +811,14 @@
for layer_idx, intermediate_out in intermediate_outs:
# we assume intermediate_out has the same length & padding
# as those of encoder_out
+ if hasattr(self.encoder, "overlap_chunk_cls"):
+ encoder_out_ctc, encoder_out_lens_ctc = \
+ self.encoder.overlap_chunk_cls.remove_chunk(
+ intermediate_out,
+ encoder_out_lens,
+ chunk_outs=None)
loss_ic, cer_ic = self._calc_ctc_loss(
- intermediate_out, encoder_out_lens, text, text_lengths
+ encoder_out_ctc, encoder_out_lens_ctc, text, text_lengths
)
loss_interctc = loss_interctc + loss_ic
@@ -672,7 +837,7 @@
# 2b. Attention decoder branch
if self.ctc_weight != 1.0:
- loss_att, acc_att, cer_att, wer_att, loss_pre = self._calc_att_loss(
+ loss_att, acc_att, cer_att, wer_att, loss_pre, pre_loss_att = self._calc_att_predictor_loss(
encoder_out, encoder_out_lens, text, text_lengths
)
@@ -684,8 +849,12 @@
else:
loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att + loss_pre * self.predictor_weight
+ if self.use_1st_decoder_loss and pre_loss_att is not None:
+ loss = loss + pre_loss_att
+
# Collect Attn branch stats
stats["loss_att"] = loss_att.detach() if loss_att is not None else None
+ stats["pre_loss_att"] = pre_loss_att.detach() if pre_loss_att is not None else None
stats["acc"] = acc_att
stats["cer"] = cer_att
stats["wer"] = wer_att
@@ -697,14 +866,67 @@
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, ind: int = 0,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Frontend + Encoder. Note that this method is used by asr_inference.py
+ Args:
+ speech: (Batch, Length, ...)
+ speech_lengths: (Batch, )
+ """
+ with autocast(False):
+ # 1. Extract feats
+ feats, feats_lengths = self._extract_feats(speech, speech_lengths)
+
+ # 2. Data augmentation
+ if self.specaug is not None and self.training:
+ feats, feats_lengths = self.specaug(feats, feats_lengths)
+
+ # 3. Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
+ if self.normalize is not None:
+ feats, feats_lengths = self.normalize(feats, feats_lengths)
+ # Pre-encoder, e.g. used for raw input data
+ if self.preencoder is not None:
+ feats, feats_lengths = self.preencoder(feats, feats_lengths)
+
+ # 4. Forward encoder
+ # feats: (Batch, Length, Dim)
+ # -> encoder_out: (Batch, Length2, Dim2)
+ if self.encoder.interctc_use_conditioning:
+ encoder_out, encoder_out_lens, _ = self.encoder(
+ feats, feats_lengths, ctc=self.ctc, ind=ind
+ )
+ else:
+ encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths, ind=ind)
+ intermediate_outs = None
+ if isinstance(encoder_out, tuple):
+ intermediate_outs = encoder_out[1]
+ encoder_out = encoder_out[0]
+
+ # Post-encoder, e.g. NLU
+ if self.postencoder is not None:
+ encoder_out, encoder_out_lens = self.postencoder(
+ encoder_out, encoder_out_lens
+ )
+
+ assert encoder_out.size(0) == speech.size(0), (
+ encoder_out.size(),
+ speech.size(0),
+ )
+ assert encoder_out.size(1) <= encoder_out_lens.max(), (
+ encoder_out.size(),
+ encoder_out_lens.max(),
+ )
+
+ if intermediate_outs is not None:
+ return (encoder_out, intermediate_outs), encoder_out_lens
+
+ return encoder_out, encoder_out_lens
+
def encode_chunk(
self, speech: torch.Tensor, speech_lengths: torch.Tensor, cache: dict = None
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Frontend + Encoder. Note that this method is used by asr_inference.py
-<<<<<<< HEAD
-=======
-
->>>>>>> 4cd79db451786548d8a100f25c3b03da0eb30f4b
Args:
speech: (Batch, Length, ...)
