From 8cd896d984d2627efb233795978c9a86ff543133 Mon Sep 17 00:00:00 2001
From: Shi Xian <40013335+R1ckShi@users.noreply.github.com>
Date: 星期六, 09 三月 2024 13:55:54 +0800
Subject: [PATCH] update hotword finetuning (#1452)
---
funasr/models/seaco_paraformer/model.py | 8
examples/industrial_data_pretraining/seaco_paraformer/finetune_from_local.sh | 71 ++++++++++
examples/industrial_data_pretraining/contextual_paraformer/finetune.sh | 44 ++++++
funasr/models/contextual_paraformer/model.py | 2
examples/industrial_data_pretraining/contextual_paraformer/finetune_from_local.sh | 71 ++++++++++
examples/industrial_data_pretraining/seaco_paraformer/finetune.sh | 44 ++++++
funasr/datasets/audio_datasets/datasets.py | 127 ++++++++++++++++++
7 files changed, 362 insertions(+), 5 deletions(-)
diff --git a/examples/industrial_data_pretraining/contextual_paraformer/finetune.sh b/examples/industrial_data_pretraining/contextual_paraformer/finetune.sh
new file mode 100644
index 0000000..98cc73c
--- /dev/null
+++ b/examples/industrial_data_pretraining/contextual_paraformer/finetune.sh
@@ -0,0 +1,44 @@
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
+
+# method1, finetune from model hub
+
+# which gpu to train or finetune
+export CUDA_VISIBLE_DEVICES="0,1"
+gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+
+# data dir, which contains: train.json, val.json
+data_dir="/Users/zhifu/funasr1.0/data/list"
+
+## generate jsonl from wav.scp and text.txt
+#python -m funasr.datasets.audio_datasets.scp2jsonl \
+#++scp_file_list='["/Users/zhifu/funasr1.0/test_local/wav.scp", "/Users/zhifu/funasr1.0/test_local/text.txt"]' \
+#++data_type_list='["source", "target"]' \
+#++jsonl_file_out=/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl
+
+train_data="${data_dir}/train.jsonl"
+val_data="${data_dir}/val.jsonl"
+
+
+# exp output dir
+output_dir="/Users/zhifu/exp"
+log_file="${output_dir}/log.txt"
+
+
+mkdir -p ${output_dir}
+echo "log_file: ${log_file}"
+
+torchrun \
+--nnodes 1 \
+--nproc_per_node ${gpu_num} \
+funasr/bin/train.py \
+++model="iic/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404" \
+++model_revision="v2.0.5" \
+++train_data_set_list="${train_data}" \
+++valid_data_set_list="${val_data}" \
+++dataset_conf.batch_size=32 \
+++dataset_conf.batch_type="example" \
+++dataset_conf.num_workers=4 \
+++train_conf.max_epoch=20 \
+++optim_conf.lr=0.0002 \
+++output_dir="${output_dir}" &> ${log_file}
\ No newline at end of file
diff --git a/examples/industrial_data_pretraining/contextual_paraformer/finetune_from_local.sh b/examples/industrial_data_pretraining/contextual_paraformer/finetune_from_local.sh
new file mode 100644
index 0000000..4dbe855
--- /dev/null
+++ b/examples/industrial_data_pretraining/contextual_paraformer/finetune_from_local.sh
@@ -0,0 +1,71 @@
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
+
+# method2, finetune from local model
+
+workspace=`pwd`
+
+echo "current path: ${workspace}" # /xxxx/funasr/examples/industrial_data_pretraining/paraformer
+
+# download model
+local_path_root=${workspace}/modelscope_models
+mkdir -p ${local_path_root}
+local_path=${local_path_root}/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
+git clone https://www.modelscope.cn/iic/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404.git ${local_path}
+
+
+# which gpu to train or finetune
+export CUDA_VISIBLE_DEVICES="0,1"
+gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+
+# data dir, which contains: train.json, val.json
+data_dir="../../../data/list"
+
+train_data="${data_dir}/train.jsonl"
+val_data="${data_dir}/val.jsonl"
+
+
+# generate train.jsonl and val.jsonl from wav.scp and text.txt
+python -m funasr.datasets.audio_datasets.scp2jsonl \
+++scp_file_list='["../../../data/list/train_wav.scp", "../../../data/list/train_text.txt"]' \
+++data_type_list='["source", "target"]' \
+++jsonl_file_out="${train_data}"
+
+python -m funasr.datasets.audio_datasets.scp2jsonl \
