From 60aef2aa96750e86158d83e52429750cf4b626e3 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 10 二月 2023 19:32:39 +0800
Subject: [PATCH] Merge pull request #95 from alibaba-damo-academy/dev_dzh
---
funasr/models/e2e_diar_sond.py | 402 ++
egs/alimeeting/diarization/sond/local/convert_label_to_rttm.py | 132
egs/alimeeting/diarization/sond/infer_alimeeting_test.py | 24
funasr/modules/multi_layer_conv.py | 52
funasr/models/encoder/opennmt_encoders/__init__.py | 0
funasr/modules/attention.py | 106
egs/alimeeting/diarization/sond/unit_test.py | 97
funasr/models/encoder/opennmt_encoders/conv_encoder.py | 277 +
funasr/models/encoder/opennmt_encoders/fsmn_encoder.py | 335 ++
funasr/models/encoder/resnet34_encoder.py | 477 ++
funasr/bin/sond_inference.py | 544 +++
funasr/bin/sv_inference_launch.py | 4
funasr/models/pooling/statistic_pooling.py | 59
funasr/utils/job_runner.py | 103
funasr/tasks/diar.py | 585 +++
funasr/bin/diar_inference_launch.py | 179 +
funasr/models/encoder/opennmt_encoders/ci_scorers.py | 38
egs/alimeeting/diarization/sond/run.sh | 48
funasr/models/encoder/opennmt_encoders/self_attention_encoder.py | 480 +++
egs/alimeeting/diarization/sond/config_fbank.yaml | 2728 +++++++++++++++++
funasr/models/frontend/wav_frontend.py | 12
funasr/utils/misc.py | 48
egs/alimeeting/diarization/sond/path.sh | 5
funasr/bin/sv_inference.py | 29
egs/alimeeting/diarization/sond/README.md | 6
egs/alimeeting/diarization/sond/config.yaml | 2740 +++++++++++++++++
26 files changed, 9,488 insertions(+), 22 deletions(-)
diff --git a/egs/alimeeting/diarization/sond/README.md b/egs/alimeeting/diarization/sond/README.md
new file mode 100644
index 0000000..8bef142
--- /dev/null
+++ b/egs/alimeeting/diarization/sond/README.md
@@ -0,0 +1,6 @@
+# Results
+You will get a DER about 4.21%, which is reported in [1], Table 6, line "SOND Oracle Profile".
+
+# Reference
+[1] Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysis, Zhihao Du, Shiliang Zhang,
+Siqi Zheng, Zhijie Yan. EMNLP 2022.
\ No newline at end of file
diff --git a/egs/alimeeting/diarization/sond/config.yaml b/egs/alimeeting/diarization/sond/config.yaml
new file mode 100644
index 0000000..072c171
--- /dev/null
+++ b/egs/alimeeting/diarization/sond/config.yaml
@@ -0,0 +1,2740 @@
+config: finetune.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/sond
+ngpu: 1
+seed: 0
+num_workers: 16
+num_att_plot: 0
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: 0
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: true
+distributed: false
+unused_parameters: true
+sharded_ddp: false
+ddp_backend: pytorch_ddp
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: false
+write_collected_feats: false
+max_epoch: 50
+patience: null
+val_scheduler_criterion:
+- valid
+- acc
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+- - valid
+ - acc
+ - max
+keep_nbest_models: 10
+nbest_averaging_interval: 0
+grad_clip: 5
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 1
+no_forward_run: false
+resume: true
+train_dtype: float32
+use_amp: false
+log_interval: 50
+use_matplotlib: false
+use_tensorboard: true
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+use_pai: true
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: null
+batch_size: 20
+valid_batch_size: null
+batch_bins: 10000
+valid_batch_bins: null
+train_shape_file:
+- /data/volume1/youyan/aishell/ark/train/speech_shape.1
+- /data/volume1/youyan/aishell/ark/train/text_shape.1
+valid_shape_file:
+- /data/volume1/youyan/aishell/ark/dev/speech_shape.1
+- /data/volume1/youyan/aishell/ark/dev/text_shape.1
+batch_type: length
+valid_batch_type: null
+fold_length:
+- 512
+- 150
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+- - /data/volume1/youyan/aishell/ark/train/data.scp
+ - speech
+ - kaldi_ark
+- - /data/volume1/youyan/aishell/ark/train/data.text.1
+ - text
+ - text
+valid_data_path_and_name_and_type:
+- - /data/volume1/youyan/aishell/ark/dev/data.scp
+ - speech
+ - kaldi_ark
+- - /data/volume1/youyan/aishell/ark/dev/data.text.1
+ - text
+ - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+ lr: 0.0005
+scheduler: warmuplr
+scheduler_conf:
+ warmup_steps: 30000
+token_list:
+- '0'
+- '1'
+- '2'
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+init: null
+input_size: null
+cmvn_file: null
+ctc_conf:
+ dropout_rate: 0.0
+ ctc_type: builtin
+ reduce: true
+ ignore_nan_grad: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: null
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
+noise_apply_prob: 1.0
+noise_db_range: '13_15'
+specaug: null
+specaug_conf: {}
+normalize: null
+normalize_conf: {}
+label_aggregator: null
+label_aggregator_conf: {}
+model: sond
+model_conf:
+ # ctc_weight: 0.0
+ lsm_weight: 0.1
+ length_normalized_loss: true
+ max_spk_num: 16
+ # predictor_weight: 1.0
+ # predictor_bias: 1
+ # sampling_ratio: 0.75
+# speech encoder
+encoder: resnet34
+encoder_conf:
+ # pass by model, equal to feature dim
+ # input_size: 80
+ pooling_type: "window_shift"
+ pool_size: 20
+ stride: 1
+ tf2torch_tensor_name_prefix_torch: encoder
+ tf2torch_tensor_name_prefix_tf: EAND/speech_encoder
+speaker_encoder: conv
+speaker_encoder_conf:
+ input_units: 256
+ num_layers: 3
+ num_units: 256
+ kernel_size: 1
+ dropout_rate: 0.0
+ position_encoder: null
+ out_units: 256
+ out_norm: false
+ auxiliary_states: false
+ tf2torch_tensor_name_prefix_torch: speaker_encoder
+ tf2torch_tensor_name_prefix_tf: EAND/speaker_encoder
+ci_scorer: dot
+ci_scorer_conf: {}
+cd_scorer: san
+cd_scorer_conf:
+ input_size: 512
+ output_size: 512
+ out_units: 1
+ attention_heads: 4
+ linear_units: 1024
+ num_blocks: 4
+ dropout_rate: 0.0
+ positional_dropout_rate: 0.0
+ attention_dropout_rate: 0.0
+ # use string "null" to remove input layer
+ input_layer: "null"
+ pos_enc_class: null
+ normalize_before: true
+ tf2torch_tensor_name_prefix_torch: cd_scorer
+ tf2torch_tensor_name_prefix_tf: EAND/compute_distance_layer
+# post net
+decoder: fsmn
+decoder_conf:
+ in_units: 32
+ out_units: 2517
+ filter_size: 31
+ fsmn_num_layers: 6
+ dnn_num_layers: 1
+ num_memory_units: 512
+ ffn_inner_dim: 512
+ dropout_rate: 0.0
+ tf2torch_tensor_name_prefix_torch: decoder
+ tf2torch_tensor_name_prefix_tf: EAND/post_net
+frontend: wav_frontend
+frontend_conf:
+ fs: 16000
+ window: povey
+ n_mels: 80
+ frame_length: 25
+ frame_shift: 10
+ filter_length_min: -1
+ filter_length_max: -1
+ lfr_m: 1
+ lfr_n: 1
+ dither: 0.0
+ snip_edges: false
+num_worker_count: 1
+required:
+- output_dir
+- token_list
+oss_bucket: 'null'
+version: 0.1.4
diff --git a/egs/alimeeting/diarization/sond/config_fbank.yaml b/egs/alimeeting/diarization/sond/config_fbank.yaml
new file mode 100644
index 0000000..cb4b8a9
--- /dev/null
+++ b/egs/alimeeting/diarization/sond/config_fbank.yaml
@@ -0,0 +1,2728 @@
+config: finetune.yaml
+print_config: false
+log_level: INFO
+dry_run: false
+iterator_type: sequence
+output_dir: exp/sond
+ngpu: 1
+seed: 0
+num_workers: 16
+num_att_plot: 0
+dist_backend: nccl
+dist_init_method: env://
+dist_world_size: null
+dist_rank: null
+local_rank: 0
+dist_master_addr: null
+dist_master_port: null
+dist_launcher: null
+multiprocessing_distributed: true
+distributed: false
+unused_parameters: true
+sharded_ddp: false
+ddp_backend: pytorch_ddp
+cudnn_enabled: true
+cudnn_benchmark: false
+cudnn_deterministic: true
+collect_stats: false
+write_collected_feats: false
+max_epoch: 50
+patience: null
+val_scheduler_criterion:
+- valid
+- acc
+early_stopping_criterion:
+- valid
+- loss
+- min
+best_model_criterion:
+- - valid
+ - acc
+ - max
+keep_nbest_models: 10
+nbest_averaging_interval: 0
+grad_clip: 5
+grad_clip_type: 2.0
+grad_noise: false
+accum_grad: 1
+no_forward_run: false
+resume: true
+train_dtype: float32
+use_amp: false
+log_interval: 50
+use_matplotlib: false
+use_tensorboard: true
+use_wandb: false
+wandb_project: null
+wandb_id: null
+wandb_entity: null
+wandb_name: null
+wandb_model_log_interval: -1
+use_pai: true
+detect_anomaly: false
+pretrain_path: null
+init_param: []
+ignore_init_mismatch: false
+freeze_param: []
+num_iters_per_epoch: null
+batch_size: 20
+valid_batch_size: null
+batch_bins: 10000
+valid_batch_bins: null
+train_shape_file:
+- /data/volume1/youyan/aishell/ark/train/speech_shape.1
+- /data/volume1/youyan/aishell/ark/train/text_shape.1
+valid_shape_file:
+- /data/volume1/youyan/aishell/ark/dev/speech_shape.1
+- /data/volume1/youyan/aishell/ark/dev/text_shape.1
+batch_type: length
+valid_batch_type: null
+fold_length:
+- 512
+- 150
+sort_in_batch: descending
+sort_batch: descending
+multiple_iterator: false
+chunk_length: 500
+chunk_shift_ratio: 0.5
+num_cache_chunks: 1024
+train_data_path_and_name_and_type:
+- - /data/volume1/youyan/aishell/ark/train/data.scp
+ - speech
+ - kaldi_ark
+- - /data/volume1/youyan/aishell/ark/train/data.text.1
+ - text
+ - text
+valid_data_path_and_name_and_type:
+- - /data/volume1/youyan/aishell/ark/dev/data.scp
+ - speech
+ - kaldi_ark
+- - /data/volume1/youyan/aishell/ark/dev/data.text.1
+ - text
+ - text
+allow_variable_data_keys: false
+max_cache_size: 0.0
+max_cache_fd: 32
+valid_max_cache_size: null
+optim: adam
+optim_conf:
+ lr: 0.0005
+scheduler: warmuplr
+scheduler_conf:
+ warmup_steps: 30000
+token_list:
+- '0'
+- '1'
+- '2'
+- '3'
+- '4'
+- '5'
+- '6'
+- '7'
+- '8'
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+- '57352'
+- '57360'
+- '57376'
+- '57408'
+- '57472'
+- '57600'
+- '57856'
+- '58368'
+- '59392'
+- '61440'
+init: null
+input_size: 80
+ctc_conf:
+ dropout_rate: 0.0
+ ctc_type: builtin
+ reduce: true
+ ignore_nan_grad: true
+joint_net_conf: null
+use_preprocessor: true
+token_type: char
+bpemodel: null
+non_linguistic_symbols: null
+cleaner: null
+g2p: null
+speech_volume_normalize: null
+rir_scp: null
+rir_apply_prob: 1.0
+noise_scp: null
+noise_apply_prob: 1.0
+noise_db_range: '13_15'
+frontend: null
+frontend_conf: {}
+specaug: null
+specaug_conf: {}
+normalize: null
+normalize_conf: {}
+label_aggregator: null
+label_aggregator_conf: {}
+model: sond
+model_conf:
+ # ctc_weight: 0.0
+ lsm_weight: 0.1
+ length_normalized_loss: true
+ max_spk_num: 16
+ # predictor_weight: 1.0
+ # predictor_bias: 1
+ # sampling_ratio: 0.75
+# speech encoder
+encoder: resnet34
+encoder_conf:
+ # pass by model, equal to feature dim
+ # input_size: 80
+ pooling_type: "window_shift"
+ pool_size: 20
+ stride: 1
+ tf2torch_tensor_name_prefix_torch: encoder
+ tf2torch_tensor_name_prefix_tf: EAND/speech_encoder
+speaker_encoder: conv
+speaker_encoder_conf:
+ input_units: 256
+ num_layers: 3
+ num_units: 256
+ kernel_size: 1
+ dropout_rate: 0.0
+ position_encoder: null
+ out_units: 256
+ out_norm: false
+ auxiliary_states: false
+ tf2torch_tensor_name_prefix_torch: speaker_encoder
+ tf2torch_tensor_name_prefix_tf: EAND/speaker_encoder
+ci_scorer: dot
+ci_scorer_conf: {}
+cd_scorer: san
+cd_scorer_conf:
+ input_size: 512
+ output_size: 512
+ out_units: 1
+ attention_heads: 4
+ linear_units: 1024
+ num_blocks: 4
+ dropout_rate: 0.0
+ positional_dropout_rate: 0.0
+ attention_dropout_rate: 0.0
+ # use string "null" to remove input layer
+ input_layer: "null"
+ pos_enc_class: null
+ normalize_before: true
+ tf2torch_tensor_name_prefix_torch: cd_scorer
+ tf2torch_tensor_name_prefix_tf: EAND/compute_distance_layer
+# post net
+decoder: fsmn
+decoder_conf:
+ in_units: 32
+ out_units: 2517
+ filter_size: 31
+ fsmn_num_layers: 6
+ dnn_num_layers: 1
+ num_memory_units: 512
+ ffn_inner_dim: 512
+ dropout_rate: 0.0
+ tf2torch_tensor_name_prefix_torch: decoder
+ tf2torch_tensor_name_prefix_tf: EAND/post_net
+num_worker_count: 1
+required:
+- output_dir
+- token_list
+oss_bucket: 'null'
+version: 0.1.6
diff --git a/egs/alimeeting/diarization/sond/infer_alimeeting_test.py b/egs/alimeeting/diarization/sond/infer_alimeeting_test.py
new file mode 100644
index 0000000..0988f5d
--- /dev/null
+++ b/egs/alimeeting/diarization/sond/infer_alimeeting_test.py
@@ -0,0 +1,24 @@
+from funasr.bin.diar_inference_launch import inference_launch
+import sys
+
+
+def main():
+ diar_config_path = sys.argv[1] if len(sys.argv) > 1 else "sond_fbank.yaml"
+ diar_model_path = sys.argv[2] if len(sys.argv) > 2 else "sond.pth"
+ output_dir = sys.argv[3] if len(sys.argv) > 3 else "./outputs"
+ data_path_and_name_and_type = [
+ ("data/test_rmsil/feats.scp", "speech", "kaldi_ark"),
+ ("data/test_rmsil/test_rmsil_tdnn6_xvec.scp", "profile", "kaldi_ark"),
+ ]
+ pipeline = inference_launch(
+ mode="sond",
+ diar_train_config=diar_config_path,
+ diar_model_file=diar_model_path,
+ output_dir=output_dir,
+ num_workers=1
+ )
+ pipeline(data_path_and_name_and_type)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/egs/alimeeting/diarization/sond/local/convert_label_to_rttm.py b/egs/alimeeting/diarization/sond/local/convert_label_to_rttm.py
new file mode 100644
index 0000000..880f60f
--- /dev/null
+++ b/egs/alimeeting/diarization/sond/local/convert_label_to_rttm.py
@@ -0,0 +1,132 @@
+import os
+from funasr.utils.job_runner import MultiProcessRunnerV3
+import numpy as np
+from funasr.utils.misc import load_scp_as_list, load_scp_as_dict
+from collections import OrderedDict
+from tqdm import tqdm
+from scipy.ndimage import median_filter
+
+
+class MyRunner(MultiProcessRunnerV3):
+ def prepare(self, parser):
+ parser.add_argument("label_txt", type=str)
+ parser.add_argument("map_scp", type=str)
+ parser.add_argument("out_rttm", type=str)
+ parser.add_argument("--n_spk", type=int, default=4)
+ parser.add_argument("--chunk_len", type=int, default=1600)
+ parser.add_argument("--shift_len", type=int, default=400)
+ parser.add_argument("--ignore_len", type=int, default=5)
+ parser.add_argument("--smooth_size", type=int, default=7)
+ parser.add_argument("--vote_prob", type=float, default=0.5)
+ args = parser.parse_args()
+
+ if not os.path.exists(os.path.dirname(args.out_rttm)):
+ os.makedirs(os.path.dirname(args.out_rttm))
+
+ utt2labels = load_scp_as_list(args.label_txt, 'list')
+ utt2labels = sorted(utt2labels, key=lambda x: x[0])
+ meeting2map = load_scp_as_dict(args.map_scp)
+ meeting2labels = OrderedDict()
+ for utt_id, chunk_label in utt2labels:
+ mid = utt_id.split("-")[0]
+ if mid not in meeting2labels:
+ meeting2labels[mid] = []
+ meeting2labels[mid].append(chunk_label)
+ task_list = [(mid, labels, meeting2map[mid]) for mid, labels in meeting2labels.items()]
+
+ return task_list, None, args
+
+ def post(self, result_list, args):
+ with open(args.out_rttm, "wt") as fd:
+ for results in result_list:
+ fd.writelines(results)
+
+
+def int2vec(x, vec_dim=8, dtype=np.int):
+ b = ('{:0' + str(vec_dim) + 'b}').format(x)
+ # little-endian order: lower bit first
+ return (np.array(list(b)[::-1]) == '1').astype(dtype)
+
+
+def seq2arr(seq, vec_dim=8):
+ return np.row_stack([int2vec(int(x), vec_dim) for x in seq])
+
+
+def sample2ms(sample, sr=16000):
+ return int(float(sample) / sr * 100)
+
+
+def calc_multi_labels(chunk_label_list, chunk_len, shift_len, n_spk, vote_prob=0.5):
+ n_chunk = len(chunk_label_list)
+ last_chunk_valid_frame = len(chunk_label_list[-1]) - (chunk_len - shift_len)
+ n_frame = (n_chunk - 2) * shift_len + chunk_len + last_chunk_valid_frame
+ multi_labels = np.zeros((n_frame, n_spk), dtype=float)
+ weight = np.zeros((n_frame, 1), dtype=float)
+ for i in range(n_chunk):
+ raw_label = chunk_label_list[i]
+ for k in range(len(raw_label)):
+ if raw_label[k] == '<unk>':
+ raw_label[k] = raw_label[k-1] if k > 0 else '0'
+ chunk_multi_label = seq2arr(raw_label, n_spk)
+ chunk_len = chunk_multi_label.shape[0]
+ multi_labels[i*shift_len:i*shift_len+chunk_len, :] += chunk_multi_label
+ weight[i*shift_len:i*shift_len+chunk_len, :] += 1
+ multi_labels = multi_labels / weight # normalizing vote
+ multi_labels = (multi_labels > vote_prob).astype(int) # voting results
+ return multi_labels
+
+
+def calc_spk_turns(label_arr, spk_list):
+ turn_list = []
+ length = label_arr.shape[0]
+ n_spk = label_arr.shape[1]
+ for k in range(n_spk):
+ if spk_list[k] == "None":
+ continue
+ in_utt = False
+ start = 0
+ for i in range(length):
+ if label_arr[i, k] == 1 and in_utt is False:
+ start = i
+ in_utt = True
+ if label_arr[i, k] == 0 and in_utt is True:
+ turn_list.append([spk_list[k], start, i - start])
+ in_utt = False
+ if in_utt:
+ turn_list.append([spk_list[k], start, length - start])
+ return turn_list
+
+
+def smooth_multi_labels(multi_label, win_len):
+ multi_label = median_filter(multi_label, (win_len, 1), mode="constant", cval=0.0).astype(int)
+ return multi_label
+
+
+def process(task_args):
+ _, task_list, _, args = task_args
+ spk_list = ["spk{}".format(i+1) for i in range(args.n_spk)]
+ template = "SPEAKER {} 1 {:.2f} {:.2f} <NA> <NA> {} <NA> <NA>\n"
+ results = []
+ for mid, chunk_label_list, map_file_path in tqdm(task_list, total=len(task_list), ascii=True, disable=args.no_pbar):
+ utt2map = load_scp_as_list(map_file_path, 'list')
+ multi_labels = calc_multi_labels(chunk_label_list, args.chunk_len, args.shift_len, args.n_spk, args.vote_prob)
+ multi_labels = smooth_multi_labels(multi_labels, args.smooth_size)
+ org_len = sample2ms(int(utt2map[-1][1][1]), args.sr)
+ org_multi_labels = np.zeros((org_len, args.n_spk))
+ for seg_id, [org_st, org_ed, st, ed] in utt2map:
+ org_st, org_dur = sample2ms(int(org_st), args.sr), sample2ms(int(org_ed) - int(org_st), args.sr)
+ st, dur = sample2ms(int(st), args.sr), sample2ms(int(ed) - int(st), args.sr)
+ ll = min(org_multi_labels[org_st: org_st+org_dur, :].shape[0], multi_labels[st: st+dur, :].shape[0])
+ org_multi_labels[org_st: org_st+ll, :] = multi_labels[st: st+ll, :]
+ spk_turns = calc_spk_turns(org_multi_labels, spk_list)
+ spk_turns = sorted(spk_turns, key=lambda x: x[1])
+ for spk, st, dur in spk_turns:
+ # TODO: handle the leak of segments at the change points
+ if dur > args.ignore_len:
+ results.append(template.format(mid, float(st)/100, float(dur)/100, spk))
+ return results
+
+
+if __name__ == '__main__':
+ my_runner = MyRunner(process)
+ my_runner.run()
diff --git a/egs/alimeeting/diarization/sond/path.sh b/egs/alimeeting/diarization/sond/path.sh
new file mode 100755
index 0000000..7972642
--- /dev/null
+++ b/egs/alimeeting/diarization/sond/path.sh
@@ -0,0 +1,5 @@
+export FUNASR_DIR=$PWD/../../..