speech_lengths: (Batch, )
@@ -750,11 +972,240 @@
return encoder_out, torch.tensor([encoder_out.size(1)])
+ def _calc_att_predictor_loss(
+ self,
+ encoder_out: torch.Tensor,
+ encoder_out_lens: torch.Tensor,
+ ys_pad: torch.Tensor,
+ ys_pad_lens: torch.Tensor,
+ ):
+ encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
+ encoder_out.device)
+ if self.predictor_bias == 1:
+ _, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
+ ys_pad_lens = ys_pad_lens + self.predictor_bias
+ mask_chunk_predictor = None
+ if self.encoder.overlap_chunk_cls is not None:
+ mask_chunk_predictor = self.encoder.overlap_chunk_cls.get_mask_chunk_predictor(None,
+ device=encoder_out.device,
+ batch_size=encoder_out.size(
+ 0))
+ mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk(None, device=encoder_out.device,
+ batch_size=encoder_out.size(0))
+ encoder_out = encoder_out * mask_shfit_chunk
+ pre_acoustic_embeds, pre_token_length, pre_alphas, _ = self.predictor(encoder_out,
+ ys_pad,
+ encoder_out_mask,
+ ignore_id=self.ignore_id,
+ mask_chunk_predictor=mask_chunk_predictor,
+ target_label_length=ys_pad_lens,
+ )
+ predictor_alignments, predictor_alignments_len = self.predictor.gen_frame_alignments(pre_alphas,
+ encoder_out_lens)
+
+ scama_mask = None
+ if self.encoder.overlap_chunk_cls is not None and self.decoder_attention_chunk_type == 'chunk':
+ encoder_chunk_size = self.encoder.overlap_chunk_cls.chunk_size_pad_shift_cur
+ attention_chunk_center_bias = 0
+ attention_chunk_size = encoder_chunk_size
+ decoder_att_look_back_factor = self.encoder.overlap_chunk_cls.decoder_att_look_back_factor_cur
+ mask_shift_att_chunk_decoder = self.encoder.overlap_chunk_cls.\
+ get_mask_shift_att_chunk_decoder(None,
+ device=encoder_out.device,
+ batch_size=encoder_out.size(0)
+ )
+ scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn(
+ predictor_alignments=predictor_alignments,
+ encoder_sequence_length=encoder_out_lens,
+ chunk_size=1,
+ encoder_chunk_size=encoder_chunk_size,
+ attention_chunk_center_bias=attention_chunk_center_bias,
+ attention_chunk_size=attention_chunk_size,
+ attention_chunk_type=self.decoder_attention_chunk_type,
+ step=None,
+ predictor_mask_chunk_hopping=mask_chunk_predictor,
+ decoder_att_look_back_factor=decoder_att_look_back_factor,
+ mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder,
+ target_length=ys_pad_lens,
+ is_training=self.training,
+ )
+ elif 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)
+ # 0. sampler
+ decoder_out_1st = None
+ pre_loss_att = None
+ if self.sampling_ratio > 0.0:
+ if self.step_cur < 2:
+ logging.info("enable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
+ if self.use_1st_decoder_loss:
+ sematic_embeds, decoder_out_1st, pre_loss_att = \
+ self.sampler_with_grad(encoder_out, encoder_out_lens, ys_pad,
+ ys_pad_lens, pre_acoustic_embeds, scama_mask)
+ else:
+ sematic_embeds, decoder_out_1st = \
+ self.sampler(encoder_out, encoder_out_lens, ys_pad,
+ ys_pad_lens, pre_acoustic_embeds, scama_mask)
+ else:
+ if self.step_cur < 2:
+ logging.info("disable sampler in paraformer, sampling_ratio: {}".format(self.sampling_ratio))
+ sematic_embeds = pre_acoustic_embeds
+
+ # 1. Forward decoder
+ decoder_outs = self.decoder(
+ encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, scama_mask
+ )
+ decoder_out, _ = decoder_outs[0], decoder_outs[1]
+
+ if decoder_out_1st is None:
+ decoder_out_1st = decoder_out
+ # 2. Compute attention loss
+ loss_att = self.criterion_att(decoder_out, ys_pad)
+ acc_att = th_accuracy(
+ decoder_out_1st.view(-1, self.vocab_size),
+ ys_pad,
+ ignore_label=self.ignore_id,
+ )
+ loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
+
+ # 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_1st.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, loss_pre, pre_loss_att
+
+ def sampler(self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds, chunk_mask=None):
+
+ tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(ys_pad.device)
+ ys_pad_masked = ys_pad * tgt_mask[:, :, 0]
+ if self.share_embedding:
+ ys_pad_embed = self.decoder.output_layer.weight[ys_pad_masked]