+++scp_file_list='["../../../data/list/val_wav.scp", "../../../data/list/val_text.txt"]' \
+++data_type_list='["source", "target"]' \
+++jsonl_file_out="${val_data}"
+
+
+tokens="${local_path}/tokens.json"
+cmvn_file="${local_path}/am.mvn"
+
+# output dir
+output_dir="./outputs"
+log_file="${output_dir}/log.txt"
+
+config_name="config.yaml"
+
+init_param="${local_path}/model.pt"
+
+mkdir -p ${output_dir}
+echo "log_file: ${log_file}"
+
+torchrun \
+--nnodes 1 \
+--nproc_per_node ${gpu_num} \
+../../../funasr/bin/train.py \
+--config-path "${local_path}" \
+--config-name "${config_name}" \
+++train_data_set_list="${train_data}" \
+++valid_data_set_list="${val_data}" \
+++tokenizer_conf.token_list="${tokens}" \
+++frontend_conf.cmvn_file="${cmvn_file}" \
+++dataset_conf.batch_size=32 \
+++dataset_conf.batch_type="example" \
+++dataset_conf.num_workers=4 \
+++train_conf.max_epoch=20 \
+++optim_conf.lr=0.0002 \
+++train_conf.log_interval=1 \
+++init_param="${init_param}" \
+++output_dir="${output_dir}" &> ${log_file}
diff --git a/examples/industrial_data_pretraining/seaco_paraformer/finetune.sh b/examples/industrial_data_pretraining/seaco_paraformer/finetune.sh
new file mode 100644
index 0000000..88f0e23
--- /dev/null
+++ b/examples/industrial_data_pretraining/seaco_paraformer/finetune.sh
@@ -0,0 +1,44 @@
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
+
+# method1, finetune from model hub
+
+# which gpu to train or finetune
+export CUDA_VISIBLE_DEVICES="0,1"
+gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+
+# data dir, which contains: train.json, val.json
+data_dir="/Users/zhifu/funasr1.0/data/list"
+
+## generate jsonl from wav.scp and text.txt
+#python -m funasr.datasets.audio_datasets.scp2jsonl \
+#++scp_file_list='["/Users/zhifu/funasr1.0/test_local/wav.scp", "/Users/zhifu/funasr1.0/test_local/text.txt"]' \
+#++data_type_list='["source", "target"]' \
+#++jsonl_file_out=/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl
+
+train_data="${data_dir}/train.jsonl"
+val_data="${data_dir}/val.jsonl"
+
+
+# exp output dir
+output_dir="/Users/zhifu/exp"
+log_file="${output_dir}/log.txt"
+
+
+mkdir -p ${output_dir}
+echo "log_file: ${log_file}"
+
+torchrun \
+--nnodes 1 \
+--nproc_per_node ${gpu_num} \
+funasr/bin/train.py \
+++model="iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
+++model_revision="v2.0.6" \
+++train_data_set_list="${train_data}" \
+++valid_data_set_list="${val_data}" \
+++dataset_conf.batch_size=32 \
+++dataset_conf.batch_type="example" \
+++dataset_conf.num_workers=4 \
+++train_conf.max_epoch=20 \
+++optim_conf.lr=0.0002 \
+++output_dir="${output_dir}" &> ${log_file}
\ No newline at end of file
diff --git a/examples/industrial_data_pretraining/seaco_paraformer/finetune_from_local.sh b/examples/industrial_data_pretraining/seaco_paraformer/finetune_from_local.sh
new file mode 100644
index 0000000..9593671
--- /dev/null
+++ b/examples/industrial_data_pretraining/seaco_paraformer/finetune_from_local.sh
@@ -0,0 +1,71 @@
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
+
+# method2, finetune from local model
+
+workspace=`pwd`
+
+echo "current path: ${workspace}" # /xxxx/funasr/examples/industrial_data_pretraining/paraformer
+
+# download model
+local_path_root=${workspace}/modelscope_models
+mkdir -p ${local_path_root}
+local_path=${local_path_root}/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
+git clone https://www.modelscope.cn/iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git ${local_path}
+
+
+# which gpu to train or finetune
+export CUDA_VISIBLE_DEVICES="0,1"
+gpu_num=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+
+# data dir, which contains: train.json, val.json
+data_dir="../../../data/list"
+
+train_data="${data_dir}/train.jsonl"
+val_data="${data_dir}/val.jsonl"
+
+
+# generate train.jsonl and val.jsonl from wav.scp and text.txt
+python -m funasr.datasets.audio_datasets.scp2jsonl \
+++scp_file_list='["../../../data/list/train_wav.scp", "../../../data/list/train_text.txt"]' \