+
+# NOTE(kan-bayashi): Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
+export PYTHONIOENCODING=UTF-8
+export PATH=$FUNASR_DIR/funasr/bin:$PATH
diff --git a/egs/alimeeting/diarization/sond/run.sh b/egs/alimeeting/diarization/sond/run.sh
new file mode 100644
index 0000000..7e9a7f7
--- /dev/null
+++ b/egs/alimeeting/diarization/sond/run.sh
@@ -0,0 +1,48 @@
+#!/bin/bash
+
+. ./path.sh || exit 1;
+
+stage=0
+stop_stage=2
+
+. utils/parse_options.sh || exit 1;
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+ echo "Downloading AliMeeting test set data..."
+ wget https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/alimeeting_test_data_for_sond.tar.gz
+ echo "Done. Extracting data..."
+ tar zxf alimeeting_test_data_for_sond.tar.gz
+ echo "Done."
+
+ echo "Downloading Pre-trained model..."
+ git clone https://www.modelscope.cn/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch.git
+ git clone https://www.modelscope.cn/damo/speech_diarization_sond-zh-cn-alimeeting-16k-n16k4-pytorch.git
+ ln -s speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/sv.pth ./sv.pth
+ cp speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/sv.yaml ./sv.yaml
+ ln -s speech_diarization_sond-zh-cn-alimeeting-16k-n16k4-pytorch/sond.pth ./sond.pth
+ cp speech_diarization_sond-zh-cn-alimeeting-16k-n16k4-pytorch/sond_fbank.yaml ./sond_fbank.yaml
+ cp speech_diarization_sond-zh-cn-alimeeting-16k-n16k4-pytorch/sond.yaml ./sond.yaml
+ echo "Done."
+
+ echo "Downloading dscore for scoring..."
+ git clone https://github.com/nryant/dscore.git
+fi
+
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+ echo "Calculating diarization results..."
+ python infer_alimeeting_test.py sond_fbank.yaml sond.pth outputs
+ python local/convert_label_to_rttm.py \
+ outputs/labels.txt \
+ data/test_rmsil/raw_rmsil_map.scp \
+ outputs/prediction_sm_83.rttm \
+ --ignore_len 10 --no_pbar --smooth_size 83 \
+ --vote_prob 0.5 --n_spk 16
+fi
+
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+ echo "Scoring..."
+ python dscore/score.py \
+ -r data/test_rmsil/test_org.crttm \
+ -s outputs/prediction_sm_83.rttm \
+ --collar 0.25
+fi
diff --git a/egs/alimeeting/diarization/sond/unit_test.py b/egs/alimeeting/diarization/sond/unit_test.py
new file mode 100644
index 0000000..84a4247
--- /dev/null
+++ b/egs/alimeeting/diarization/sond/unit_test.py
@@ -0,0 +1,97 @@
+from funasr.bin.diar_inference_launch import inference_launch
+import os
+
+
+def test_fbank_cpu_infer():
+ diar_config_path = "config_fbank.yaml"
+ diar_model_path = "sond.pth"
+ output_dir = "./outputs"
+ data_path_and_name_and_type = [
+ ("data/unit_test/test_feats.scp", "speech", "kaldi_ark"),
+ ("data/unit_test/test_profile.scp", "profile", "kaldi_ark"),
+ ]
+ pipeline = inference_launch(
+ mode="sond",
+ diar_train_config=diar_config_path,
+ diar_model_file=diar_model_path,
+ output_dir=output_dir,
+ num_workers=1,
+ log_level="WARNING",
+ )
+ results = pipeline(data_path_and_name_and_type)
+ print(results)
+
+
+def test_fbank_gpu_infer():
+ diar_config_path = "config_fbank.yaml"
+ diar_model_path = "sond.pth"
+ output_dir = "./outputs"
+ data_path_and_name_and_type = [
+ ("data/unit_test/test_feats.scp", "speech", "kaldi_ark"),
+ ("data/unit_test/test_profile.scp", "profile", "kaldi_ark"),
+ ]
+ pipeline = inference_launch(
+ mode="sond",
+ diar_train_config=diar_config_path,
+ diar_model_file=diar_model_path,
+ output_dir=output_dir,
+ ngpu=1,
+ num_workers=1,
+ log_level="WARNING",
+ )
+ results = pipeline(data_path_and_name_and_type)
+ print(results)
+
+
+def test_wav_gpu_infer():
+ diar_config_path = "config.yaml"
+ diar_model_path = "sond.pth"
+ output_dir = "./outputs"
+ data_path_and_name_and_type = [
+ ("data/unit_test/test_wav.scp", "speech", "sound"),
+ ("data/unit_test/test_profile.scp", "profile", "kaldi_ark"),
+ ]
+ pipeline = inference_launch(
+ mode="sond",
+ diar_train_config=diar_config_path,
+ diar_model_file=diar_model_path,
+ output_dir=output_dir,
+ ngpu=1,
+ num_workers=1,
+ log_level="WARNING",
+ )
+ results = pipeline(data_path_and_name_and_type)
+ print(results)
+
+
+def test_without_profile_gpu_infer():
+ diar_config_path = "config.yaml"
+ diar_model_path = "sond.pth"
+ output_dir = "./outputs"
+ raw_inputs = [[
+ "data/unit_test/raw_inputs/record.wav",
+ "data/unit_test/raw_inputs/spk1.wav",
+ "data/unit_test/raw_inputs/spk2.wav",
+ "data/unit_test/raw_inputs/spk3.wav",
+ "data/unit_test/raw_inputs/spk4.wav"
+ ]]
+ pipeline = inference_launch(
+ mode="sond_demo",
+ diar_train_config=diar_config_path,
+ diar_model_file=diar_model_path,
+ output_dir=output_dir,
+ ngpu=1,
+ num_workers=1,
+ log_level="WARNING",
+ param_dict={},
+ )
+ results = pipeline(raw_inputs=raw_inputs)
+ print(results)
+
+
+if __name__ == '__main__':
+ os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+ test_fbank_cpu_infer()
+ test_fbank_gpu_infer()
+ test_wav_gpu_infer()
+ test_without_profile_gpu_infer()
diff --git a/funasr/bin/diar_inference_launch.py b/funasr/bin/diar_inference_launch.py
new file mode 100755
index 0000000..c3e210b
--- /dev/null
+++ b/funasr/bin/diar_inference_launch.py
@@ -0,0 +1,179 @@
+#!/usr/bin/env python3
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
+
+import argparse
+import logging
+import os
+import sys
+from typing import Union, Dict, Any
+
+from funasr.utils import config_argparse
+from funasr.utils.cli_utils import get_commandline_args
+from funasr.utils.types import str2bool
+from funasr.utils.types import str2triple_str
+from funasr.utils.types import str_or_none
+
+
+def get_parser():
+ parser = config_argparse.ArgumentParser(
+ description="Speaker Verification",
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+ )
+
+ # Note(kamo): Use '_' instead of '-' as separator.
+ # '-' is confusing if written in yaml.
+ parser.add_argument(
+ "--log_level",
+ type=lambda x: x.upper(),
+ default="INFO",
+ choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
+ help="The verbose level of logging",
+ )
+
+ parser.add_argument("--output_dir", type=str, required=False)
+ parser.add_argument(
+ "--ngpu",
+ type=int,
+ default=0,
+ help="The number of gpus. 0 indicates CPU mode",
+ )
+ parser.add_argument(
+ "--njob",
+ type=int,
+ default=1,
+ help="The number of jobs for each gpu",
+ )
+ parser.add_argument(
+ "--gpuid_list",
+ type=str,
+ default="",
+ help="The visible gpus",
+ )
+ parser.add_argument("--seed", type=int, default=0, help="Random seed")
+ parser.add_argument(
+ "--dtype",
+ default="float32",
+ choices=["float16", "float32", "float64"],
+ help="Data type",
+ )
+ parser.add_argument(
+ "--num_workers",
+ type=int,
+ default=1,
+ help="The number of workers used for DataLoader",
+ )
+
+ group = parser.add_argument_group("Input data related")
+ group.add_argument(
+ "--data_path_and_name_and_type",
+ type=str2triple_str,
+ required=False,
+ action="append",
+ )
+ group.add_argument("--key_file", type=str_or_none)
+ group.add_argument("--allow_variable_data_keys", type=str2bool, default=True)
+
+ group = parser.add_argument_group("The model configuration related")
+ group.add_argument(
+ "--vad_infer_config",
+ type=str,
+ help="VAD infer configuration",
+ )
+ group.add_argument(
+ "--vad_model_file",
+ type=str,
+ help="VAD model parameter file",
+ )
+ group.add_argument(
+ "--diar_train_config",
+ type=str,
+ help="ASR training configuration",
+ )
+ group.add_argument(
+ "--diar_model_file",
+ type=str,
+ help="ASR model parameter file",
+ )
+ group.add_argument(
+ "--cmvn_file",
+ type=str,
+ help="Global CMVN file",
+ )
+ group.add_argument(
+ "--model_tag",
+ type=str,
+ help="Pretrained model tag. If specify this option, *_train_config and "
+ "*_file will be overwritten",
+ )
+
+ group = parser.add_argument_group("The inference configuration related")
+ group.add_argument(
+ "--batch_size",
+ type=int,
+ default=1,
+ help="The batch size for inference",
+ )
+ group.add_argument(
+ "--diar_smooth_size",
+ type=int,
+ default=121,
+ help="The smoothing size for post-processing"
+ )
+
+ return parser
+
+
+def inference_launch(mode, **kwargs):
+ if mode == "sond":
+ from funasr.bin.sond_inference import inference_modelscope
+ return inference_modelscope(**kwargs)
+ elif mode == "sond_demo":
+ from funasr.bin.sond_inference import inference_modelscope
+ param_dict = {
+ "extract_profile": True,
+ "sv_train_config": "sv.yaml",
+ "sv_model_file": "sv.pth",
+ }
+ if "param_dict" in kwargs:
+ kwargs["param_dict"].update(param_dict)
+ else:
+ kwargs["param_dict"] = param_dict
+ return inference_modelscope(**kwargs)
+ else:
+ logging.info("Unknown decoding mode: {}".format(mode))
+ return None
+
+
+def main(cmd=None):
+ print(get_commandline_args(), file=sys.stderr)
+ parser = get_parser()
+ parser.add_argument(
+ "--mode",
+ type=str,
+ default="sond",
+ help="The decoding mode",
+ )
+ args = parser.parse_args(cmd)
+ kwargs = vars(args)
+ kwargs.pop("config", None)
+
+ # set logging messages
+ logging.basicConfig(
+ level=args.log_level,
+ format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
+ )
+ logging.info("Decoding args: {}".format(kwargs))
+
+ # gpu setting
+ if args.ngpu > 0:
+ jobid = int(args.output_dir.split(".")[-1])
+ gpuid = args.gpuid_list.split(",")[(jobid - 1) // args.njob]
+ os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+ os.environ["CUDA_VISIBLE_DEVICES"] = gpuid
+
+ inference_launch(**kwargs)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/funasr/bin/sond_inference.py b/funasr/bin/sond_inference.py
new file mode 100755
index 0000000..299de0d
--- /dev/null
+++ b/funasr/bin/sond_inference.py
@@ -0,0 +1,544 @@
+#!/usr/bin/env python3
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
+
+import argparse
+import logging
+import os
+import sys
+from pathlib import Path
+from typing import Any
+from typing import List
+from typing import Optional
+from typing import Sequence
+from typing import Tuple
+from typing import Union
+
+from collections import OrderedDict
+import numpy as np
+import soundfile
+import torch
+from torch.nn import functional as F
+from typeguard import check_argument_types
+from typeguard import check_return_type
+
+from funasr.utils.cli_utils import get_commandline_args
+from funasr.tasks.diar import DiarTask
+from funasr.tasks.asr import ASRTask
+from funasr.torch_utils.device_funcs import to_device
+from funasr.torch_utils.set_all_random_seed import set_all_random_seed
+from funasr.utils import config_argparse
+from funasr.utils.types import str2bool
+from funasr.utils.types import str2triple_str
+from funasr.utils.types import str_or_none
+from scipy.ndimage import median_filter
+from funasr.utils.misc import statistic_model_parameters
+
+class Speech2Diarization:
+ """Speech2Xvector class
+
+ Examples:
+ >>> import soundfile
+ >>> import numpy as np
+ >>> speech2diar = Speech2Diarization("diar_sond_config.yml", "diar_sond.pth")
+ >>> profile = np.load("profiles.npy")
+ >>> audio, rate = soundfile.read("speech.wav")
+ >>> speech2diar(audio, profile)
+ {"spk1": [(int, int), ...], ...}
+
+ """
+
+ def __init__(
+ self,
+ diar_train_config: Union[Path, str] = None,
+ diar_model_file: Union[Path, str] = None,
+ device: str = "cpu",
+ batch_size: int = 1,
+ dtype: str = "float32",
+ streaming: bool = False,
+ smooth_size: int = 83,
+ dur_threshold: float = 10,
+ ):
+ assert check_argument_types()
+
+ # TODO: 1. Build Diarization model
+ diar_model, diar_train_args = DiarTask.build_model_from_file(
+ config_file=diar_train_config,
+ model_file=diar_model_file,
+ device=device
+ )
+ logging.info("diar_model: {}".format(diar_model))
+ logging.info("model parameter number: {}".format(statistic_model_parameters(diar_model)))
+ logging.info("diar_train_args: {}".format(diar_train_args))
+ diar_model.to(dtype=getattr(torch, dtype)).eval()
+
+ self.diar_model = diar_model
+ self.diar_train_args = diar_train_args
+ self.token_list = diar_train_args.token_list
+ self.smooth_size = smooth_size
+ self.dur_threshold = dur_threshold
+ self.device = device
+ self.dtype = dtype
+
+ def smooth_multi_labels(self, multi_label):
+ multi_label = median_filter(multi_label, (self.smooth_size, 1), mode="constant", cval=0.0).astype(int)
+ return multi_label
+
+ @staticmethod
+ def calc_spk_turns(label_arr, spk_list):
+ turn_list = []
+ length = label_arr.shape[0]
+ n_spk = label_arr.shape[1]
+ for k in range(n_spk):
+ if spk_list[k] == "None":
+ continue
+ in_utt = False
+ start = 0
+ for i in range(length):
+ if label_arr[i, k] == 1 and in_utt is False:
+ start = i
+ in_utt = True
+ if label_arr[i, k] == 0 and in_utt is True:
+ turn_list.append([spk_list[k], start, i - start])
+ in_utt = False
+ if in_utt:
+ turn_list.append([spk_list[k], start, length - start])
+ return turn_list
+
+ @staticmethod
+ def seq2arr(seq, vec_dim=8):
+ def int2vec(x, vec_dim=8, dtype=np.int):
+ b = ('{:0' + str(vec_dim) + 'b}').format(x)
+ # little-endian order: lower bit first
+ return (np.array(list(b)[::-1]) == '1').astype(dtype)
+
+ return np.row_stack([int2vec(int(x), vec_dim) for x in seq])
+
+ def post_processing(self, raw_logits: torch.Tensor, spk_num: int):
+ logits_idx = raw_logits.argmax(-1) # B, T, vocab_size -> B, T
+ # upsampling outputs to match inputs
+ ut = logits_idx.shape[1] * self.diar_model.encoder.time_ds_ratio
+ logits_idx = F.upsample(
+ logits_idx.unsqueeze(1).float(),
+ size=(ut, ),
+ mode="nearest",
+ ).squeeze(1).long()
+ logits_idx = logits_idx[0].tolist()
+ pse_labels = [self.token_list[x] for x in logits_idx]
+ multi_labels = self.seq2arr(pse_labels, spk_num)[:, :spk_num] # remove padding speakers
+ multi_labels = self.smooth_multi_labels(multi_labels)
+ spk_list = ["spk{}".format(i + 1) for i in range(spk_num)]
+ spk_turns = self.calc_spk_turns(multi_labels, spk_list)
+ results = OrderedDict()
+ for spk, st, dur in spk_turns:
+ if spk not in results:
+ results[spk] = []
+ if dur > self.dur_threshold:
+ results[spk].append((st, st+dur))
+
+ # sort segments in start time ascending
+ for spk in results:
+ results[spk] = sorted(results[spk], key=lambda x: x[0])
+
+ return results, pse_labels
+
+ @torch.no_grad()
+ def __call__(
+ self,
+ speech: Union[torch.Tensor, np.ndarray],
+ profile: Union[torch.Tensor, np.ndarray],
+ ):
+ """Inference
+
+ Args:
+ speech: Input speech data
+ profile: Speaker profiles
+ Returns:
+ diarization results for each speaker
+
+ """
+ assert check_argument_types()
+ # Input as audio signal
+ if isinstance(speech, np.ndarray):
+ speech = torch.tensor(speech)
+ if isinstance(profile, np.ndarray):
+ profile = torch.tensor(profile)
+
+ # data: (Nsamples,) -> (1, Nsamples)
+ speech = speech.unsqueeze(0).to(getattr(torch, self.dtype))
+ profile = profile.unsqueeze(0).to(getattr(torch, self.dtype))
+ # lengths: (1,)
+ speech_lengths = speech.new_full([1], dtype=torch.long, fill_value=speech.size(1))
+ profile_lengths = profile.new_full([1], dtype=torch.long, fill_value=profile.size(1))
+ batch = {"speech": speech, "speech_lengths": speech_lengths,
+ "profile": profile, "profile_lengths": profile_lengths}
+ # a. To device
+ batch = to_device(batch, device=self.device)
+
+ logits = self.diar_model.prediction_forward(**batch)
+ results, pse_labels = self.post_processing(logits, profile.shape[1])
+
+ return results, pse_labels
+
+ @staticmethod
+ def from_pretrained(
+ model_tag: Optional[str] = None,
+ **kwargs: Optional[Any],
+ ):
+ """Build Speech2Xvector instance from the pretrained model.