+ else:
+ ys_pad_embed = self.decoder.embed(ys_pad_masked)
+ with torch.no_grad():
+ decoder_outs = self.decoder(
+ encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_pad_lens, chunk_mask
+ )
+ decoder_out, _ = decoder_outs[0], decoder_outs[1]
+ pred_tokens = decoder_out.argmax(-1)
+ nonpad_positions = ys_pad.ne(self.ignore_id)
+ seq_lens = (nonpad_positions).sum(1)
+ same_num = ((pred_tokens == ys_pad) & nonpad_positions).sum(1)
+ input_mask = torch.ones_like(nonpad_positions)
+ bsz, seq_len = ys_pad.size()
+ for li in range(bsz):
+ target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long()
+ if target_num > 0:
+ input_mask[li].scatter_(dim=0, index=torch.randperm(seq_lens[li])[:target_num].cuda(), value=0)
+ input_mask = input_mask.eq(1)
+ input_mask = input_mask.masked_fill(~nonpad_positions, False)
+ input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device)
+
+ sematic_embeds = pre_acoustic_embeds.masked_fill(~input_mask_expand_dim, 0) + ys_pad_embed.masked_fill(
+ input_mask_expand_dim, 0)
+ return sematic_embeds * tgt_mask, decoder_out * tgt_mask
+
+ def sampler_with_grad(self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds, chunk_mask=None):
+ tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(ys_pad.device)
+ ys_pad_masked = ys_pad * tgt_mask[:, :, 0]
+ if self.share_embedding:
+ ys_pad_embed = self.decoder.output_layer.weight[ys_pad_masked]
+ else:
+ ys_pad_embed = self.decoder.embed(ys_pad_masked)
+ decoder_outs = self.decoder(
+ encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_pad_lens, chunk_mask
+ )
+ pre_loss_att = self.criterion_att(decoder_outs[0], ys_pad)
+ decoder_out, _ = decoder_outs[0], decoder_outs[1]
+ pred_tokens = decoder_out.argmax(-1)
+ nonpad_positions = ys_pad.ne(self.ignore_id)
+ seq_lens = (nonpad_positions).sum(1)
+ same_num = ((pred_tokens == ys_pad) & nonpad_positions).sum(1)
+ input_mask = torch.ones_like(nonpad_positions)
+ bsz, seq_len = ys_pad.size()
+ for li in range(bsz):
+ target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long()
+ if target_num > 0:
+ input_mask[li].scatter_(dim=0, index=torch.randperm(seq_lens[li])[:target_num].cuda(), value=0)
+ input_mask = input_mask.eq(1)
+ input_mask = input_mask.masked_fill(~nonpad_positions, False)
+ input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device)
+
+ sematic_embeds = pre_acoustic_embeds.masked_fill(~input_mask_expand_dim, 0) + ys_pad_embed.masked_fill(
+ input_mask_expand_dim, 0)
+
+ return sematic_embeds * tgt_mask, decoder_out * tgt_mask, pre_loss_att
+
+ def calc_predictor(self, encoder_out, encoder_out_lens):
+
+ encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
+ encoder_out.device)
+ mask_chunk_predictor = None
+ if self.encoder.overlap_chunk_cls is not None:
+ mask_chunk_predictor = self.encoder.overlap_chunk_cls.get_mask_chunk_predictor(None,
+ device=encoder_out.device,
+ batch_size=encoder_out.size(
+ 0))
+ mask_shfit_chunk = self.encoder.overlap_chunk_cls.get_mask_shfit_chunk(None, device=encoder_out.device,
+ batch_size=encoder_out.size(0))
+ encoder_out = encoder_out * mask_shfit_chunk
+ pre_acoustic_embeds, pre_token_length, pre_alphas, pre_peak_index = self.predictor(encoder_out,
+ None,
+ encoder_out_mask,
+ ignore_id=self.ignore_id,
+ mask_chunk_predictor=mask_chunk_predictor,
+ target_label_length=None,
+ )
+ predictor_alignments, predictor_alignments_len = self.predictor.gen_frame_alignments(pre_alphas,
+ encoder_out_lens+1 if self.predictor.tail_threshold > 0.0 else encoder_out_lens)
+
+ scama_mask = None
+ if self.encoder.overlap_chunk_cls is not None and self.decoder_attention_chunk_type == 'chunk':
+ encoder_chunk_size = self.encoder.overlap_chunk_cls.chunk_size_pad_shift_cur
+ attention_chunk_center_bias = 0
+ attention_chunk_size = encoder_chunk_size
+ decoder_att_look_back_factor = self.encoder.overlap_chunk_cls.decoder_att_look_back_factor_cur
+ mask_shift_att_chunk_decoder = self.encoder.overlap_chunk_cls.\
+ get_mask_shift_att_chunk_decoder(None,
+ device=encoder_out.device,
+ batch_size=encoder_out.size(0)
+ )
+ scama_mask = self.build_scama_mask_for_cross_attention_decoder_fn(