+++data_type_list='["source", "target"]' \
+++jsonl_file_out="${train_data}"
+
+python -m funasr.datasets.audio_datasets.scp2jsonl \
+++scp_file_list='["../../../data/list/val_wav.scp", "../../../data/list/val_text.txt"]' \
+++data_type_list='["source", "target"]' \
+++jsonl_file_out="${val_data}"
+
+
+tokens="${local_path}/tokens.json"
+cmvn_file="${local_path}/am.mvn"
+
+# output dir
+output_dir="./outputs"
+log_file="${output_dir}/log.txt"
+
+config_name="config.yaml"
+
+init_param="${local_path}/model.pt"
+
+mkdir -p ${output_dir}
+echo "log_file: ${log_file}"
+
+torchrun \
+--nnodes 1 \
+--nproc_per_node ${gpu_num} \
+../../../funasr/bin/train.py \
+--config-path "${local_path}" \
+--config-name "${config_name}" \
+++train_data_set_list="${train_data}" \
+++valid_data_set_list="${val_data}" \
+++tokenizer_conf.token_list="${tokens}" \
+++frontend_conf.cmvn_file="${cmvn_file}" \
+++dataset_conf.batch_size=32 \
+++dataset_conf.batch_type="example" \
+++dataset_conf.num_workers=4 \
+++train_conf.max_epoch=20 \
+++optim_conf.lr=0.0002 \
+++train_conf.log_interval=1 \
+++init_param="${init_param}" \
+++output_dir="${output_dir}" &> ${log_file}
diff --git a/funasr/datasets/audio_datasets/datasets.py b/funasr/datasets/audio_datasets/datasets.py
index ab08fb0..260236c 100644
--- a/funasr/datasets/audio_datasets/datasets.py
+++ b/funasr/datasets/audio_datasets/datasets.py
@@ -1,4 +1,5 @@
import torch
+import random
from funasr.register import tables
from funasr.utils.load_utils import extract_fbank, load_audio_text_image_video
@@ -98,3 +99,129 @@
outputs[key] = torch.nn.utils.rnn.pad_sequence(data_list, batch_first=True, padding_value=pad_value)
return outputs
+
+@tables.register("dataset_classes", "AudioDatasetHotword")
+class AudioDatasetHotword(AudioDataset):
+ # for finetuning contextual_paraformer and seaco_paraformer
+ def __init__(
+ self,
+ *args,
+ seaco_id: bool = 0,
+ **kwargs,
+ ):
+ super().__init__(*args, **kwargs)
+ self.seaco_id = seaco_id
+
+ def __getitem__(self, index):
+ item = self.index_ds[index]
+ # import pdb;
+ # pdb.set_trace()
+ source = item["source"]
+ data_src = load_audio_text_image_video(source, fs=self.fs)
+ if self.preprocessor_speech:
+ data_src = self.preprocessor_speech(data_src, fs=self.fs)
+ speech, speech_lengths = extract_fbank(data_src, data_type=self.data_type, frontend=self.frontend, is_final=True) # speech: [b, T, d]
+
+ target = item["target"]
+ if self.preprocessor_text:
+ target = self.preprocessor_text(target)
+ if self.tokenizer:
+ ids = self.tokenizer.encode(target)
+ text = torch.tensor(ids, dtype=torch.int64)
+ else:
+ ids = target
+ text = ids
+ ids_lengths = len(ids)
+ text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)
+
+ def generate_index(length,
+ hotword_min_length=2,
+ hotword_max_length=8,
+ sample_rate=0.75,
+ double_rate=0.1,
+ pre_prob=0.0,
+ pre_index=None,
+ pre_hwlist=None):
+ if length < hotword_min_length:
+ return [-1]
+ if random.random() < sample_rate:
+ if pre_prob > 0 and random.random() < pre_prob and pre_index is not None:
+ return pre_index
+ if length == hotword_min_length:
+ return [0, length-1]
+ elif random.random() < double_rate and length > hotword_max_length + hotword_min_length + 2:
+ # sample two hotwords in a sentence
+ _max_hw_length = min(hotword_max_length, length // 2)
+ # first hotword
+ start1 = random.randint(0, length // 3)
+ end1 = random.randint(start1 + hotword_min_length - 1, start1 + _max_hw_length - 1)
+ # second hotword
+ start2 = random.randint(end1 + 1, length - hotword_min_length)
+ end2 = random.randint(min(length-1, start2+hotword_min_length-1), min(length-1, start2+hotword_max_length-1))
+ return [start1, end1, start2, end2]
+ else: # single hotword
+ start = random.randint(0, length - hotword_min_length)
+ end = random.randint(min(length-1, start+hotword_min_length-1), min(length-1, start+hotword_max_length-1))
+ return [start, end]
+ else:
+ return [-1]
+
+ hotword_indx = generate_index(text_lengths[0])