+
+ Args:
+ model_tag (Optional[str]): Model tag of the pretrained models.
+ Currently, the tags of espnet_model_zoo are supported.
+
+ Returns:
+ Speech2Xvector: Speech2Xvector instance.
+
+ """
+ if model_tag is not None:
+ try:
+ from espnet_model_zoo.downloader import ModelDownloader
+
+ except ImportError:
+ logging.error(
+ "`espnet_model_zoo` is not installed. "
+ "Please install via `pip install -U espnet_model_zoo`."
+ )
+ raise
+ d = ModelDownloader()
+ kwargs.update(**d.download_and_unpack(model_tag))
+
+ return Speech2Diarization(**kwargs)
+
+
+def inference_modelscope(
+ diar_train_config: str,
+ diar_model_file: str,
+ output_dir: Optional[str] = None,
+ batch_size: int = 1,
+ dtype: str = "float32",
+ ngpu: int = 0,
+ seed: int = 0,
+ num_workers: int = 0,
+ log_level: Union[int, str] = "INFO",
+ key_file: Optional[str] = None,
+ model_tag: Optional[str] = None,
+ allow_variable_data_keys: bool = True,
+ streaming: bool = False,
+ smooth_size: int = 83,
+ dur_threshold: int = 10,
+ out_format: str = "vad",
+ param_dict: Optional[dict] = None,
+ **kwargs,
+):
+ assert check_argument_types()
+ if batch_size > 1:
+ raise NotImplementedError("batch decoding is not implemented")
+ if ngpu > 1:
+ raise NotImplementedError("only single GPU decoding is supported")
+
+ logging.basicConfig(
+ level=log_level,
+ format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
+ )
+ logging.info("param_dict: {}".format(param_dict))
+
+ if ngpu >= 1 and torch.cuda.is_available():
+ device = "cuda"
+ else:
+ device = "cpu"
+
+ # 1. Set random-seed
+ set_all_random_seed(seed)
+
+ # 2a. Build speech2xvec [Optional]
+ if param_dict is not None and "extract_profile" in param_dict and param_dict["extract_profile"]:
+ assert "sv_train_config" in param_dict, "sv_train_config must be provided param_dict."
+ assert "sv_model_file" in param_dict, "sv_model_file must be provided in param_dict."
+ sv_train_config = param_dict["sv_train_config"]
+ sv_model_file = param_dict["sv_model_file"]
+ from funasr.bin.sv_inference import Speech2Xvector
+ speech2xvector_kwargs = dict(
+ sv_train_config=sv_train_config,
+ sv_model_file=sv_model_file,
+ device=device,
+ dtype=dtype,
+ streaming=streaming,
+ embedding_node="resnet1_dense"
+ )
+ logging.info("speech2xvector_kwargs: {}".format(speech2xvector_kwargs))
+ speech2xvector = Speech2Xvector.from_pretrained(
+ model_tag=model_tag,
+ **speech2xvector_kwargs,
+ )
+ speech2xvector.sv_model.eval()
+
+ # 2b. Build speech2diar
+ speech2diar_kwargs = dict(
+ diar_train_config=diar_train_config,
+ diar_model_file=diar_model_file,
+ device=device,
+ dtype=dtype,
+ streaming=streaming,
+ smooth_size=smooth_size,
+ dur_threshold=dur_threshold,
+ )
+ logging.info("speech2diarization_kwargs: {}".format(speech2diar_kwargs))
+ speech2diar = Speech2Diarization.from_pretrained(
+ model_tag=model_tag,
+ **speech2diar_kwargs,
+ )
+ speech2diar.diar_model.eval()
+
+ def output_results_str(results: dict, uttid: str):
+ rst = []
+ mid = uttid.rsplit("-", 1)[0]
+ for key in results:
+ results[key] = [(x[0]/100, x[1]/100) for x in results[key]]
+ if out_format == "vad":
+ for spk, segs in results.items():
+ rst.append("{} {}".format(spk, segs))
+ else:
+ template = "SPEAKER {} 0 {:.2f} {:.2f} <NA> <NA> {} <NA> <NA>"
+ for spk, segs in results.items():
+ rst.extend([template.format(mid, st, ed, spk) for st, ed in segs])
+
+ return "\n".join(rst)
+
+ def _forward(
+ data_path_and_name_and_type: Sequence[Tuple[str, str, str]] = None,
+ raw_inputs: List[List[Union[np.ndarray, torch.Tensor, str]]] = None,
+ output_dir_v2: Optional[str] = None,
+ param_dict: Optional[dict] = None,
+ ):
+ logging.info("param_dict: {}".format(param_dict))
+ if data_path_and_name_and_type is None and raw_inputs is not None:
+ if isinstance(raw_inputs, (list, tuple)):
+ assert all([len(example) >= 2 for example in raw_inputs]), \
+ "The length of test case in raw_inputs must larger than 1 (>=2)."
+
+ def prepare_dataset():
+ for idx, example in enumerate(raw_inputs):
+ # read waveform file
+ example = [soundfile.read(x)[0] if isinstance(example[0], str) else x
+ for x in example]
+ # convert torch tensor to numpy array
+ example = [x.numpy() if isinstance(example[0], torch.Tensor) else x
+ for x in example]
+ speech = example[0]
+ logging.info("Extracting profiles for {} waveforms".format(len(example)-1))
+ profile = [speech2xvector.calculate_embedding(x) for x in example[1:]]
+ profile = torch.cat(profile, dim=0)
+ yield ["test{}".format(idx)], {"speech": [speech], "profile": [profile]}
+
+ loader = prepare_dataset()
+ else:
+ raise TypeError("raw_inputs must be a list or tuple in [speech, profile1, profile2, ...] ")
+ else:
+ # 3. Build data-iterator
+ loader = ASRTask.build_streaming_iterator(
+ data_path_and_name_and_type,
+ dtype=dtype,
+ batch_size=batch_size,
+ key_file=key_file,
+ num_workers=num_workers,
+ preprocess_fn=None,
+ collate_fn=None,
+ allow_variable_data_keys=allow_variable_data_keys,
+ inference=True,
+ )
+
+ # 7. Start for-loop
+ output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
+ if output_path is not None:
+ os.makedirs(output_path, exist_ok=True)
+ output_writer = open("{}/result.txt".format(output_path), "w")
+ pse_label_writer = open("{}/labels.txt".format(output_path), "w")
+ logging.info("Start to diarize...")
+ result_list = []
+ for keys, batch in loader:
+ assert isinstance(batch, dict), type(batch)
+ assert all(isinstance(s, str) for s in keys), keys
+ _bs = len(next(iter(batch.values())))
+ assert len(keys) == _bs, f"{len(keys)} != {_bs}"
+ batch = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")}
+
+ results, pse_labels = speech2diar(**batch)
+ # Only supporting batch_size==1
+ key, value = keys[0], output_results_str(results, keys[0])
+ item = {"key": key, "value": value}
+ result_list.append(item)
+ if output_path is not None:
+ output_writer.write(value)
+ output_writer.flush()
+ pse_label_writer.write("{} {}\n".format(key, " ".join(pse_labels)))
+ pse_label_writer.flush()
+
+ if output_path is not None:
+ output_writer.close()
+ pse_label_writer.close()
+
+ return result_list
+
+ return _forward
+
+
+def inference(
+ data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
+ diar_train_config: Optional[str],
+ diar_model_file: Optional[str],
+ output_dir: Optional[str] = None,
+ batch_size: int = 1,
+ dtype: str = "float32",
+ ngpu: int = 0,
+ seed: int = 0,
+ num_workers: int = 1,
+ log_level: Union[int, str] = "INFO",
+ key_file: Optional[str] = None,
+ model_tag: Optional[str] = None,
+ allow_variable_data_keys: bool = True,
+ streaming: bool = False,
+ smooth_size: int = 83,
+ dur_threshold: int = 10,
+ out_format: str = "vad",
+ **kwargs,
+):
+ inference_pipeline = inference_modelscope(
+ diar_train_config=diar_train_config,
+ diar_model_file=diar_model_file,
+ output_dir=output_dir,
+ batch_size=batch_size,
+ dtype=dtype,
+ ngpu=ngpu,
+ seed=seed,
+ num_workers=num_workers,
+ log_level=log_level,
+ key_file=key_file,
+ model_tag=model_tag,
+ allow_variable_data_keys=allow_variable_data_keys,
+ streaming=streaming,
+ smooth_size=smooth_size,
+ dur_threshold=dur_threshold,
+ out_format=out_format,
+ **kwargs,
+ )
+
+ return inference_pipeline(data_path_and_name_and_type, raw_inputs=None)
+
+
+def get_parser():
+ parser = config_argparse.ArgumentParser(
+ description="Speaker verification/x-vector extraction",
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+ )
+
+ # Note(kamo): Use '_' instead of '-' as separator.
+ # '-' is confusing if written in yaml.
+ parser.add_argument(
+ "--log_level",
+ type=lambda x: x.upper(),
+ default="INFO",
+ choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
+ help="The verbose level of logging",
+ )
+
+ parser.add_argument("--output_dir", type=str, required=False)
+ parser.add_argument(
+ "--ngpu",
+ type=int,
+ default=0,
+ help="The number of gpus. 0 indicates CPU mode",
+ )
+ parser.add_argument(
+ "--gpuid_list",
+ type=str,
+ default="",
+ help="The visible gpus",
+ )
+ parser.add_argument("--seed", type=int, default=0, help="Random seed")
+ parser.add_argument(
+ "--dtype",
+ default="float32",
+ choices=["float16", "float32", "float64"],
+ help="Data type",
+ )
+ parser.add_argument(
+ "--num_workers",
+ type=int,
+ default=1,
+ help="The number of workers used for DataLoader",
+ )
+
+ group = parser.add_argument_group("Input data related")
+ group.add_argument(
+ "--data_path_and_name_and_type",
+ type=str2triple_str,
+ required=False,
+ action="append",
+ )
+ group.add_argument("--key_file", type=str_or_none)
+ group.add_argument("--allow_variable_data_keys", type=str2bool, default=False)
+
+ group = parser.add_argument_group("The model configuration related")
+ group.add_argument(
+ "--diar_train_config",
+ type=str,
+ help="diarization training configuration",
+ )
+ group.add_argument(
+ "--diar_model_file",
+ type=str,
+ help="diarization model parameter file",
+ )
+ group.add_argument(
+ "--dur_threshold",
+ type=int,
+ default=10,
+ help="The threshold for short segments in number frames"
+ )
+ parser.add_argument(
+ "--smooth_size",
+ type=int,
+ default=83,
+ help="The smoothing window length in number frames"
+ )
+ group.add_argument(
+ "--model_tag",
+ type=str,
+ help="Pretrained model tag. If specify this option, *_train_config and "
+ "*_file will be overwritten",
+ )
+ parser.add_argument(
+ "--batch_size",
+ type=int,
+ default=1,
+ help="The batch size for inference",
+ )
+ parser.add_argument("--streaming", type=str2bool, default=False)
+
+ return parser
+
+
+def main(cmd=None):
+ print(get_commandline_args(), file=sys.stderr)
+ parser = get_parser()
+ args = parser.parse_args(cmd)
+ kwargs = vars(args)
+ kwargs.pop("config", None)
+ logging.info("args: {}".format(kwargs))
+ if args.output_dir is None:
+ jobid, n_gpu = 1, 1
+ gpuid = args.gpuid_list.split(",")[jobid-1]
+ else:
+ jobid = int(args.output_dir.split(".")[-1])
+ n_gpu = len(args.gpuid_list.split(","))
+ gpuid = args.gpuid_list.split(",")[(jobid - 1) % n_gpu]
+ os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+ os.environ["CUDA_VISIBLE_DEVICES"] = gpuid
+ results_list = inference(**kwargs)
+ for results in results_list:
+ print("{} {}".format(results["key"], results["value"]))
+
+
+if __name__ == "__main__":
+ main()
diff --git a/funasr/bin/sv_inference.py b/funasr/bin/sv_inference.py
index 57ce91d..a78bccd 100755
--- a/funasr/bin/sv_inference.py
+++ b/funasr/bin/sv_inference.py
@@ -1,4 +1,7 @@
#!/usr/bin/env python3
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
+
import argparse
import logging
import os
@@ -26,7 +29,7 @@
from funasr.utils.types import str2bool
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_none
-
+from funasr.utils.misc import statistic_model_parameters
class Speech2Xvector:
"""Speech2Xvector class
@@ -59,6 +62,7 @@
device=device
)
logging.info("sv_model: {}".format(sv_model))
+ logging.info("model parameter number: {}".format(statistic_model_parameters(sv_model)))
logging.info("sv_train_args: {}".format(sv_train_args))
sv_model.to(dtype=getattr(torch, dtype)).eval()
@@ -156,17 +160,17 @@
def inference_modelscope(
- output_dir: Optional[str],
- batch_size: int,
- dtype: str,
- ngpu: int,
- seed: int,
- num_workers: int,
- log_level: Union[int, str],
- key_file: Optional[str],
- sv_train_config: Optional[str],
- sv_model_file: Optional[str],
- model_tag: Optional[str],
+ output_dir: Optional[str] = None,
+ batch_size: int = 1,
+ dtype: str = "float32",
+ ngpu: int = 1,
+ seed: int = 0,
+ num_workers: int = 0,
+ log_level: Union[int, str] = "INFO",
+ key_file: Optional[str] = None,
+ sv_train_config: Optional[str] = "sv.yaml",
+ sv_model_file: Optional[str] = "sv.pth",
+ model_tag: Optional[str] = None,
allow_variable_data_keys: bool = True,
streaming: bool = False,
embedding_node: str = "resnet1_dense",
@@ -214,7 +218,6 @@
data_path_and_name_and_type: Sequence[Tuple[str, str, str]] = None,
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
output_dir_v2: Optional[str] = None,
- fs: dict = None,
param_dict: Optional[dict] = None,
):
logging.info("param_dict: {}".format(param_dict))
diff --git a/funasr/bin/sv_inference_launch.py b/funasr/bin/sv_inference_launch.py
index c511dc7..1205d19 100755
--- a/funasr/bin/sv_inference_launch.py
+++ b/funasr/bin/sv_inference_launch.py
@@ -1,6 +1,6 @@
#!/usr/bin/env python3
-# Copyright ESPnet (https://github.com/espnet/espnet). All Rights Reserved.
-# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
import argparse
import logging
diff --git a/funasr/models/e2e_diar_sond.py b/funasr/models/e2e_diar_sond.py
new file mode 100644
index 0000000..d29ffe5
--- /dev/null
+++ b/funasr/models/e2e_diar_sond.py
@@ -0,0 +1,402 @@
+#!/usr/bin/env python3
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+# MIT License (https://opensource.org/licenses/MIT)
+
+from contextlib import contextmanager
+from distutils.version import LooseVersion
+from itertools import permutations
+from typing import Dict
+from typing import Optional
+from typing import Tuple
+
+import numpy as np
+import torch
+from torch.nn import functional as F
+from typeguard import check_argument_types
+
+from funasr.modules.nets_utils import to_device
+from funasr.modules.nets_utils import make_pad_mask
+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.models.specaug.abs_specaug import AbsSpecAug
+from funasr.layers.abs_normalize import AbsNormalize
+from funasr.torch_utils.device_funcs import force_gatherable
+from funasr.train.abs_espnet_model import AbsESPnetModel
+
+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
+
+
+class DiarSondModel(AbsESPnetModel):
+ """Speaker overlap-aware neural diarization model
+ reference: https://arxiv.org/abs/2211.10243
+ """
+
+ def __init__(
+ self,
+ vocab_size: int,
+ frontend: Optional[AbsFrontend],
+ specaug: Optional[AbsSpecAug],
+ normalize: Optional[AbsNormalize],
+ encoder: AbsEncoder,
+ speaker_encoder: AbsEncoder,
+ ci_scorer: torch.nn.Module,
+ cd_scorer: torch.nn.Module,
+ decoder: torch.nn.Module,
+ token_list: list,
+ lsm_weight: float = 0.1,
+ length_normalized_loss: bool = False,
+ max_spk_num: int = 16,
+ label_aggregator: Optional[torch.nn.Module] = None,
+ normlize_speech_speaker: bool = False,
+ ):
+ assert check_argument_types()
+
+ super().__init__()
+
+ self.encoder = encoder
+ self.speaker_encoder = speaker_encoder
+ self.ci_scorer = ci_scorer
+ self.cd_scorer = cd_scorer
+ self.normalize = normalize
+ self.frontend = frontend
+ self.specaug = specaug
+ self.label_aggregator = label_aggregator
+ self.decoder = decoder
+ self.token_list = token_list
+ self.max_spk_num = max_spk_num
+ self.normalize_speech_speaker = normlize_speech_speaker
+
+ def forward(
+ self,
+ speech: torch.Tensor,
+ speech_lengths: torch.Tensor = None,
+ profile: torch.Tensor = None,
+ profile_lengths: torch.Tensor = None,
+ spk_labels: torch.Tensor = None,
+ spk_labels_lengths: torch.Tensor = None,
+ ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
+ """Frontend + Encoder + Speaker Encoder + CI Scorer + CD Scorer + Decoder + Calc loss
+
+ Args:
+ speech: (Batch, samples)
+ speech_lengths: (Batch,) default None for chunk interator,
+ because the chunk-iterator does not
+ have the speech_lengths returned.