+ predictor_alignments=predictor_alignments,
+ encoder_sequence_length=encoder_out_lens,
+ chunk_size=1,
+ encoder_chunk_size=encoder_chunk_size,
+ attention_chunk_center_bias=attention_chunk_center_bias,
+ attention_chunk_size=attention_chunk_size,
+ attention_chunk_type=self.decoder_attention_chunk_type,
+ step=None,
+ predictor_mask_chunk_hopping=mask_chunk_predictor,
+ decoder_att_look_back_factor=decoder_att_look_back_factor,
+ mask_shift_att_chunk_decoder=mask_shift_att_chunk_decoder,
+ target_length=None,
+ is_training=self.training,
+ )
+ self.scama_mask = scama_mask
+
+ return pre_acoustic_embeds, pre_token_length, pre_alphas, pre_peak_index
+
def calc_predictor_chunk(self, encoder_out, cache=None):
pre_acoustic_embeds, pre_token_length = \
self.predictor.forward_chunk(encoder_out, cache["encoder"])
return pre_acoustic_embeds, pre_token_length
+
+ def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens):
+ decoder_outs = self.decoder(
+ encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens, self.scama_mask
+ )
+ decoder_out = decoder_outs[0]
+ decoder_out = torch.log_softmax(decoder_out, dim=-1)
+ return decoder_out, ys_pad_lens
def cal_decoder_with_predictor_chunk(self, encoder_out, sematic_embeds, cache=None):
decoder_outs = self.decoder.forward_chunk(
@@ -1800,4 +2251,4 @@
"torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_tf,
var_dict_tf[name_tf].shape))
- return var_dict_torch_update
\ No newline at end of file
+ return var_dict_torch_update
diff --git a/funasr/models/encoder/sanm_encoder.py b/funasr/models/encoder/sanm_encoder.py
index da67586..46eabd1 100644
--- a/funasr/models/encoder/sanm_encoder.py
+++ b/funasr/models/encoder/sanm_encoder.py
@@ -633,6 +633,8 @@
self.embed = torch.nn.Linear(input_size, output_size)
elif input_layer == "pe":
self.embed = SinusoidalPositionEncoder()
+ elif input_layer == "pe_online":
+ self.embed = StreamSinusoidalPositionEncoder()
else:
raise ValueError("unknown input_layer: " + input_layer)
self.normalize_before = normalize_before
@@ -818,6 +820,59 @@
return (xs_pad, intermediate_outs), olens, None
return xs_pad, olens, None
+ def _add_overlap_chunk(self, feats: np.ndarray, cache: dict = {}):
+ if len(cache) == 0:
+ return feats
+ cache["feats"] = to_device(cache["feats"], device=feats.device)
+ overlap_feats = torch.cat((cache["feats"], feats), dim=1)
+ cache["feats"] = overlap_feats[:, -(cache["chunk_size"][0] + cache["chunk_size"][2]):, :]
+ return overlap_feats
+
+ def forward_chunk(self,
+ xs_pad: torch.Tensor,
+ ilens: torch.Tensor,
+ cache: dict = None,
+ ctc: CTC = None,
+ ):
+ xs_pad *= self.output_size() ** 0.5
+ if self.embed is None:
+ xs_pad = xs_pad
+ else:
+ xs_pad = self.embed(xs_pad, cache)
+ if cache["tail_chunk"]:
+ xs_pad = to_device(cache["feats"], device=xs_pad.device)
+ else:
+ xs_pad = self._add_overlap_chunk(xs_pad, cache)
+ encoder_outs = self.encoders0(xs_pad, None, None, None, None)
+ xs_pad, masks = encoder_outs[0], encoder_outs[1]
+ intermediate_outs = []
+ if len(self.interctc_layer_idx) == 0:
+ encoder_outs = self.encoders(xs_pad, None, None, None, None)
+ xs_pad, masks = encoder_outs[0], encoder_outs[1]
+ else:
+ for layer_idx, encoder_layer in enumerate(self.encoders):
+ encoder_outs = encoder_layer(xs_pad, None, None, None, None)
+ xs_pad, masks = encoder_outs[0], encoder_outs[1]
+ if layer_idx + 1 in self.interctc_layer_idx:
+ encoder_out = xs_pad
+
+ # intermediate outputs are also normalized
+ if self.normalize_before:
+ encoder_out = self.after_norm(encoder_out)
+
+ intermediate_outs.append((layer_idx + 1, encoder_out))
+
+ if self.interctc_use_conditioning:
+ ctc_out = ctc.softmax(encoder_out)
+ xs_pad = xs_pad + self.conditioning_layer(ctc_out)
+
+ if self.normalize_before:
+ xs_pad = self.after_norm(xs_pad)
+
+ if len(intermediate_outs) > 0:
+ return (xs_pad, intermediate_outs), None, None
+ return xs_pad, ilens, None
+
def gen_tf2torch_map_dict(self):
tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch
tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf
--
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