+ return {"speech": speech[0, :, :],
+ "speech_lengths": speech_lengths,
+ "text": text,
+ "text_lengths": text_lengths,
+ "hotword_indx": hotword_indx,
+ "seaco_id": self.seaco_id,
+ }
+
+ def collator(self, samples: list=None):
+ outputs = {}
+ hotword_indxs = []
+ seaco_id = samples[0]['seaco_id']
+ for sample in samples:
+ for key in sample.keys():
+ if key == 'seaco_id':
+ continue
+ elif key == 'hotword_indx':
+ hotword_indxs.append(sample[key])
+ else:
+ if key not in outputs:
+ outputs[key] = []
+ outputs[key].append(sample[key])
+
+ for key, data_list in outputs.items():
+ if isinstance(data_list[0], torch.Tensor):
+ if data_list[0].dtype == torch.int64:
+ pad_value = self.int_pad_value
+ else:
+ pad_value = self.float_pad_value
+ outputs[key] = torch.nn.utils.rnn.pad_sequence(data_list, batch_first=True, padding_value=pad_value)
+
+ hotword_list, hotword_lengths = [], []
+ text = outputs['text']
+ seaco_label_pad = torch.ones_like(text) * -1 if seaco_id else None
+ for b, (hotword_indx, one_text, length) in enumerate(zip(hotword_indxs, text, outputs['text_lengths'])):
+ length = length[0]
+ if seaco_label_pad is not None: seaco_label_pad[b][:length] = seaco_id
+ if hotword_indx[0] != -1:
+ start, end = int(hotword_indx[0]), int(hotword_indx[1])
+ hotword = one_text[start: end+1]
+ hotword_list.append(hotword)
+ hotword_lengths.append(end-start+1)
+ if seaco_label_pad is not None: seaco_label_pad[b][start: end+1] = one_text[start: end+1]
+ if len(hotword_indx) == 4 and hotword_indx[2] != -1:
+ # the second hotword if exist
+ start, end = int(hotword_indx[2]), int(hotword_indx[3])
+ hotword_list.append(one_text[start: end+1])
+ hotword_lengths.append(end-start+1)
+ if seaco_label_pad is not None: seaco_label_pad[b][start: end+1] = one_text[start: end+1]
+ hotword_list.append(torch.tensor([1]))
+ hotword_lengths.append(1)
+ hotword_pad = torch.nn.utils.rnn.pad_sequence(hotword_list,
+ batch_first=True,
+ padding_value=0)
+ outputs["hotword_pad"] = hotword_pad
+ outputs["hotword_lengths"] = torch.tensor(hotword_lengths, dtype=torch.int32)
+ if seaco_label_pad is not None: outputs['seaco_label_pad'] = seaco_label_pad
+ return outputs
\ No newline at end of file
diff --git a/funasr/models/contextual_paraformer/model.py b/funasr/models/contextual_paraformer/model.py
index 7d6f729..18cab60 100644
--- a/funasr/models/contextual_paraformer/model.py
+++ b/funasr/models/contextual_paraformer/model.py
@@ -107,7 +107,7 @@
hotword_pad = kwargs.get("hotword_pad")
hotword_lengths = kwargs.get("hotword_lengths")
- dha_pad = kwargs.get("dha_pad")
+ # dha_pad = kwargs.get("dha_pad")
# 1. Encoder
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
diff --git a/funasr/models/seaco_paraformer/model.py b/funasr/models/seaco_paraformer/model.py
index 5d0f602..92fc989 100644
--- a/funasr/models/seaco_paraformer/model.py
+++ b/funasr/models/seaco_paraformer/model.py
@@ -128,7 +128,7 @@
hotword_pad = kwargs.get("hotword_pad")
hotword_lengths = kwargs.get("hotword_lengths")
- dha_pad = kwargs.get("dha_pad")
+ seaco_label_pad = kwargs.get("seaco_label_pad")
batch_size = speech.shape[0]
# for data-parallel
@@ -148,7 +148,7 @@
ys_lengths,
hotword_pad,
hotword_lengths,
- dha_pad,
+ seaco_label_pad,
)
if self.train_decoder:
loss_att, acc_att = self._calc_att_loss(
@@ -185,7 +185,7 @@
ys_lengths: torch.Tensor,
hotword_pad: torch.Tensor,
hotword_lengths: torch.Tensor,
- dha_pad: torch.Tensor,
+ seaco_label_pad: torch.Tensor,
):
# predictor forward
encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
@@ -204,7 +204,7 @@
dec_attended, _ = self.seaco_decoder(contextual_info, _contextual_length, decoder_out, ys_lengths)
merged = self._merge(cif_attended, dec_attended)
dha_output = self.hotword_output_layer(merged[:, :-1]) # remove the last token in loss calculation
- loss_att = self.criterion_seaco(dha_output, dha_pad)
+ loss_att = self.criterion_seaco(dha_output, seaco_label_pad)
return loss_att
def _seaco_decode_with_ASF(self,
--
Gitblit v1.9.1