+ see in
+ espnet2/iterators/chunk_iter_factory.py
+ profile: (Batch, N_spk, dim)
+ profile_lengths: (Batch,)
+ spk_labels: (Batch, )
+ """
+ assert speech.shape[0] == spk_labels.shape[0], (speech.shape, spk_labels.shape)
+ batch_size = speech.shape[0]
+
+ # 1. Encoder
+ encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
+
+ if self.attractor is None:
+ # 2a. Decoder (baiscally a predction layer after encoder_out)
+ pred = self.decoder(encoder_out, encoder_out_lens)
+ else:
+ # 2b. Encoder Decoder Attractors
+ # Shuffle the chronological order of encoder_out, then calculate attractor
+ encoder_out_shuffled = encoder_out.clone()
+ for i in range(len(encoder_out_lens)):
+ encoder_out_shuffled[i, : encoder_out_lens[i], :] = encoder_out[
+ i, torch.randperm(encoder_out_lens[i]), :
+ ]
+ attractor, att_prob = self.attractor(
+ encoder_out_shuffled,
+ encoder_out_lens,
+ to_device(
+ self,
+ torch.zeros(
+ encoder_out.size(0), spk_labels.size(2) + 1, encoder_out.size(2)
+ ),
+ ),
+ )
+ # Remove the final attractor which does not correspond to a speaker
+ # Then multiply the attractors and encoder_out
+ pred = torch.bmm(encoder_out, attractor[:, :-1, :].permute(0, 2, 1))
+ # 3. Aggregate time-domain labels
+ if self.label_aggregator is not None:
+ spk_labels, spk_labels_lengths = self.label_aggregator(
+ spk_labels, spk_labels_lengths
+ )
+
+ # If encoder uses conv* as input_layer (i.e., subsampling),
+ # the sequence length of 'pred' might be slighly less than the
+ # length of 'spk_labels'. Here we force them to be equal.
+ length_diff_tolerance = 2
+ length_diff = spk_labels.shape[1] - pred.shape[1]
+ if length_diff > 0 and length_diff <= length_diff_tolerance:
+ spk_labels = spk_labels[:, 0 : pred.shape[1], :]
+
+ if self.attractor is None:
+ loss_pit, loss_att = None, None
+ loss, perm_idx, perm_list, label_perm = self.pit_loss(
+ pred, spk_labels, encoder_out_lens
+ )
+ else:
+ loss_pit, perm_idx, perm_list, label_perm = self.pit_loss(
+ pred, spk_labels, encoder_out_lens
+ )
+ loss_att = self.attractor_loss(att_prob, spk_labels)
+ loss = loss_pit + self.attractor_weight * loss_att
+ (
+ correct,
+ num_frames,
+ speech_scored,
+ speech_miss,
+ speech_falarm,
+ speaker_scored,
+ speaker_miss,
+ speaker_falarm,
+ speaker_error,
+ ) = self.calc_diarization_error(pred, label_perm, encoder_out_lens)
+
+ if speech_scored > 0 and num_frames > 0:
+ sad_mr, sad_fr, mi, fa, cf, acc, der = (
+ speech_miss / speech_scored,
+ speech_falarm / speech_scored,
+ speaker_miss / speaker_scored,
+ speaker_falarm / speaker_scored,
+ speaker_error / speaker_scored,
+ correct / num_frames,
+ (speaker_miss + speaker_falarm + speaker_error) / speaker_scored,
+ )
+ else:
+ sad_mr, sad_fr, mi, fa, cf, acc, der = 0, 0, 0, 0, 0, 0, 0
+
+ stats = dict(
+ loss=loss.detach(),
+ loss_att=loss_att.detach() if loss_att is not None else None,
+ loss_pit=loss_pit.detach() if loss_pit is not None else None,
+ sad_mr=sad_mr,
+ sad_fr=sad_fr,
+ mi=mi,
+ fa=fa,
+ cf=cf,
+ acc=acc,
+ der=der,
+ )
+
+ loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
+ return loss, stats, weight
+
+ def collect_feats(
+ self,
+ speech: torch.Tensor,
+ speech_lengths: torch.Tensor,
+ spk_labels: torch.Tensor = None,
+ spk_labels_lengths: torch.Tensor = None,
+ ) -> Dict[str, torch.Tensor]:
+ feats, feats_lengths = self._extract_feats(speech, speech_lengths)
+ return {"feats": feats, "feats_lengths": feats_lengths}
+
+ def encode_speaker(
+ self,
+ profile: torch.Tensor,
+ profile_lengths: torch.Tensor,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ with autocast(False):
+ if profile.shape[1] < self.max_spk_num:
+ profile = F.pad(profile, [0, 0, 0, self.max_spk_num-profile.shape[1], 0, 0], "constant", 0.0)
+ profile_mask = (torch.linalg.norm(profile, ord=2, dim=2, keepdim=True) > 0).float()
+ profile = F.normalize(profile, dim=2)
+ if self.speaker_encoder is not None:
+ profile = self.speaker_encoder(profile, profile_lengths)[0]
+ return profile * profile_mask, profile_lengths
+ else:
+ return profile, profile_lengths
+
+ def encode_speech(
+ self,
+ speech: torch.Tensor,
+ speech_lengths: torch.Tensor,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ if self.encoder is not None:
+ speech, speech_lengths = self.encode(speech, speech_lengths)
+ speech_mask = ~make_pad_mask(speech_lengths, maxlen=speech.shape[1])
+ speech_mask = speech_mask.to(speech.device).unsqueeze(-1).float()
+ return speech * speech_mask, speech_lengths
+ else:
+ return speech, speech_lengths
+
+ @staticmethod
+ def concate_speech_ivc(
+ speech: torch.Tensor,
+ ivc: torch.Tensor
+ ) -> torch.Tensor:
+ nn, tt = ivc.shape[1], speech.shape[1]
+ speech = speech.unsqueeze(dim=1) # B x 1 x T x D
+ speech = speech.expand(-1, nn, -1, -1) # B x N x T x D
+ ivc = ivc.unsqueeze(dim=2) # B x N x 1 x D
+ ivc = ivc.expand(-1, -1, tt, -1) # B x N x T x D
+ sd_in = torch.cat([speech, ivc], dim=3) # B x N x T x 2D
+ return sd_in
+
+ def calc_similarity(
+ self,
+ speech_encoder_outputs: torch.Tensor,
+ speaker_encoder_outputs: torch.Tensor,
+ seq_len: torch.Tensor = None,
+ spk_len: torch.Tensor = None,
+ ) -> torch.Tensor:
+ bb, tt = speech_encoder_outputs.shape[0], speech_encoder_outputs.shape[1]
+ d_sph, d_spk = speech_encoder_outputs.shape[2], speaker_encoder_outputs.shape[2]
+ if self.normalize_speech_speaker:
+ speech_encoder_outputs = F.normalize(speech_encoder_outputs, dim=2)
+ speaker_encoder_outputs = F.normalize(speaker_encoder_outputs, dim=2)
+ ge_in = self.concate_speech_ivc(speech_encoder_outputs, speaker_encoder_outputs)
+ ge_in = torch.reshape(ge_in, [bb * self.max_spk_num, tt, d_sph + d_spk])
+ ge_len = seq_len.unsqueeze(1).expand(-1, self.max_spk_num)
+ ge_len = torch.reshape(ge_len, [bb * self.max_spk_num])
+ cd_simi = self.cd_scorer(ge_in, ge_len)[0]
+ cd_simi = torch.reshape(cd_simi, [bb, self.max_spk_num, tt, 1])
+ cd_simi = cd_simi.squeeze(dim=3).permute([0, 2, 1])
+
+ if isinstance(self.ci_scorer, AbsEncoder):
+ ci_simi = self.ci_scorer(ge_in, ge_len)[0]
+ else:
+ ci_simi = self.ci_scorer(speech_encoder_outputs, speaker_encoder_outputs)
+ simi = torch.cat([cd_simi, ci_simi], dim=2)
+
+ return simi
+
+ def post_net_forward(self, simi, seq_len):
+ logits = self.decoder(simi, seq_len)[0]
+
+ return logits
+
+ def prediction_forward(
+ self,
+ speech: torch.Tensor,
+ speech_lengths: torch.Tensor,
+ profile: torch.Tensor,
+ profile_lengths: torch.Tensor,
+ ) -> torch.Tensor:
+ # speech encoding
+ speech, speech_lengths = self.encode_speech(speech, speech_lengths)
+ # speaker encoding
+ profile, profile_lengths = self.encode_speaker(profile, profile_lengths)
+ # calculating similarity
+ similarity = self.calc_similarity(speech, profile, speech_lengths, profile_lengths)
+ # post net forward
+ logits = self.post_net_forward(similarity, speech_lengths)
+
+ return logits
+
+ def encode(
+ self, speech: torch.Tensor, speech_lengths: torch.Tensor
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Frontend + Encoder
+
+ 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)
+
+ # 4. Forward encoder
+ # feats: (Batch, Length, Dim)
+ # -> encoder_out: (Batch, Length2, Dim)
+ encoder_out, encoder_out_lens, _ = self.encoder(feats, feats_lengths)
+
+ 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(),
+ )
+
+ return encoder_out, encoder_out_lens
+
+ def _extract_feats(
+ self, speech: torch.Tensor, speech_lengths: torch.Tensor
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ batch_size = speech.shape[0]
+ speech_lengths = (
+ speech_lengths
+ if speech_lengths is not None
+ else torch.ones(batch_size).int() * speech.shape[1]
+ )
+
+ assert speech_lengths.dim() == 1, speech_lengths.shape
+
+ # for data-parallel
+ speech = speech[:, : speech_lengths.max()]
+
+ if self.frontend is not None:
+ # Frontend
+ # e.g. STFT and Feature extract
+ # data_loader may send time-domain signal in this case
+ # speech (Batch, NSamples) -> feats: (Batch, NFrames, Dim)
+ feats, feats_lengths = self.frontend(speech, speech_lengths)
+ else:
+ # No frontend and no feature extract
+ feats, feats_lengths = speech, speech_lengths
+ return feats, feats_lengths
+
+ @staticmethod
+ def calc_diarization_error(pred, label, length):
+ # Note (jiatong): Credit to https://github.com/hitachi-speech/EEND
+
+ (batch_size, max_len, num_output) = label.size()
+ # mask the padding part
+ mask = np.zeros((batch_size, max_len, num_output))
+ for i in range(batch_size):
+ mask[i, : length[i], :] = 1
+
+ # pred and label have the shape (batch_size, max_len, num_output)
+ label_np = label.data.cpu().numpy().astype(int)
+ pred_np = (pred.data.cpu().numpy() > 0).astype(int)
+ label_np = label_np * mask
+ pred_np = pred_np * mask
+ length = length.data.cpu().numpy()
+
+ # compute speech activity detection error
+ n_ref = np.sum(label_np, axis=2)
+ n_sys = np.sum(pred_np, axis=2)
+ speech_scored = float(np.sum(n_ref > 0))
+ speech_miss = float(np.sum(np.logical_and(n_ref > 0, n_sys == 0)))
+ speech_falarm = float(np.sum(np.logical_and(n_ref == 0, n_sys > 0)))
+
+ # compute speaker diarization error
+ speaker_scored = float(np.sum(n_ref))
+ speaker_miss = float(np.sum(np.maximum(n_ref - n_sys, 0)))
+ speaker_falarm = float(np.sum(np.maximum(n_sys - n_ref, 0)))
+ n_map = np.sum(np.logical_and(label_np == 1, pred_np == 1), axis=2)
+ speaker_error = float(np.sum(np.minimum(n_ref, n_sys) - n_map))
+ correct = float(1.0 * np.sum((label_np == pred_np) * mask) / num_output)
+ num_frames = np.sum(length)
+ return (
+ correct,
+ num_frames,
+ speech_scored,
+ speech_miss,
+ speech_falarm,
+ speaker_scored,
+ speaker_miss,
+ speaker_falarm,
+ speaker_error,
+ )
diff --git a/funasr/models/encoder/opennmt_encoders/__init__.py b/funasr/models/encoder/opennmt_encoders/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/funasr/models/encoder/opennmt_encoders/__init__.py
diff --git a/funasr/models/encoder/opennmt_encoders/ci_scorers.py b/funasr/models/encoder/opennmt_encoders/ci_scorers.py
new file mode 100644
index 0000000..50056ee
--- /dev/null
+++ b/funasr/models/encoder/opennmt_encoders/ci_scorers.py
@@ -0,0 +1,38 @@
+import torch
+from torch.nn import functional as F
+
+
+class DotScorer(torch.nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def forward(
+ self,
+ xs_pad: torch.Tensor,
+ spk_emb: torch.Tensor,
+ ):
+ # xs_pad: B, T, D
+ # spk_emb: B, N, D
+ scores = torch.matmul(xs_pad, spk_emb.transpose(1, 2))
+ return scores
+
+ def convert_tf2torch(self, var_dict_tf, var_dict_torch):
+ return {}
+
+
+class CosScorer(torch.nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def forward(
+ self,
+ xs_pad: torch.Tensor,
+ spk_emb: torch.Tensor,
+ ):
+ # xs_pad: B, T, D
+ # spk_emb: B, N, D
+ scores = F.cosine_similarity(xs_pad.unsqueeze(2), spk_emb.unsqueeze(1), dim=-1)
+ return scores
+
+ def convert_tf2torch(self, var_dict_tf, var_dict_torch):
+ return {}
diff --git a/funasr/models/encoder/opennmt_encoders/conv_encoder.py b/funasr/models/encoder/opennmt_encoders/conv_encoder.py
new file mode 100644
index 0000000..4096743
--- /dev/null
+++ b/funasr/models/encoder/opennmt_encoders/conv_encoder.py
@@ -0,0 +1,277 @@
+from typing import List
+from typing import Optional
+from typing import Sequence
+from typing import Tuple
+from typing import Union
+import logging
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from typeguard import check_argument_types
+import numpy as np
+from funasr.modules.nets_utils import make_pad_mask
+from funasr.modules.layer_norm import LayerNorm
+from funasr.models.encoder.abs_encoder import AbsEncoder
+import math
+from funasr.modules.repeat import repeat
+
+
+class EncoderLayer(nn.Module):
+ def __init__(
+ self,
+ input_units,
+ num_units,
+ kernel_size=3,
+ activation="tanh",
+ stride=1,
+ include_batch_norm=False,
+ residual=False
+ ):
+ super().__init__()
+ left_padding = math.ceil((kernel_size - stride) / 2)
+ right_padding = kernel_size - stride - left_padding
+ self.conv_padding = nn.ConstantPad1d((left_padding, right_padding), 0.0)
+ self.conv1d = nn.Conv1d(
+ input_units,
+ num_units,
+ kernel_size,
+ stride,
+ )
+ self.activation = self.get_activation(activation)
+ if include_batch_norm:
+ self.bn = nn.BatchNorm1d(num_units, momentum=0.99, eps=1e-3)
+ self.residual = residual
+ self.include_batch_norm = include_batch_norm
+ self.input_units = input_units
+ self.num_units = num_units
+ self.stride = stride
+
+ @staticmethod
+ def get_activation(activation):
+ if activation == "tanh":
+ return nn.Tanh()
+ else:
+ return nn.ReLU()
+
+ def forward(self, xs_pad, ilens=None):
+ outputs = self.conv1d(self.conv_padding(xs_pad))
+ if self.residual and self.stride == 1 and self.input_units == self.num_units:
+ outputs = outputs + xs_pad
+
+ if self.include_batch_norm:
+ outputs = self.bn(outputs)
+
+ # add parenthesis for repeat module
+ return self.activation(outputs), ilens
+
+
+class ConvEncoder(AbsEncoder):
+ """
+ author: Speech Lab, Alibaba Group, China
+ Convolution encoder in OpenNMT framework
+ """
+
+ def __init__(
+ self,
+ num_layers,
+ input_units,
+ num_units,
+ kernel_size=3,
+ dropout_rate=0.3,
+ position_encoder=None,
+ activation='tanh',
+ auxiliary_states=True,
+ out_units=None,
+ out_norm=False,
+ out_residual=False,
+ include_batchnorm=False,
+ regularization_weight=0.0,
+ stride=1,
+ tf2torch_tensor_name_prefix_torch: str = "speaker_encoder",
+ tf2torch_tensor_name_prefix_tf: str = "EAND/speaker_encoder",
+ ):
+ assert check_argument_types()
+ super().__init__()
+ self._output_size = num_units
+
+ self.num_layers = num_layers
+ self.input_units = input_units
+ self.num_units = num_units
+ self.kernel_size = kernel_size
+ self.dropout_rate = dropout_rate
+ self.position_encoder = position_encoder
+ self.out_units = out_units
+ self.auxiliary_states = auxiliary_states
+ self.out_norm = out_norm
+ self.activation = activation
+ self.out_residual = out_residual
+ self.include_batch_norm = include_batchnorm
+ self.regularization_weight = regularization_weight
+ self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
+ self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
+ if isinstance(stride, int):
+ self.stride = [stride] * self.num_layers
+ else:
+ self.stride = stride
+ self.downsample_rate = 1
+ for s in self.stride:
+ self.downsample_rate *= s
+
+ self.dropout = nn.Dropout(dropout_rate)
+ self.cnn_a = repeat(
+ self.num_layers,
+ lambda lnum: EncoderLayer(
+ input_units if lnum == 0 else num_units,
+ num_units,
+ kernel_size,
+ activation,
+ self.stride[lnum],
+ include_batchnorm,
+ residual=True if lnum > 0 else False
+ )
+ )
+
+ if self.out_units is not None:
+ left_padding = math.ceil((kernel_size - stride) / 2)
+ right_padding = kernel_size - stride - left_padding
+ self.out_padding = nn.ConstantPad1d((left_padding, right_padding), 0.0)
+ self.conv_out = nn.Conv1d(
+ num_units,
+ num_units,
+ kernel_size,
+ )
+
+ if self.out_norm:
+ self.after_norm = LayerNorm(num_units)
+
+ def output_size(self) -> int:
+ return self.num_units
+
+ def forward(
+ self,
+ xs_pad: torch.Tensor,
+ ilens: torch.Tensor,
+ prev_states: torch.Tensor = None,
+ ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
+
+ inputs = xs_pad
+ if self.position_encoder is not None:
+ inputs = self.position_encoder(inputs)
+
+ if self.dropout_rate > 0:
+ inputs = self.dropout(inputs)
+
+ outputs, _ = self.cnn_a(inputs.transpose(1, 2), ilens)
+
+ if self.out_units is not None:
+ outputs = self.conv_out(self.out_padding(outputs))
+
+ outputs = outputs.transpose(1, 2)
+ if self.out_norm:
+ outputs = self.after_norm(outputs)
+
+ if self.out_residual:
+ outputs = outputs + inputs
+
+ return outputs, 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
+ map_dict_local = {
+ # torch: conv1d.weight in "out_channel in_channel kernel_size"
+ # tf : conv1d.weight in "kernel_size in_channel out_channel"
+ # torch: linear.weight in "out_channel in_channel"
+ # tf : dense.weight in "in_channel out_channel"
+ "{}.cnn_a.0.conv1d.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/cnn_a/conv1d/kernel".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": (2, 1, 0),
+ },
+ "{}.cnn_a.0.conv1d.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/cnn_a/conv1d/bias".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ },
+
+ "{}.cnn_a.layeridx.conv1d.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/cnn_a/conv1d_layeridx/kernel".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": (2, 1, 0),
+ },
+ "{}.cnn_a.layeridx.conv1d.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/cnn_a/conv1d_layeridx/bias".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ },
+ }
+ if self.out_units is not None:
+ # add output layer
+ map_dict_local.update({
+ "{}.conv_out.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/cnn_a/conv1d_{}/kernel".format(tensor_name_prefix_tf, self.num_layers),
+ "squeeze": None,
+ "transpose": (2, 1, 0),
+ }, # tf: (1, 256, 256) -> torch: (256, 256, 1)
+ "{}.conv_out.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/cnn_a/conv1d_{}/bias".format(tensor_name_prefix_tf, self.num_layers),
+ "squeeze": None,
+ "transpose": None,
+ }, # tf: (256,) -> torch: (256,)
+ })
+
+ return map_dict_local
+
+ def convert_tf2torch(self,
+ var_dict_tf,
+ var_dict_torch,
+ ):
+
+ map_dict = self.gen_tf2torch_map_dict()
+
+ var_dict_torch_update = dict()
+ for name in sorted(var_dict_torch.keys(), reverse=False):
+ if name.startswith(self.tf2torch_tensor_name_prefix_torch):
+ # process special (first and last) layers
+ if name in map_dict:
+ name_tf = map_dict[name]["name"]
+ data_tf = var_dict_tf[name_tf]
+ if map_dict[name]["squeeze"] is not None:
+ data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"])
+ if map_dict[name]["transpose"] is not None:
+ data_tf = np.transpose(data_tf, map_dict[name]["transpose"])
+ data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
+ assert var_dict_torch[name].size() == data_tf.size(), \
+ "{}, {}, {} != {}".format(name, name_tf,
+ var_dict_torch[name].size(), data_tf.size())
+ var_dict_torch_update[name] = data_tf
+ logging.info("torch tensor: {}, {}, loading from tf tensor: {}, {}".format(
+ name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape
+ ))
+ # process general layers
+ else:
+ # self.tf2torch_tensor_name_prefix_torch may include ".", solve this case
+ names = name.replace(self.tf2torch_tensor_name_prefix_torch, "todo").split('.')
+ layeridx = int(names[2])
+ name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
+ if name_q in map_dict.keys():
+ name_v = map_dict[name_q]["name"]
+ name_tf = name_v.replace("layeridx", "{}".format(layeridx))
+ data_tf = var_dict_tf[name_tf]
+ if map_dict[name_q]["squeeze"] is not None:
+ data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
+ if map_dict[name_q]["transpose"] is not None:
+ data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
+ data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
+ assert var_dict_torch[name].size() == data_tf.size(), \
+ "{}, {}, {} != {}".format(name, name_tf,
+ var_dict_torch[name].size(), data_tf.size())
+ var_dict_torch_update[name] = data_tf
+ logging.info("torch tensor: {}, {}, loading from tf tensor: {}, {}".format(
+ name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape
+ ))
+ else:
+ logging.warning("{} is missed from tf checkpoint".format(name))
+
+ return var_dict_torch_update
+
diff --git a/funasr/models/encoder/opennmt_encoders/fsmn_encoder.py b/funasr/models/encoder/opennmt_encoders/fsmn_encoder.py
new file mode 100644
index 0000000..e41b2aa
--- /dev/null
+++ b/funasr/models/encoder/opennmt_encoders/fsmn_encoder.py
@@ -0,0 +1,335 @@
+from typing import List
+from typing import Optional
+from typing import Sequence
+from typing import Tuple
+from typing import Union
+import logging
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from typeguard import check_argument_types
+import numpy as np
+from funasr.modules.nets_utils import make_pad_mask
+from funasr.modules.layer_norm import LayerNorm
+from funasr.models.encoder.abs_encoder import AbsEncoder
+import math
+from funasr.modules.repeat import repeat
+from funasr.modules.multi_layer_conv import FsmnFeedForward
+
+
+class FsmnBlock(torch.nn.Module):
+ def __init__(
+ self,
+ n_feat,
+ dropout_rate,
+ kernel_size,
+ fsmn_shift=0,
+ ):
+ super().__init__()
+ self.dropout = nn.Dropout(p=dropout_rate)
+ self.fsmn_block = nn.Conv1d(n_feat, n_feat, kernel_size, stride=1,
+ padding=0, groups=n_feat, bias=False)
+ # padding
+ left_padding = (kernel_size - 1) // 2
+ if fsmn_shift > 0:
+ left_padding = left_padding + fsmn_shift
+ right_padding = kernel_size - 1 - left_padding
+ self.pad_fn = nn.ConstantPad1d((left_padding, right_padding), 0.0)
+
+ def forward(self, inputs, mask, mask_shfit_chunk=None):
+ b, t, d = inputs.size()
+ if mask is not None:
+ mask = torch.reshape(mask, (b, -1, 1))
+ if mask_shfit_chunk is not None:
+ mask = mask * mask_shfit_chunk
+
+ inputs = inputs * mask
+ x = inputs.transpose(1, 2)
+ x = self.pad_fn(x)
+ x = self.fsmn_block(x)
+ x = x.transpose(1, 2)
+ x = x + inputs
+ x = self.dropout(x)
+ return x * mask
+
+
+class EncoderLayer(torch.nn.Module):
+ def __init__(
+ self,
+ in_size,
+ size,
+ feed_forward,
+ fsmn_block,
+ dropout_rate=0.0
+ ):
+ super().__init__()
+ self.in_size = in_size
+ self.size = size
+ self.ffn = feed_forward
+ self.memory = fsmn_block
+ self.dropout = nn.Dropout(dropout_rate)
+
+ def forward(
+ self,
+ xs_pad: torch.Tensor,
+ mask: torch.Tensor
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ # xs_pad in Batch, Time, Dim
+
+ context = self.ffn(xs_pad)[0]
+ memory = self.memory(context, mask)
+
+ memory = self.dropout(memory)
+ if self.in_size == self.size:
+ return memory + xs_pad, mask
+
+ return memory, mask
+
+
+class FsmnEncoder(AbsEncoder):
+ """Encoder using Fsmn
+ """
+
+ def __init__(self,
+ in_units,
+ filter_size,
+ fsmn_num_layers,
+ dnn_num_layers,
+ num_memory_units=512,
+ ffn_inner_dim=2048,
+ dropout_rate=0.0,
+ shift=0,
+ position_encoder=None,
+ sample_rate=1,
+ out_units=None,
+ tf2torch_tensor_name_prefix_torch="post_net",
+ tf2torch_tensor_name_prefix_tf="EAND/post_net"
+ ):
+ """Initializes the parameters of the encoder.
+
+ Args:
+ filter_size: the total order of memory block
+ fsmn_num_layers: The number of fsmn layers.
+ dnn_num_layers: The number of dnn layers
+ num_units: The number of memory units.
+ ffn_inner_dim: The number of units of the inner linear transformation
+ in the feed forward layer.
+ dropout_rate: The probability to drop units from the outputs.
+ shift: left padding, to control delay
+ position_encoder: The :class:`opennmt.layers.position.PositionEncoder` to
+ apply on inputs or ``None``.
+ """
+ super(FsmnEncoder, self).__init__()
+ self.in_units = in_units
+ self.filter_size = filter_size
+ self.fsmn_num_layers = fsmn_num_layers
+ self.dnn_num_layers = dnn_num_layers
+ self.num_memory_units = num_memory_units
+ self.ffn_inner_dim = ffn_inner_dim
+ self.dropout_rate = dropout_rate
+ self.shift = shift
+ if not isinstance(shift, list):
+ self.shift = [shift for _ in range(self.fsmn_num_layers)]
+ self.sample_rate = sample_rate
+ if not isinstance(sample_rate, list):
+ self.sample_rate = [sample_rate for _ in range(self.fsmn_num_layers)]
+ self.position_encoder = position_encoder
+ self.dropout = nn.Dropout(dropout_rate)
+ self.out_units = out_units
+ self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
+ self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
+
+ self.fsmn_layers = repeat(
+ self.fsmn_num_layers,
+ lambda lnum: EncoderLayer(
+ in_units if lnum == 0 else num_memory_units,
+ num_memory_units,
+ FsmnFeedForward(
+ in_units if lnum == 0 else num_memory_units,
+ ffn_inner_dim,
+ num_memory_units,
+ 1,
+ dropout_rate
+ ),
+ FsmnBlock(
+ num_memory_units,
+ dropout_rate,
+ filter_size,
+ self.shift[lnum]
+ )
+ ),
+ )
+
+ self.dnn_layers = repeat(
+ dnn_num_layers,
+ lambda lnum: FsmnFeedForward(
+ num_memory_units,
+ ffn_inner_dim,
+ num_memory_units,
+ 1,
+ dropout_rate,
+ )
+ )
+ if out_units is not None:
+ self.conv1d = nn.Conv1d(num_memory_units, out_units, 1, 1)
+
+ def output_size(self) -> int:
+ return self.num_memory_units
+
+ def forward(
+ self,
+ xs_pad: torch.Tensor,
+ ilens: torch.Tensor,
+ prev_states: torch.Tensor = None
+ ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
+ inputs = xs_pad
+ if self.position_encoder is not None:
+ inputs = self.position_encoder(inputs)
+
+ inputs = self.dropout(inputs)
+ masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
+ inputs = self.fsmn_layers(inputs, masks)[0]
+ inputs = self.dnn_layers(inputs)[0]
+
+ if self.out_units is not None:
+ inputs = self.conv1d(inputs.transpose(1, 2)).transpose(1, 2)
+
+ return inputs, 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
+ map_dict_local = {
+ # torch: conv1d.weight in "out_channel in_channel kernel_size"
+ # tf : conv1d.weight in "kernel_size in_channel out_channel"
+ # torch: linear.weight in "out_channel in_channel"
+ # tf : dense.weight in "in_channel out_channel"
+ # for fsmn_layers
+ "{}.fsmn_layers.layeridx.ffn.norm.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/fsmn_layer_layeridx/ffn/LayerNorm/beta".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.fsmn_layers.layeridx.ffn.norm.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/fsmn_layer_layeridx/ffn/LayerNorm/gamma".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.fsmn_layers.layeridx.ffn.w_1.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/fsmn_layer_layeridx/ffn/conv1d/bias".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.fsmn_layers.layeridx.ffn.w_1.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/fsmn_layer_layeridx/ffn/conv1d/kernel".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": (2, 1, 0),
+ },
+ "{}.fsmn_layers.layeridx.ffn.w_2.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/fsmn_layer_layeridx/ffn/conv1d_1/kernel".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": (2, 1, 0),
+ },
+ "{}.fsmn_layers.layeridx.memory.fsmn_block.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/fsmn_layer_layeridx/memory/depth_conv_w".format(tensor_name_prefix_tf),
+ "squeeze": 0,
+ "transpose": (1, 2, 0),
+ }, # (1, 31, 512, 1) -> (31, 512, 1) -> (512, 1, 31)
+
+ # for dnn_layers
+ "{}.dnn_layers.layeridx.norm.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/dnn_layer_layeridx/LayerNorm/beta".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.dnn_layers.layeridx.norm.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/dnn_layer_layeridx/LayerNorm/gamma".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.dnn_layers.layeridx.w_1.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/dnn_layer_layeridx/conv1d/bias".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.dnn_layers.layeridx.w_1.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/dnn_layer_layeridx/conv1d/kernel".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": (2, 1, 0),
+ },
+ "{}.dnn_layers.layeridx.w_2.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/dnn_layer_layeridx/conv1d_1/kernel".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": (2, 1, 0),
+ },
+
+ }
+ if self.out_units is not None:
+ # add output layer
+ map_dict_local.update({
+ "{}.conv1d.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/conv1d/kernel".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": (2, 1, 0),
+ },
+ "{}.conv1d.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/conv1d/bias".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ },
+ })
+
+ return map_dict_local
+
+ def convert_tf2torch(self,
+ var_dict_tf,
+ var_dict_torch,
+ ):
+
+ map_dict = self.gen_tf2torch_map_dict()
+
+ var_dict_torch_update = dict()
+ for name in sorted(var_dict_torch.keys(), reverse=False):
+ if name.startswith(self.tf2torch_tensor_name_prefix_torch):
+ # process special (first and last) layers
+ if name in map_dict:
+ name_tf = map_dict[name]["name"]
+ data_tf = var_dict_tf[name_tf]
+ if map_dict[name]["squeeze"] is not None:
+ data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"])
+ if map_dict[name]["transpose"] is not None:
+ data_tf = np.transpose(data_tf, map_dict[name]["transpose"])
+ data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
+ assert var_dict_torch[name].size() == data_tf.size(), \
+ "{}, {}, {} != {}".format(name, name_tf,
+ var_dict_torch[name].size(), data_tf.size())
+ var_dict_torch_update[name] = data_tf
+ logging.info("torch tensor: {}, {}, loading from tf tensor: {}, {}".format(
+ name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape
+ ))
+ # process general layers
+ else:
+ # self.tf2torch_tensor_name_prefix_torch may include ".", solve this case
+ names = name.replace(self.tf2torch_tensor_name_prefix_torch, "todo").split('.')
+ layeridx = int(names[2])
+ name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
+ if name_q in map_dict.keys():
+ name_v = map_dict[name_q]["name"]
+ name_tf = name_v.replace("layeridx", "{}".format(layeridx))
+ data_tf = var_dict_tf[name_tf]
+ if map_dict[name_q]["squeeze"] is not None:
+ data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
+ if map_dict[name_q]["transpose"] is not None:
+ data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
+ data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
+ assert var_dict_torch[name].size() == data_tf.size(), \
+ "{}, {}, {} != {}".format(name, name_tf,
+ var_dict_torch[name].size(), data_tf.size())
+ var_dict_torch_update[name] = data_tf
+ logging.info("torch tensor: {}, {}, loading from tf tensor: {}, {}".format(
+ name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape
+ ))
+ else:
+ logging.warning("{} is missed from tf checkpoint".format(name))
+
+ return var_dict_torch_update
diff --git a/funasr/models/encoder/opennmt_encoders/self_attention_encoder.py b/funasr/models/encoder/opennmt_encoders/self_attention_encoder.py
new file mode 100644
index 0000000..443b37a
--- /dev/null
+++ b/funasr/models/encoder/opennmt_encoders/self_attention_encoder.py
@@ -0,0 +1,480 @@
+from typing import List
+from typing import Optional
+from typing import Sequence
+from typing import Tuple
+from typing import Union
+import logging
+import torch
+import torch.nn as nn
+from funasr.modules.streaming_utils.chunk_utilis import overlap_chunk
+from typeguard import check_argument_types
+import numpy as np
+from funasr.modules.nets_utils import make_pad_mask
+from funasr.modules.attention import MultiHeadSelfAttention, MultiHeadedAttentionSANM
+from funasr.modules.embedding import SinusoidalPositionEncoder
+from funasr.modules.layer_norm import LayerNorm
+from funasr.modules.multi_layer_conv import Conv1dLinear
+from funasr.modules.multi_layer_conv import MultiLayeredConv1d
+from funasr.modules.positionwise_feed_forward import (
+ PositionwiseFeedForward, # noqa: H301
+)
+from funasr.modules.repeat import repeat
+from funasr.modules.subsampling import Conv2dSubsampling
+from funasr.modules.subsampling import Conv2dSubsampling2
+from funasr.modules.subsampling import Conv2dSubsampling6
+from funasr.modules.subsampling import Conv2dSubsampling8
+from funasr.modules.subsampling import TooShortUttError
+from funasr.modules.subsampling import check_short_utt
+from funasr.models.ctc import CTC
+from funasr.models.encoder.abs_encoder import AbsEncoder
+
+
+class EncoderLayer(nn.Module):
+ def __init__(
+ self,
+ in_size,
+ size,
+ self_attn,
+ feed_forward,
+ dropout_rate,
+ normalize_before=True,
+ concat_after=False,
+ stochastic_depth_rate=0.0,
+ ):
+ """Construct an EncoderLayer object."""
+ super(EncoderLayer, self).__init__()
+ self.self_attn = self_attn
+ self.feed_forward = feed_forward
+ self.norm1 = LayerNorm(in_size)
+ self.norm2 = LayerNorm(size)
+ self.dropout = nn.Dropout(dropout_rate)
+ self.in_size = in_size
+ self.size = size
+ self.normalize_before = normalize_before
+ self.concat_after = concat_after
+ if self.concat_after:
+ self.concat_linear = nn.Linear(size + size, size)
+ self.stochastic_depth_rate = stochastic_depth_rate
+ self.dropout_rate = dropout_rate
+
+ def forward(self, x, mask, cache=None, mask_att_chunk_encoder=None):
+ """Compute encoded features.
+
+ Args:
+ x_input (torch.Tensor): Input tensor (#batch, time, size).
+ mask (torch.Tensor): Mask tensor for the input (#batch, time).
+ cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
+
+ Returns:
+ torch.Tensor: Output tensor (#batch, time, size).
+ torch.Tensor: Mask tensor (#batch, time).
+
+ """
+ skip_layer = False
+ # with stochastic depth, residual connection `x + f(x)` becomes
+ # `x <- x + 1 / (1 - p) * f(x)` at training time.
+ stoch_layer_coeff = 1.0
+ if self.training and self.stochastic_depth_rate > 0:
+ skip_layer = torch.rand(1).item() < self.stochastic_depth_rate
+ stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate)
+
+ if skip_layer:
+ if cache is not None:
+ x = torch.cat([cache, x], dim=1)
+ return x, mask
+
+ residual = x
+ if self.normalize_before:
+ x = self.norm1(x)
+
+ if self.concat_after:
+ x_concat = torch.cat((x, self.self_attn(x, mask, mask_att_chunk_encoder=mask_att_chunk_encoder)), dim=-1)
+ if self.in_size == self.size:
+ x = residual + stoch_layer_coeff * self.concat_linear(x_concat)
+ else:
+ x = stoch_layer_coeff * self.concat_linear(x_concat)
+ else:
+ if self.in_size == self.size:
+ x = residual + stoch_layer_coeff * self.dropout(
+ self.self_attn(x, mask, mask_att_chunk_encoder=mask_att_chunk_encoder)
+ )
+ else:
+ x = stoch_layer_coeff * self.dropout(
+ self.self_attn(x, mask, mask_att_chunk_encoder=mask_att_chunk_encoder)
+ )
+ if not self.normalize_before:
+ x = self.norm1(x)
+
+ residual = x
+ if self.normalize_before:
+ x = self.norm2(x)
+ x = residual + stoch_layer_coeff * self.dropout(self.feed_forward(x))
+ if not self.normalize_before:
+ x = self.norm2(x)
+
+ return x, mask, cache, mask_att_chunk_encoder
+
+
+class SelfAttentionEncoder(AbsEncoder):
+ """
+ author: Speech Lab, Alibaba Group, China
+ Self attention encoder in OpenNMT framework
+ """
+
+ def __init__(
+ self,
+ input_size: int,
+ output_size: int = 256,
+ attention_heads: int = 4,
+ linear_units: int = 2048,
+ num_blocks: int = 6,
+ dropout_rate: float = 0.1,
+ positional_dropout_rate: float = 0.1,
+ attention_dropout_rate: float = 0.0,
+ input_layer: Optional[str] = "conv2d",
+ pos_enc_class=SinusoidalPositionEncoder,
+ normalize_before: bool = True,
+ concat_after: bool = False,
+ positionwise_layer_type: str = "linear",
+ positionwise_conv_kernel_size: int = 1,
+ padding_idx: int = -1,
+ interctc_layer_idx: List[int] = [],
+ interctc_use_conditioning: bool = False,
+ tf2torch_tensor_name_prefix_torch: str = "encoder",
+ tf2torch_tensor_name_prefix_tf: str = "seq2seq/encoder",
+ out_units=None,
+ ):
+ assert check_argument_types()
+ super().__init__()
+ self._output_size = output_size
+
+ if input_layer == "linear":
+ self.embed = torch.nn.Sequential(
+ torch.nn.Linear(input_size, output_size),
+ torch.nn.LayerNorm(output_size),
+ torch.nn.Dropout(dropout_rate),
+ torch.nn.ReLU(),
+ pos_enc_class(output_size, positional_dropout_rate),
+ )
+ elif input_layer == "conv2d":
+ self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate)
+ elif input_layer == "conv2d2":
+ self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate)
+ elif input_layer == "conv2d6":
+ self.embed = Conv2dSubsampling6(input_size, output_size, dropout_rate)
+ elif input_layer == "conv2d8":
+ self.embed = Conv2dSubsampling8(input_size, output_size, dropout_rate)
+ elif input_layer == "embed":
+ self.embed = torch.nn.Sequential(
+ torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx),
+ SinusoidalPositionEncoder(),
+ )
+ elif input_layer is None:
+ if input_size == output_size:
+ self.embed = None
+ else:
+ self.embed = torch.nn.Linear(input_size, output_size)
+ elif input_layer == "pe":
+ self.embed = SinusoidalPositionEncoder()
+ elif input_layer == "null":
+ self.embed = None
+ else:
+ raise ValueError("unknown input_layer: " + input_layer)
+ self.normalize_before = normalize_before
+ if positionwise_layer_type == "linear":
+ positionwise_layer = PositionwiseFeedForward
+ positionwise_layer_args = (
+ output_size,
+ linear_units,
+ dropout_rate,
+ )
+ elif positionwise_layer_type == "conv1d":
+ positionwise_layer = MultiLayeredConv1d
+ positionwise_layer_args = (
+ output_size,
+ linear_units,
+ positionwise_conv_kernel_size,
+ dropout_rate,
+ )
+ elif positionwise_layer_type == "conv1d-linear":
+ positionwise_layer = Conv1dLinear
+ positionwise_layer_args = (
+ output_size,
+ linear_units,
+ positionwise_conv_kernel_size,
+ dropout_rate,
+ )
+ else:
+ raise NotImplementedError("Support only linear or conv1d.")
+
+ self.encoders = repeat(
+ num_blocks,
+ lambda lnum: EncoderLayer(
+ output_size,
+ output_size,
+ MultiHeadSelfAttention(
+ attention_heads,
+ output_size,
+ output_size,
+ attention_dropout_rate,
+ ),
+ positionwise_layer(*positionwise_layer_args),
+ dropout_rate,
+ normalize_before,
+ concat_after,
+ ) if lnum > 0 else EncoderLayer(
+ input_size,
+ output_size,
+ MultiHeadSelfAttention(
+ attention_heads,
+ input_size if input_layer == "pe" or input_layer == "null" else output_size,
+ output_size,
+ attention_dropout_rate,
+ ),
+ positionwise_layer(*positionwise_layer_args),
+ dropout_rate,
+ normalize_before,
+ concat_after,
+ ),
+ )
+ if self.normalize_before:
+ self.after_norm = LayerNorm(output_size)
+
+ self.interctc_layer_idx = interctc_layer_idx
+ if len(interctc_layer_idx) > 0:
+ assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks
+ self.interctc_use_conditioning = interctc_use_conditioning
+ self.conditioning_layer = None
+ self.dropout = nn.Dropout(dropout_rate)
+ self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
+ self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
+ self.out_units = out_units
+ if out_units is not None:
+ self.output_linear = nn.Linear(output_size, out_units)
+
+ def output_size(self) -> int:
+ return self._output_size
+
+ def forward(
+ self,
+ xs_pad: torch.Tensor,
+ ilens: torch.Tensor,
+ prev_states: torch.Tensor = None,
+ ctc: CTC = None,
+ ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
+ """Embed positions in tensor.
+
+ Args:
+ xs_pad: input tensor (B, L, D)
+ ilens: input length (B)
+ prev_states: Not to be used now.
+ Returns:
+ position embedded tensor and mask
+ """
+ masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
+ xs_pad *= self.output_size()**0.5
+ if self.embed is None:
+ xs_pad = xs_pad
+ elif (
+ isinstance(self.embed, Conv2dSubsampling)
+ or isinstance(self.embed, Conv2dSubsampling2)
+ or isinstance(self.embed, Conv2dSubsampling6)
+ or isinstance(self.embed, Conv2dSubsampling8)
+ ):
+ short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1))
+ if short_status:
+ raise TooShortUttError(
+ f"has {xs_pad.size(1)} frames and is too short for subsampling "
+ + f"(it needs more than {limit_size} frames), return empty results",
+ xs_pad.size(1),
+ limit_size,
+ )
+ xs_pad, masks = self.embed(xs_pad, masks)
+ else:
+ xs_pad = self.embed(xs_pad)
+
+ xs_pad = self.dropout(xs_pad)
+ # encoder_outs = self.encoders0(xs_pad, masks)
+ # xs_pad, masks = encoder_outs[0], encoder_outs[1]
+ intermediate_outs = []
+ if len(self.interctc_layer_idx) == 0:
+ encoder_outs = self.encoders(xs_pad, masks)
+ 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, masks)
+ 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 self.out_units is not None:
+ xs_pad = self.output_linear(xs_pad)
+ olens = masks.squeeze(1).sum(1)
+ if len(intermediate_outs) > 0:
+ return (xs_pad, intermediate_outs), olens, None
+ return xs_pad, olens, 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
+ map_dict_local = {
+ # cicd
+ # torch: conv1d.weight in "out_channel in_channel kernel_size"
+ # tf : conv1d.weight in "kernel_size in_channel out_channel"
+ # torch: linear.weight in "out_channel in_channel"
+ # tf : dense.weight in "in_channel out_channel"
+ "{}.encoders.layeridx.norm1.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/layer_layeridx/multi_head/LayerNorm/gamma".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ }, # (256,),(256,)
+ "{}.encoders.layeridx.norm1.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/layer_layeridx/multi_head/LayerNorm/beta".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ }, # (256,),(256,)
+ "{}.encoders.layeridx.self_attn.linear_q_k_v.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/layer_layeridx/multi_head/conv1d/kernel".format(tensor_name_prefix_tf),
+ "squeeze": 0,
+ "transpose": (1, 0),
+ }, # (768,256),(1,256,768)
+ "{}.encoders.layeridx.self_attn.linear_q_k_v.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/layer_layeridx/multi_head/conv1d/bias".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ }, # (768,),(768,)
+ "{}.encoders.layeridx.self_attn.linear_out.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/layer_layeridx/multi_head/conv1d_1/kernel".format(tensor_name_prefix_tf),
+ "squeeze": 0,
+ "transpose": (1, 0),
+ }, # (256,256),(1,256,256)
+ "{}.encoders.layeridx.self_attn.linear_out.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/layer_layeridx/multi_head/conv1d_1/bias".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ }, # (256,),(256,)
+ # ffn
+ "{}.encoders.layeridx.norm2.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/layer_layeridx/ffn/LayerNorm/gamma".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ }, # (256,),(256,)
+ "{}.encoders.layeridx.norm2.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/layer_layeridx/ffn/LayerNorm/beta".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ }, # (256,),(256,)
+ "{}.encoders.layeridx.feed_forward.w_1.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/layer_layeridx/ffn/conv1d/kernel".format(tensor_name_prefix_tf),
+ "squeeze": 0,
+ "transpose": (1, 0),
+ }, # (1024,256),(1,256,1024)
+ "{}.encoders.layeridx.feed_forward.w_1.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/layer_layeridx/ffn/conv1d/bias".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ }, # (1024,),(1024,)
+ "{}.encoders.layeridx.feed_forward.w_2.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/layer_layeridx/ffn/conv1d_1/kernel".format(tensor_name_prefix_tf),
+ "squeeze": 0,
+ "transpose": (1, 0),
+ }, # (256,1024),(1,1024,256)
+ "{}.encoders.layeridx.feed_forward.w_2.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/layer_layeridx/ffn/conv1d_1/bias".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ }, # (256,),(256,)
+ # out norm
+ "{}.after_norm.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/LayerNorm/gamma".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ }, # (256,),(256,)
+ "{}.after_norm.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/LayerNorm/beta".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ }, # (256,),(256,)
+ }
+ if self.out_units is not None:
+ map_dict_local.update({
+ "{}.output_linear.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/conv1d/kernel".format(tensor_name_prefix_tf),
+ "squeeze": 0,
+ "transpose": (1, 0),
+ },
+ "{}.output_linear.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/conv1d/bias".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ }, # (256,),(256,)
+ })
+
+ return map_dict_local
+
+ def convert_tf2torch(self,
+ var_dict_tf,
+ var_dict_torch,
+ ):
+
+ map_dict = self.gen_tf2torch_map_dict()
+
+ var_dict_torch_update = dict()
+ for name in sorted(var_dict_torch.keys(), reverse=False):
+ if name.startswith(self.tf2torch_tensor_name_prefix_torch):
+ # process special (first and last) layers
+ if name in map_dict:
+ name_tf = map_dict[name]["name"]
+ data_tf = var_dict_tf[name_tf]
+ data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
+ if map_dict[name]["squeeze"] is not None:
+ data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"])
+ if map_dict[name]["transpose"] is not None:
+ data_tf = np.transpose(data_tf, map_dict[name]["transpose"])
+ assert var_dict_torch[name].size() == data_tf.size(), \
+ "{}, {}, {} != {}".format(name, name_tf,
+ var_dict_torch[name].size(), data_tf.size())
+ var_dict_torch_update[name] = data_tf
+ logging.info("torch tensor: {}, {}, loading from tf tensor: {}, {}".format(
+ name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape
+ ))
+ # process general layers
+ else:
+ # self.tf2torch_tensor_name_prefix_torch may include ".", solve this case
+ names = name.replace(self.tf2torch_tensor_name_prefix_torch, "todo").split('.')
+ layeridx = int(names[2])
+ name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
+ if name_q in map_dict.keys():
+ name_v = map_dict[name_q]["name"]
+ name_tf = name_v.replace("layeridx", "{}".format(layeridx))
+ data_tf = var_dict_tf[name_tf]
+ if map_dict[name_q]["squeeze"] is not None:
+ data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
+ if map_dict[name_q]["transpose"] is not None:
+ data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
+ data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
+ assert var_dict_torch[name].size() == data_tf.size(), \
+ "{}, {}, {} != {}".format(name, name_tf,
+ var_dict_torch[name].size(), data_tf.size())
+ var_dict_torch_update[name] = data_tf
+ logging.info("torch tensor: {}, {}, loading from tf tensor: {}, {}".format(
+ name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape
+ ))
+ else:
+ logging.warning("{} is missed from tf checkpoint".format(name))
+
+ return var_dict_torch_update
diff --git a/funasr/models/encoder/resnet34_encoder.py b/funasr/models/encoder/resnet34_encoder.py
index 66e446c..952ce15 100644
--- a/funasr/models/encoder/resnet34_encoder.py
+++ b/funasr/models/encoder/resnet34_encoder.py
@@ -1,7 +1,11 @@
import torch
from torch.nn import functional as F
from funasr.models.encoder.abs_encoder import AbsEncoder
-from typing import Tuple
+from typing import Tuple, Optional
+from funasr.models.pooling.statistic_pooling import statistic_pooling, windowed_statistic_pooling
+from collections import OrderedDict
+import logging
+import numpy as np
class BasicLayer(torch.nn.Module):
@@ -116,10 +120,18 @@
self.resnet0_dense = torch.nn.Conv2d(filters_in_block[-1], num_nodes_pooling_layer, 1)
self.resnet0_bn = torch.nn.BatchNorm2d(num_nodes_pooling_layer, eps=1e-3, momentum=batchnorm_momentum)
+ self.time_ds_ratio = 8
+
def output_size(self) -> int:
return self.num_nodes_pooling_layer
- def forward(self, xs_pad: torch.Tensor, ilens: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+ def forward(
+ self,
+ xs_pad: torch.Tensor,
+ ilens: torch.Tensor,
+ prev_states: torch.Tensor = None
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+
features = xs_pad
assert features.size(-1) == self.input_size, \
"Dimension of features {} doesn't match the input_size {}.".format(features.size(-1), self.input_size)
@@ -141,4 +153,463 @@
features = F.relu(features)
features = self.resnet0_bn(features)
- return features, ilens // 8
+ return features, resnet_out_lens
+
+# Note: For training, this implement is not equivalent to tf because of the kernel_regularizer in tf.layers.
+# TODO: implement kernel_regularizer in torch with munal loss addition or weigth_decay in the optimizer
+class ResNet34_SP_L2Reg(AbsEncoder):
+ def __init__(
+ self,
+ input_size,
+ use_head_conv=True,
+ batchnorm_momentum=0.5,
+ use_head_maxpool=False,
+ num_nodes_pooling_layer=256,
+ layers_in_block=(3, 4, 6, 3),
+ filters_in_block=(32, 64, 128, 256),
+ tf2torch_tensor_name_prefix_torch="encoder",
+ tf2torch_tensor_name_prefix_tf="EAND/speech_encoder",
+ tf_train_steps=720000,
+ ):
+ super(ResNet34_SP_L2Reg, self).__init__()
+
+ self.use_head_conv = use_head_conv
+ self.use_head_maxpool = use_head_maxpool
+ self.num_nodes_pooling_layer = num_nodes_pooling_layer
+ self.layers_in_block = layers_in_block
+ self.filters_in_block = filters_in_block
+ self.input_size = input_size
+ self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
+ self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
+ self.tf_train_steps = tf_train_steps
+
+ pre_filters = filters_in_block[0]
+ if use_head_conv:
+ self.pre_conv = torch.nn.Conv2d(1, pre_filters, 3, 1, 1, bias=False, padding_mode="zeros")
+ self.pre_conv_bn = torch.nn.BatchNorm2d(pre_filters, eps=1e-3, momentum=batchnorm_momentum)
+
+ if use_head_maxpool:
+ self.head_maxpool = torch.nn.MaxPool2d(3, 1, padding=1)
+
+ for i in range(len(layers_in_block)):
+ if i == 0:
+ in_filters = pre_filters if self.use_head_conv else 1
+ else:
+ in_filters = filters_in_block[i-1]
+
+ block = BasicBlock(in_filters,
+ filters=filters_in_block[i],
+ num_layer=layers_in_block[i],
+ stride=1 if i == 0 else 2,
+ bn_momentum=batchnorm_momentum)
+ self.add_module("block_{}".format(i), block)
+
+ self.resnet0_dense = torch.nn.Conv1d(filters_in_block[-1] * input_size // 8, num_nodes_pooling_layer, 1)
+ self.resnet0_bn = torch.nn.BatchNorm1d(num_nodes_pooling_layer, eps=1e-3, momentum=batchnorm_momentum)
+
+ self.time_ds_ratio = 8
+
+ def output_size(self) -> int:
+ return self.num_nodes_pooling_layer
+
+ def forward(
+ self,
+ xs_pad: torch.Tensor,
+ ilens: torch.Tensor,
+ prev_states: torch.Tensor = None
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+
+ features = xs_pad
+ assert features.size(-1) == self.input_size, \
+ "Dimension of features {} doesn't match the input_size {}.".format(features.size(-1), self.input_size)
+ features = torch.unsqueeze(features, dim=1)
+ if self.use_head_conv:
+ features = self.pre_conv(features)
+ features = self.pre_conv_bn(features)
+ features = F.relu(features)
+
+ if self.use_head_maxpool:
+ features = self.head_maxpool(features)
+
+ resnet_outs, resnet_out_lens = features, ilens
+ for i in range(len(self.layers_in_block)):
+ block = self._modules["block_{}".format(i)]
+ resnet_outs, resnet_out_lens = block(resnet_outs, resnet_out_lens)
+
+ # B, C, T, F
+ bb, cc, tt, ff = resnet_outs.shape
+ resnet_outs = torch.reshape(resnet_outs.permute(0, 3, 1, 2), [bb, ff*cc, tt])
+ features = self.resnet0_dense(resnet_outs)
+ features = F.relu(features)
+ features = self.resnet0_bn(features)
+
+ return features, resnet_out_lens
+
+ 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
+ train_steps = self.tf_train_steps
+ map_dict_local = {
+ # torch: conv1d.weight in "out_channel in_channel kernel_size"
+ # tf : conv1d.weight in "kernel_size in_channel out_channel"
+ # torch: linear.weight in "out_channel in_channel"
+ # tf : dense.weight in "in_channel out_channel"
+ "{}.pre_conv.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/pre_conv/kernel".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": (3, 2, 0, 1),
+ },
+ "{}.pre_conv_bn.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/pre_conv_bn/beta".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.pre_conv_bn.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/pre_conv_bn/gamma".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.pre_conv_bn.running_mean".format(tensor_name_prefix_torch):
+ {"name": "{}/pre_conv_bn/moving_mean".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.pre_conv_bn.running_var".format(tensor_name_prefix_torch):
+ {"name": "{}/pre_conv_bn/moving_variance".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.pre_conv_bn.num_batches_tracked".format(tensor_name_prefix_torch): train_steps
+ }
+ for layer_idx in range(3):
+ map_dict_local.update({
+ "{}.resnet{}_dense.weight".format(tensor_name_prefix_torch, layer_idx):
+ {"name": "{}/resnet{}_dense/kernel".format(tensor_name_prefix_tf, layer_idx),
+ "squeeze": None,
+ "transpose": (2, 1, 0) if layer_idx == 0 else (1, 0),
+ },
+ "{}.resnet{}_dense.bias".format(tensor_name_prefix_torch, layer_idx):
+ {"name": "{}/resnet{}_dense/bias".format(tensor_name_prefix_tf, layer_idx),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.resnet{}_bn.weight".format(tensor_name_prefix_torch, layer_idx):
+ {"name": "{}/resnet{}_bn/gamma".format(tensor_name_prefix_tf, layer_idx),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.resnet{}_bn.bias".format(tensor_name_prefix_torch, layer_idx):
+ {"name": "{}/resnet{}_bn/beta".format(tensor_name_prefix_tf, layer_idx),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.resnet{}_bn.running_mean".format(tensor_name_prefix_torch, layer_idx):
+ {"name": "{}/resnet{}_bn/moving_mean".format(tensor_name_prefix_tf, layer_idx),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.resnet{}_bn.running_var".format(tensor_name_prefix_torch, layer_idx):
+ {"name": "{}/resnet{}_bn/moving_variance".format(tensor_name_prefix_tf, layer_idx),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.resnet{}_bn.num_batches_tracked".format(tensor_name_prefix_torch, layer_idx): train_steps
+ })
+
+ for block_idx in range(len(self.layers_in_block)):
+ for layer_idx in range(self.layers_in_block[block_idx]):
+ for i in ["1", "2", "_sc"]:
+ map_dict_local.update({
+ "{}.block_{}.layer_{}.conv{}.weight".format(tensor_name_prefix_torch, block_idx, layer_idx, i):
+ {"name": "{}/block_{}/layer_{}/conv{}/kernel".format(tensor_name_prefix_tf, block_idx, layer_idx, i),
+ "squeeze": None,
+ "transpose": (3, 2, 0, 1),
+ },
+ "{}.block_{}.layer_{}.bn{}.weight".format(tensor_name_prefix_torch, block_idx, layer_idx, i):
+ {"name": "{}/block_{}/layer_{}/bn{}/gamma".format(tensor_name_prefix_tf, block_idx, layer_idx, i),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.block_{}.layer_{}.bn{}.bias".format(tensor_name_prefix_torch, block_idx, layer_idx, i):
+ {"name": "{}/block_{}/layer_{}/bn{}/beta".format(tensor_name_prefix_tf, block_idx, layer_idx, i),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.block_{}.layer_{}.bn{}.running_mean".format(tensor_name_prefix_torch, block_idx, layer_idx, i):
+ {"name": "{}/block_{}/layer_{}/bn{}/moving_mean".format(tensor_name_prefix_tf, block_idx, layer_idx, i),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.block_{}.layer_{}.bn{}.running_var".format(tensor_name_prefix_torch, block_idx, layer_idx, i):
+ {"name": "{}/block_{}/layer_{}/bn{}/moving_variance".format(tensor_name_prefix_tf, block_idx, layer_idx, i),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.block_{}.layer_{}.bn{}.num_batches_tracked".format(tensor_name_prefix_torch, block_idx, layer_idx, i): train_steps,
+ })
+
+ return map_dict_local
+
+ def convert_tf2torch(self,
+ var_dict_tf,
+ var_dict_torch,
+ ):
+
+ map_dict = self.gen_tf2torch_map_dict()
+
+ var_dict_torch_update = dict()
+ for name in sorted(var_dict_torch.keys(), reverse=False):
+ if name.startswith(self.tf2torch_tensor_name_prefix_torch):
+ if name in map_dict:
+ if "num_batches_tracked" not in name:
+ name_tf = map_dict[name]["name"]
+ data_tf = var_dict_tf[name_tf]
+ if map_dict[name]["squeeze"] is not None:
+ data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"])
+ if map_dict[name]["transpose"] is not None:
+ data_tf = np.transpose(data_tf, map_dict[name]["transpose"])
+ data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
+ assert var_dict_torch[name].size() == data_tf.size(), \
+ "{}, {}, {} != {}".format(name, name_tf,
+ var_dict_torch[name].size(), data_tf.size())
+ var_dict_torch_update[name] = data_tf
+ logging.info("torch tensor: {}, {}, loading from tf tensor: {}, {}".format(
+ name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape
+ ))
+ else:
+ var_dict_torch_update[name] = torch.Tensor(map_dict[name]).type(torch.int64).to("cpu")
+ logging.info("torch tensor: {}, manually assigning to: {}".format(
+ name, map_dict[name]
+ ))
+ else:
+ logging.warning("{} is missed from tf checkpoint".format(name))
+
+ return var_dict_torch_update
+
+
+
+class ResNet34Diar(ResNet34):
+ def __init__(
+ self,
+ input_size,
+ embedding_node="resnet1_dense",
+ use_head_conv=True,
+ batchnorm_momentum=0.5,
+ use_head_maxpool=False,
+ num_nodes_pooling_layer=256,
+ layers_in_block=(3, 4, 6, 3),
+ filters_in_block=(32, 64, 128, 256),
+ num_nodes_resnet1=256,
+ num_nodes_last_layer=256,
+ pooling_type="window_shift",
+ pool_size=20,
+ stride=1,
+ tf2torch_tensor_name_prefix_torch="encoder",
+ tf2torch_tensor_name_prefix_tf="seq2seq/speech_encoder"
+ ):
+ super(ResNet34Diar, self).__init__(
+ input_size,
+ use_head_conv=use_head_conv,
+ batchnorm_momentum=batchnorm_momentum,
+ use_head_maxpool=use_head_maxpool,
+ num_nodes_pooling_layer=num_nodes_pooling_layer,
+ layers_in_block=layers_in_block,
+ filters_in_block=filters_in_block,
+ )
+
+ self.embedding_node = embedding_node
+ self.num_nodes_resnet1 = num_nodes_resnet1
+ self.num_nodes_last_layer = num_nodes_last_layer
+ self.pooling_type = pooling_type
+ self.pool_size = pool_size
+ self.stride = stride
+ self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
+ self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
+
+ self.resnet1_dense = torch.nn.Linear(num_nodes_pooling_layer * 2, num_nodes_resnet1)
+ self.resnet1_bn = torch.nn.BatchNorm1d(num_nodes_resnet1, eps=1e-3, momentum=batchnorm_momentum)
+
+ self.resnet2_dense = torch.nn.Linear(num_nodes_resnet1, num_nodes_last_layer)
+ self.resnet2_bn = torch.nn.BatchNorm1d(num_nodes_last_layer, eps=1e-3, momentum=batchnorm_momentum)
+
+ def output_size(self) -> int:
+ if self.embedding_node.startswith("resnet1"):
+ return self.num_nodes_resnet1
+ elif self.embedding_node.startswith("resnet2"):
+ return self.num_nodes_last_layer
+
+ return self.num_nodes_pooling_layer
+
+ def forward(
+ self,
+ xs_pad: torch.Tensor,
+ ilens: torch.Tensor,
+ prev_states: torch.Tensor = None,
+ ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
+
+ endpoints = OrderedDict()
+ res_out, ilens = super().forward(xs_pad, ilens)
+ endpoints["resnet0_bn"] = res_out
+ if self.pooling_type == "frame_gsp":
+ features = statistic_pooling(res_out, ilens, (3, ))
+ else:
+ features, ilens = windowed_statistic_pooling(res_out, ilens, (2, 3), self.pool_size, self.stride)
+ features = features.transpose(1, 2)
+ endpoints["pooling"] = features
+
+ features = self.resnet1_dense(features)
+ endpoints["resnet1_dense"] = features
+ features = F.relu(features)
+ endpoints["resnet1_relu"] = features
+ features = self.resnet1_bn(features.transpose(1, 2)).transpose(1, 2)
+ endpoints["resnet1_bn"] = features
+
+ features = self.resnet2_dense(features)
+ endpoints["resnet2_dense"] = features
+ features = F.relu(features)
+ endpoints["resnet2_relu"] = features
+ features = self.resnet2_bn(features.transpose(1, 2)).transpose(1, 2)
+ endpoints["resnet2_bn"] = features
+
+ return endpoints[self.embedding_node], 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
+ train_steps = 300000
+ map_dict_local = {
+ # torch: conv1d.weight in "out_channel in_channel kernel_size"
+ # tf : conv1d.weight in "kernel_size in_channel out_channel"
+ # torch: linear.weight in "out_channel in_channel"
+ # tf : dense.weight in "in_channel out_channel"
+ "{}.pre_conv.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/pre_conv/kernel".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": (3, 2, 0, 1),
+ },
+ "{}.pre_conv_bn.bias".format(tensor_name_prefix_torch):
+ {"name": "{}/pre_conv_bn/beta".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.pre_conv_bn.weight".format(tensor_name_prefix_torch):
+ {"name": "{}/pre_conv_bn/gamma".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.pre_conv_bn.running_mean".format(tensor_name_prefix_torch):
+ {"name": "{}/pre_conv_bn/moving_mean".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.pre_conv_bn.running_var".format(tensor_name_prefix_torch):
+ {"name": "{}/pre_conv_bn/moving_variance".format(tensor_name_prefix_tf),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.pre_conv_bn.num_batches_tracked".format(tensor_name_prefix_torch): train_steps
+ }
+ for layer_idx in range(3):
+ map_dict_local.update({
+ "{}.resnet{}_dense.weight".format(tensor_name_prefix_torch, layer_idx):
+ {"name": "{}/resnet{}_dense/kernel".format(tensor_name_prefix_tf, layer_idx),
+ "squeeze": None,
+ "transpose": (3, 2, 0, 1) if layer_idx == 0 else (1, 0),
+ },
+ "{}.resnet{}_dense.bias".format(tensor_name_prefix_torch, layer_idx):
+ {"name": "{}/resnet{}_dense/bias".format(tensor_name_prefix_tf, layer_idx),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.resnet{}_bn.weight".format(tensor_name_prefix_torch, layer_idx):
+ {"name": "{}/resnet{}_bn/gamma".format(tensor_name_prefix_tf, layer_idx),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.resnet{}_bn.bias".format(tensor_name_prefix_torch, layer_idx):
+ {"name": "{}/resnet{}_bn/beta".format(tensor_name_prefix_tf, layer_idx),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.resnet{}_bn.running_mean".format(tensor_name_prefix_torch, layer_idx):
+ {"name": "{}/resnet{}_bn/moving_mean".format(tensor_name_prefix_tf, layer_idx),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.resnet{}_bn.running_var".format(tensor_name_prefix_torch, layer_idx):
+ {"name": "{}/resnet{}_bn/moving_variance".format(tensor_name_prefix_tf, layer_idx),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.resnet{}_bn.num_batches_tracked".format(tensor_name_prefix_torch, layer_idx): train_steps
+ })
+
+ for block_idx in range(len(self.layers_in_block)):
+ for layer_idx in range(self.layers_in_block[block_idx]):
+ for i in ["1", "2", "_sc"]:
+ map_dict_local.update({
+ "{}.block_{}.layer_{}.conv{}.weight".format(tensor_name_prefix_torch, block_idx, layer_idx, i):
+ {"name": "{}/block_{}/layer_{}/conv{}/kernel".format(tensor_name_prefix_tf, block_idx, layer_idx, i),
+ "squeeze": None,
+ "transpose": (3, 2, 0, 1),
+ },
+ "{}.block_{}.layer_{}.bn{}.weight".format(tensor_name_prefix_torch, block_idx, layer_idx, i):
+ {"name": "{}/block_{}/layer_{}/bn{}/gamma".format(tensor_name_prefix_tf, block_idx, layer_idx, i),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.block_{}.layer_{}.bn{}.bias".format(tensor_name_prefix_torch, block_idx, layer_idx, i):
+ {"name": "{}/block_{}/layer_{}/bn{}/beta".format(tensor_name_prefix_tf, block_idx, layer_idx, i),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.block_{}.layer_{}.bn{}.running_mean".format(tensor_name_prefix_torch, block_idx, layer_idx, i):
+ {"name": "{}/block_{}/layer_{}/bn{}/moving_mean".format(tensor_name_prefix_tf, block_idx, layer_idx, i),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.block_{}.layer_{}.bn{}.running_var".format(tensor_name_prefix_torch, block_idx, layer_idx, i):
+ {"name": "{}/block_{}/layer_{}/bn{}/moving_variance".format(tensor_name_prefix_tf, block_idx, layer_idx, i),
+ "squeeze": None,
+ "transpose": None,
+ },
+ "{}.block_{}.layer_{}.bn{}.num_batches_tracked".format(tensor_name_prefix_torch, block_idx, layer_idx, i): train_steps,
+ })
+
+ return map_dict_local
+
+ def convert_tf2torch(self,
+ var_dict_tf,
+ var_dict_torch,
+ ):
+
+ map_dict = self.gen_tf2torch_map_dict()
+
+ var_dict_torch_update = dict()
+ for name in sorted(var_dict_torch.keys(), reverse=False):
+ if name.startswith(self.tf2torch_tensor_name_prefix_torch):
+ if name in map_dict:
+ if "num_batches_tracked" not in name:
+ name_tf = map_dict[name]["name"]
+ data_tf = var_dict_tf[name_tf]
+ if map_dict[name]["squeeze"] is not None:
+ data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"])
+ if map_dict[name]["transpose"] is not None:
+ data_tf = np.transpose(data_tf, map_dict[name]["transpose"])
+ data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
+ assert var_dict_torch[name].size() == data_tf.size(), \
+ "{}, {}, {} != {}".format(name, name_tf,
+ var_dict_torch[name].size(), data_tf.size())
+ var_dict_torch_update[name] = data_tf
+ logging.info("torch tensor: {}, {}, loading from tf tensor: {}, {}".format(
+ name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape
+ ))
+ else:
+ var_dict_torch_update[name] = torch.Tensor(map_dict[name]).type(torch.int64).to("cpu")
+ logging.info("torch tensor: {}, manually assigning to: {}".format(
+ name, map_dict[name]
+ ))
+ else:
+ logging.warning("{} is missed from tf checkpoint".format(name))
+
+ return var_dict_torch_update
diff --git a/funasr/models/frontend/wav_frontend.py b/funasr/models/frontend/wav_frontend.py
index 57c5976..7a6425b 100644
--- a/funasr/models/frontend/wav_frontend.py
+++ b/funasr/models/frontend/wav_frontend.py
@@ -90,7 +90,9 @@
filter_length_max: int = -1,
lfr_m: int = 1,
lfr_n: int = 1,
- dither: float = 1.0
+ dither: float = 1.0,
+ snip_edges: bool = True,
+ upsacle_samples: bool = True,
):
assert check_argument_types()
super().__init__()
@@ -105,6 +107,8 @@
self.lfr_n = lfr_n
self.cmvn_file = cmvn_file
self.dither = dither
+ self.snip_edges = snip_edges
+ self.upsacle_samples = upsacle_samples
def output_size(self) -> int:
return self.n_mels * self.lfr_m
@@ -119,7 +123,8 @@
for i in range(batch_size):
waveform_length = input_lengths[i]
waveform = input[i][:waveform_length]
- waveform = waveform * (1 << 15)
+ if self.upsacle_samples:
+ waveform = waveform * (1 << 15)
waveform = waveform.unsqueeze(0)
mat = kaldi.fbank(waveform,
num_mel_bins=self.n_mels,
@@ -128,7 +133,8 @@
dither=self.dither,
energy_floor=0.0,
window_type=self.window,
- sample_frequency=self.fs)
+ sample_frequency=self.fs,
+ snip_edges=self.snip_edges)
if self.lfr_m != 1 or self.lfr_n != 1:
mat = apply_lfr(mat, self.lfr_m, self.lfr_n)
diff --git a/funasr/models/pooling/statistic_pooling.py b/funasr/models/pooling/statistic_pooling.py
index eeaed7d..dc8c98f 100644
--- a/funasr/models/pooling/statistic_pooling.py
+++ b/funasr/models/pooling/statistic_pooling.py
@@ -2,7 +2,10 @@
from typing import Tuple
from typing import Union
from funasr.modules.nets_utils import make_non_pad_mask
+from torch.nn import functional as F
+import math
+VAR2STD_EPSILON = 1e-12
class StatisticPooling(torch.nn.Module):
def __init__(self, pooling_dim: Union[int, Tuple] = 2, eps=1e-12):
@@ -34,3 +37,59 @@
stat_pooling = torch.cat([mean, stddev], dim=1)
return stat_pooling
+
+ def convert_tf2torch(self, var_dict_tf, var_dict_torch):
+ return {}
+
+
+def statistic_pooling(
+ xs_pad: torch.Tensor,
+ ilens: torch.Tensor = None,
+ pooling_dim: Tuple = (2, 3)
+) -> torch.Tensor:
+ # xs_pad in (Batch, Channel, Time, Frequency)
+
+ if ilens is None:
+ seq_mask = torch.ones_like(xs_pad).to(xs_pad)
+ else:
+ seq_mask = make_non_pad_mask(ilens, xs_pad, length_dim=2).to(xs_pad)
+ mean = (torch.sum(xs_pad, dim=pooling_dim, keepdim=True) /
+ torch.sum(seq_mask, dim=pooling_dim, keepdim=True))
+ squared_difference = torch.pow(xs_pad - mean, 2.0)
+ variance = (torch.sum(squared_difference, dim=pooling_dim, keepdim=True) /
+ torch.sum(seq_mask, dim=pooling_dim, keepdim=True))
+ for i in reversed(pooling_dim):
+ mean, variance = torch.squeeze(mean, dim=i), torch.squeeze(variance, dim=i)
+
+ value_mask = torch.less_equal(variance, VAR2STD_EPSILON).float()
+ variance = (1.0 - value_mask) * variance + value_mask * VAR2STD_EPSILON
+ stddev = torch.sqrt(variance)
+
+ stat_pooling = torch.cat([mean, stddev], dim=1)
+
+ return stat_pooling
+
+
+def windowed_statistic_pooling(
+ xs_pad: torch.Tensor,
+ ilens: torch.Tensor = None,
+ pooling_dim: Tuple = (2, 3),
+ pooling_size: int = 20,
+ pooling_stride: int = 1
+) -> Tuple[torch.Tensor, int]:
+ # xs_pad in (Batch, Channel, Time, Frequency)
+
+ tt = xs_pad.shape[2]
+ num_chunk = int(math.ceil(tt / pooling_stride))
+ pad = pooling_size // 2
+ features = F.pad(xs_pad, (0, 0, pad, pad), "reflect")
+ stat_list = []
+
+ for i in range(num_chunk):
+ # B x C
+ st, ed = i*pooling_stride, i*pooling_stride+pooling_size
+ stat = statistic_pooling(features[:, :, st: ed, :], pooling_dim=pooling_dim)
+ stat_list.append(stat.unsqueeze(2))
+
+ # B x C x T
+ return torch.cat(stat_list, dim=2), ilens / pooling_stride
diff --git a/funasr/modules/attention.py b/funasr/modules/attention.py
index e3ad56a..c47d96d 100644
--- a/funasr/modules/attention.py
+++ b/funasr/modules/attention.py
@@ -622,4 +622,108 @@
q_h, k_h, v_h = self.forward_qkv(x, memory)
q_h = q_h * self.d_k ** (-0.5)
scores = torch.matmul(q_h, k_h.transpose(-2, -1))
- return self.forward_attention(v_h, scores, memory_mask)
\ No newline at end of file
+ return self.forward_attention(v_h, scores, memory_mask)
+
+
+class MultiHeadSelfAttention(nn.Module):
+ """Multi-Head Attention layer.
+
+ Args:
+ n_head (int): The number of heads.
+ n_feat (int): The number of features.
+ dropout_rate (float): Dropout rate.
+
+ """
+
+ def __init__(self, n_head, in_feat, n_feat, dropout_rate):
+ """Construct an MultiHeadedAttention object."""
+ super(MultiHeadSelfAttention, self).__init__()
+ assert n_feat % n_head == 0
+ # We assume d_v always equals d_k
+ self.d_k = n_feat // n_head
+ self.h = n_head
+ self.linear_out = nn.Linear(n_feat, n_feat)
+ self.linear_q_k_v = nn.Linear(in_feat, n_feat * 3)
+ self.attn = None
+ self.dropout = nn.Dropout(p=dropout_rate)
+
+ def forward_qkv(self, x):
+ """Transform query, key and value.
+
+ Args:
+ query (torch.Tensor): Query tensor (#batch, time1, size).
+ key (torch.Tensor): Key tensor (#batch, time2, size).
+ value (torch.Tensor): Value tensor (#batch, time2, size).
+
+ Returns:
+ torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k).
+ torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k).
+ torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k).
+
+ """
+ b, t, d = x.size()
+ q_k_v = self.linear_q_k_v(x)
+ q, k, v = torch.split(q_k_v, int(self.h * self.d_k), dim=-1)
+ q_h = torch.reshape(q, (b, t, self.h, self.d_k)).transpose(1, 2) # (batch, head, time1, d_k)
+ k_h = torch.reshape(k, (b, t, self.h, self.d_k)).transpose(1, 2) # (batch, head, time2, d_k)
+ v_h = torch.reshape(v, (b, t, self.h, self.d_k)).transpose(1, 2) # (batch, head, time2, d_k)
+
+ return q_h, k_h, v_h, v
+
+ def forward_attention(self, value, scores, mask, mask_att_chunk_encoder=None):
+ """Compute attention context vector.
+
+ Args:
+ value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k).
+ scores (torch.Tensor): Attention score (#batch, n_head, time1, time2).
+ mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2).
+
+ Returns:
+ torch.Tensor: Transformed value (#batch, time1, d_model)
+ weighted by the attention score (#batch, time1, time2).
+
+ """
+ n_batch = value.size(0)
+ if mask is not None:
+ if mask_att_chunk_encoder is not None:
+ mask = mask * mask_att_chunk_encoder
+
+ mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2)
+
+ min_value = float(
+ numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min
+ )
+ scores = scores.masked_fill(mask, min_value)
+ self.attn = torch.softmax(scores, dim=-1).masked_fill(
+ mask, 0.0
+ ) # (batch, head, time1, time2)
+ else:
+ self.attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2)
+
+ p_attn = self.dropout(self.attn)
+ x = torch.matmul(p_attn, value) # (batch, head, time1, d_k)
+ x = (
+ x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)
+ ) # (batch, time1, d_model)
+
+ return self.linear_out(x) # (batch, time1, d_model)
+
+ def forward(self, x, mask, mask_att_chunk_encoder=None):
+ """Compute scaled dot product attention.
+
+ Args:
+ query (torch.Tensor): Query tensor (#batch, time1, size).
+ key (torch.Tensor): Key tensor (#batch, time2, size).
+ value (torch.Tensor): Value tensor (#batch, time2, size).
+ mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
+ (#batch, time1, time2).
+
+ Returns:
+ torch.Tensor: Output tensor (#batch, time1, d_model).
+
+ """
+ q_h, k_h, v_h, v = self.forward_qkv(x)
+ q_h = q_h * self.d_k ** (-0.5)
+ scores = torch.matmul(q_h, k_h.transpose(-2, -1))
+ att_outs = self.forward_attention(v_h, scores, mask, mask_att_chunk_encoder)
+ return att_outs
diff --git a/funasr/modules/multi_layer_conv.py b/funasr/modules/multi_layer_conv.py
index 5fb0717..9d269ab 100644
--- a/funasr/modules/multi_layer_conv.py
+++ b/funasr/modules/multi_layer_conv.py
@@ -63,6 +63,58 @@
return self.w_2(self.dropout(x).transpose(-1, 1)).transpose(-1, 1)
+class FsmnFeedForward(torch.nn.Module):
+ """Position-wise feed forward for FSMN blocks.
+
+ This is a module of multi-leyered conv1d designed
+ to replace position-wise feed-forward network
+ in FSMN block.
+ """
+
+ def __init__(self, in_chans, hidden_chans, out_chans, kernel_size, dropout_rate):
+ """Initialize FsmnFeedForward module.
+
+ Args:
+ in_chans (int): Number of input channels.
+ hidden_chans (int): Number of hidden channels.
+ out_chans (int): Number of output channels.
+ kernel_size (int): Kernel size of conv1d.
+ dropout_rate (float): Dropout rate.
+
+ """
+ super(FsmnFeedForward, self).__init__()
+ self.w_1 = torch.nn.Conv1d(
+ in_chans,
+ hidden_chans,
+ kernel_size,
+ stride=1,
+ padding=(kernel_size - 1) // 2,
+ )
+ self.w_2 = torch.nn.Conv1d(
+ hidden_chans,
+ out_chans,
+ kernel_size,
+ stride=1,
+ padding=(kernel_size - 1) // 2,
+ bias=False
+ )
+ self.norm = torch.nn.LayerNorm(hidden_chans)
+ self.dropout = torch.nn.Dropout(dropout_rate)
+
+ def forward(self, x, ilens=None):
+ """Calculate forward propagation.
+
+ Args:
+ x (torch.Tensor): Batch of input tensors (B, T, in_chans).
+
+ Returns:
+ torch.Tensor: Batch of output tensors (B, T, out_chans).
+
+ """
+ x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1)
+ return self.w_2(self.norm(self.dropout(x)).transpose(-1, 1)).transpose(-1, 1), ilens
+
+
class Conv1dLinear(torch.nn.Module):
"""Conv1D + Linear for Transformer block.
diff --git a/funasr/tasks/diar.py b/funasr/tasks/diar.py
new file mode 100644
index 0000000..f3212f1
--- /dev/null
+++ b/funasr/tasks/diar.py
@@ -0,0 +1,585 @@
+import argparse
+import logging
+import os
+from pathlib import Path
+from typing import Callable
+from typing import Collection
+from typing import Dict
+from typing import List
+from typing import Optional
+from typing import Tuple
+from typing import Union
+
+import numpy as np
+import torch
+import yaml
+from typeguard import check_argument_types
+from typeguard import check_return_type
+
+from funasr.datasets.collate_fn import CommonCollateFn
+from funasr.datasets.preprocessor import CommonPreprocessor
+from funasr.layers.abs_normalize import AbsNormalize
+from funasr.layers.global_mvn import GlobalMVN
+from funasr.layers.utterance_mvn import UtteranceMVN
+from funasr.layers.label_aggregation import LabelAggregate
+from funasr.models.ctc import CTC
+from funasr.models.encoder.resnet34_encoder import ResNet34Diar
+from funasr.models.encoder.opennmt_encoders.conv_encoder import ConvEncoder
+from funasr.models.encoder.opennmt_encoders.fsmn_encoder import FsmnEncoder
+from funasr.models.encoder.opennmt_encoders.self_attention_encoder import SelfAttentionEncoder
+from funasr.models.encoder.opennmt_encoders.ci_scorers import DotScorer, CosScorer
+from funasr.models.e2e_diar_sond import DiarSondModel
+from funasr.models.encoder.abs_encoder import AbsEncoder
+from funasr.models.encoder.conformer_encoder import ConformerEncoder
+from funasr.models.encoder.data2vec_encoder import Data2VecEncoder
+from funasr.models.encoder.rnn_encoder import RNNEncoder
+from funasr.models.encoder.sanm_encoder import SANMEncoder, SANMEncoderChunkOpt
+from funasr.models.encoder.transformer_encoder import TransformerEncoder
+from funasr.models.frontend.abs_frontend import AbsFrontend
+from funasr.models.frontend.default import DefaultFrontend
+from funasr.models.frontend.fused import FusedFrontends
+from funasr.models.frontend.s3prl import S3prlFrontend
+from funasr.models.frontend.wav_frontend import WavFrontend
+from funasr.models.frontend.windowing import SlidingWindow
+from funasr.models.postencoder.abs_postencoder import AbsPostEncoder
+from funasr.models.postencoder.hugging_face_transformers_postencoder import (
+ HuggingFaceTransformersPostEncoder, # noqa: H301
+)
+from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
+from funasr.models.preencoder.linear import LinearProjection
+from funasr.models.preencoder.sinc import LightweightSincConvs
+from funasr.models.specaug.abs_specaug import AbsSpecAug
+from funasr.models.specaug.specaug import SpecAug
+from funasr.models.specaug.specaug import SpecAugLFR
+from funasr.tasks.abs_task import AbsTask
+from funasr.torch_utils.initialize import initialize
+from funasr.train.abs_espnet_model import AbsESPnetModel
+from funasr.train.class_choices import ClassChoices
+from funasr.train.trainer import Trainer
+from funasr.utils.types import float_or_none
+from funasr.utils.types import int_or_none
+from funasr.utils.types import str2bool
+from funasr.utils.types import str_or_none
+
+frontend_choices = ClassChoices(
+ name="frontend",
+ classes=dict(
+ default=DefaultFrontend,
+ sliding_window=SlidingWindow,
+ s3prl=S3prlFrontend,
+ fused=FusedFrontends,
+ wav_frontend=WavFrontend,
+ ),
+ type_check=AbsFrontend,
+ default="default",
+)
+specaug_choices = ClassChoices(
+ name="specaug",
+ classes=dict(
+ specaug=SpecAug,
+ specaug_lfr=SpecAugLFR,
+ ),
+ type_check=AbsSpecAug,
+ default=None,
+ optional=True,
+)
+normalize_choices = ClassChoices(
+ "normalize",
+ classes=dict(
+ global_mvn=GlobalMVN,
+ utterance_mvn=UtteranceMVN,
+ ),
+ type_check=AbsNormalize,
+ default=None,
+ optional=True,
+)
+label_aggregator_choices = ClassChoices(
+ "label_aggregator",
+ classes=dict(
+ label_aggregator=LabelAggregate
+ ),
+ type_check=torch.nn.Module,
+ default=None,
+ optional=True,
+)
+model_choices = ClassChoices(
+ "model",
+ classes=dict(
+ sond=DiarSondModel,
+ ),
+ type_check=AbsESPnetModel,
+ default="sond",
+)
+encoder_choices = ClassChoices(
+ "encoder",
+ classes=dict(
+ conformer=ConformerEncoder,
+ transformer=TransformerEncoder,
+ rnn=RNNEncoder,
+ sanm=SANMEncoder,
+ san=SelfAttentionEncoder,
+ fsmn=FsmnEncoder,
+ conv=ConvEncoder,
+ resnet34=ResNet34Diar,
+ sanm_chunk_opt=SANMEncoderChunkOpt,
+ data2vec_encoder=Data2VecEncoder,
+ ),
+ type_check=AbsEncoder,
+ default="resnet34",
+)
+speaker_encoder_choices = ClassChoices(
+ "speaker_encoder",
+ classes=dict(
+ conformer=ConformerEncoder,
+ transformer=TransformerEncoder,
+ rnn=RNNEncoder,
+ sanm=SANMEncoder,
+ san=SelfAttentionEncoder,
+ fsmn=FsmnEncoder,
+ conv=ConvEncoder,
+ sanm_chunk_opt=SANMEncoderChunkOpt,
+ data2vec_encoder=Data2VecEncoder,
+ ),
+ type_check=AbsEncoder,
+ default=None,
+ optional=True
+)
+cd_scorer_choices = ClassChoices(
+ "cd_scorer",
+ classes=dict(
+ san=SelfAttentionEncoder,
+ ),
+ type_check=AbsEncoder,
+ default=None,
+ optional=True,
+)
+ci_scorer_choices = ClassChoices(
+ "ci_scorer",
+ classes=dict(
+ dot=DotScorer,
+ cosine=CosScorer,
+ ),
+ type_check=torch.nn.Module,
+ default=None,
+ optional=True,
+)
+# decoder is used for output (e.g. post_net in SOND)
+decoder_choices = ClassChoices(
+ "decoder",
+ classes=dict(
+ rnn=RNNEncoder,
+ fsmn=FsmnEncoder,
+ ),
+ type_check=torch.nn.Module,
+ default="fsmn",
+)
+
+
+class DiarTask(AbsTask):
+ # If you need more than 1 optimizer, change this value
+ num_optimizers: int = 1
+
+ # Add variable objects configurations
+ class_choices_list = [
+ # --frontend and --frontend_conf
+ frontend_choices,
+ # --specaug and --specaug_conf
+ specaug_choices,
+ # --normalize and --normalize_conf
+ normalize_choices,
+ # --model and --model_conf
+ model_choices,
+ # --encoder and --encoder_conf
+ encoder_choices,
+ # --speaker_encoder and --speaker_encoder_conf
+ speaker_encoder_choices,
+ # --cd_scorer and cd_scorer_conf
+ cd_scorer_choices,
+ # --ci_scorer and ci_scorer_conf
+ ci_scorer_choices,
+ # --decoder and --decoder_conf
+ decoder_choices,
+ ]
+
+ # If you need to modify train() or eval() procedures, change Trainer class here
+ trainer = Trainer
+
+ @classmethod
+ def add_task_arguments(cls, parser: argparse.ArgumentParser):
+ group = parser.add_argument_group(description="Task related")
+
+ # NOTE(kamo): add_arguments(..., required=True) can't be used
+ # to provide --print_config mode. Instead of it, do as
+ # required = parser.get_default("required")
+ # required += ["token_list"]
+
+ group.add_argument(
+ "--token_list",
+ type=str_or_none,
+ default=None,
+ help="A text mapping int-id to token",
+ )
+ group.add_argument(
+ "--split_with_space",
+ type=str2bool,
+ default=True,
+ help="whether to split text using <space>",
+ )
+ group.add_argument(
+ "--seg_dict_file",
+ type=str,
+ default=None,
+ help="seg_dict_file for text processing",
+ )
+ group.add_argument(
+ "--init",
+ type=lambda x: str_or_none(x.lower()),
+ default=None,
+ help="The initialization method",
+ choices=[
+ "chainer",
+ "xavier_uniform",
+ "xavier_normal",
+ "kaiming_uniform",
+ "kaiming_normal",
+ None,
+ ],
+ )
+
+ group.add_argument(
+ "--input_size",
+ type=int_or_none,
+ default=None,
+ help="The number of input dimension of the feature",
+ )
+
+ group = parser.add_argument_group(description="Preprocess related")
+ group.add_argument(
+ "--use_preprocessor",
+ type=str2bool,
+ default=True,
+ help="Apply preprocessing to data or not",
+ )
+ group.add_argument(
+ "--token_type",
+ type=str,
+ default="char",
+ choices=["char"],
+ help="The text will be tokenized in the specified level token",
+ )
+ parser.add_argument(
+ "--speech_volume_normalize",
+ type=float_or_none,
+ default=None,
+ help="Scale the maximum amplitude to the given value.",
+ )
+ parser.add_argument(
+ "--rir_scp",
+ type=str_or_none,
+ default=None,
+ help="The file path of rir scp file.",
+ )
+ parser.add_argument(
+ "--rir_apply_prob",
+ type=float,
+ default=1.0,
+ help="THe probability for applying RIR convolution.",
+ )
+ parser.add_argument(
+ "--cmvn_file",
+ type=str_or_none,
+ default=None,
+ help="The file path of noise scp file.",
+ )
+ parser.add_argument(
+ "--noise_scp",
+ type=str_or_none,
+ default=None,
+ help="The file path of noise scp file.",
+ )
+ parser.add_argument(
+ "--noise_apply_prob",
+ type=float,
+ default=1.0,
+ help="The probability applying Noise adding.",
+ )
+ parser.add_argument(
+ "--noise_db_range",
+ type=str,
+ default="13_15",
+ help="The range of noise decibel level.",
+ )
+
+ for class_choices in cls.class_choices_list:
+ # Append --<name> and --<name>_conf.
+ # e.g. --encoder and --encoder_conf
+ class_choices.add_arguments(group)
+
+ @classmethod
+ def build_collate_fn(
+ cls, args: argparse.Namespace, train: bool
+ ) -> Callable[
+ [Collection[Tuple[str, Dict[str, np.ndarray]]]],
+ Tuple[List[str], Dict[str, torch.Tensor]],
+ ]:
+ assert check_argument_types()
+ # NOTE(kamo): int value = 0 is reserved by CTC-blank symbol
+ return CommonCollateFn(float_pad_value=0.0, int_pad_value=-1)
+
+ @classmethod
+ def build_preprocess_fn(
+ cls, args: argparse.Namespace, train: bool
+ ) -> Optional[Callable[[str, Dict[str, np.array]], Dict[str, np.ndarray]]]:
+ assert check_argument_types()
+ if args.use_preprocessor:
+ retval = CommonPreprocessor(
+ train=train,
+ token_type=args.token_type,
+ token_list=args.token_list,
+ bpemodel=None,
+ non_linguistic_symbols=None,
+ text_cleaner=None,
+ g2p_type=None,
+ split_with_space=args.split_with_space if hasattr(args, "split_with_space") else False,
+ seg_dict_file=args.seg_dict_file if hasattr(args, "seg_dict_file") else None,
+ # NOTE(kamo): Check attribute existence for backward compatibility
+ rir_scp=args.rir_scp if hasattr(args, "rir_scp") else None,
+ rir_apply_prob=args.rir_apply_prob
+ if hasattr(args, "rir_apply_prob")
+ else 1.0,
+ noise_scp=args.noise_scp if hasattr(args, "noise_scp") else None,
+ noise_apply_prob=args.noise_apply_prob
+ if hasattr(args, "noise_apply_prob")
+ else 1.0,
+ noise_db_range=args.noise_db_range
+ if hasattr(args, "noise_db_range")
+ else "13_15",
+ speech_volume_normalize=args.speech_volume_normalize
+ if hasattr(args, "rir_scp")
+ else None,
+ )
+ else:
+ retval = None
+ assert check_return_type(retval)
+ return retval
+
+ @classmethod
+ def required_data_names(
+ cls, train: bool = True, inference: bool = False
+ ) -> Tuple[str, ...]:
+ if not inference:
+ retval = ("speech", "profile", "label")
+ else:
+ # Recognition mode
+ retval = ("speech", "profile")
+ return retval
+
+ @classmethod
+ def optional_data_names(
+ cls, train: bool = True, inference: bool = False
+ ) -> Tuple[str, ...]:
+ retval = ()
+ assert check_return_type(retval)
+ return retval
+
+ @classmethod
+ def build_model(cls, args: argparse.Namespace):
+ assert check_argument_types()
+ if isinstance(args.token_list, str):
+ with open(args.token_list, encoding="utf-8") as f:
+ token_list = [line.rstrip() for line in f]
+
+ # Overwriting token_list to keep it as "portable".
+ args.token_list = list(token_list)
+ elif isinstance(args.token_list, (tuple, list)):
+ token_list = list(args.token_list)
+ else:
+ raise RuntimeError("token_list must be str or list")
+ vocab_size = len(token_list)
+ logging.info(f"Vocabulary size: {vocab_size}")
+
+ # 1. frontend
+ if args.input_size is None:
+ # Extract features in the model
+ frontend_class = frontend_choices.get_class(args.frontend)
+ if args.frontend == 'wav_frontend':
+ frontend = frontend_class(cmvn_file=args.cmvn_file, **args.frontend_conf)
+ else:
+ frontend = frontend_class(**args.frontend_conf)
+ input_size = frontend.output_size()
+ else:
+ # Give features from data-loader
+ args.frontend = None
+ args.frontend_conf = {}
+ frontend = None
+ input_size = args.input_size
+
+ # 2. Data augmentation for spectrogram
+ if args.specaug is not None:
+ specaug_class = specaug_choices.get_class(args.specaug)
+ specaug = specaug_class(**args.specaug_conf)
+ else:
+ specaug = None
+
+ # 3. Normalization layer
+ if args.normalize is not None:
+ normalize_class = normalize_choices.get_class(args.normalize)
+ normalize = normalize_class(**args.normalize_conf)
+ else:
+ normalize = None
+
+ # 4. Encoder
+ encoder_class = encoder_choices.get_class(args.encoder)
+ encoder = encoder_class(input_size=input_size, **args.encoder_conf)
+
+ # 5. speaker encoder
+ if getattr(args, "speaker_encoder", None) is not None:
+ speaker_encoder_class = speaker_encoder_choices.get_class(args.speaker_encoder)
+ speaker_encoder = speaker_encoder_class(**args.speaker_encoder_conf)
+ else:
+ speaker_encoder = None
+
+ # 6. CI & CD scorer
+ if getattr(args, "ci_scorer", None) is not None:
+ ci_scorer_class = ci_scorer_choices.get_class(args.ci_scorer)
+ ci_scorer = ci_scorer_class(**args.ci_scorer_conf)
+ else:
+ ci_scorer = None
+
+ if getattr(args, "cd_scorer", None) is not None:
+ cd_scorer_class = cd_scorer_choices.get_class(args.cd_scorer)
+ cd_scorer = cd_scorer_class(**args.cd_scorer_conf)
+ else:
+ cd_scorer = None
+
+ # 7. Decoder
+ decoder_class = decoder_choices.get_class(args.decoder)
+ decoder = decoder_class(**args.decoder_conf)
+
+ if getattr(args, "label_aggregator", None) is not None:
+ label_aggregator_class = label_aggregator_choices.get_class(args.label_aggregator)
+ label_aggregator = label_aggregator_class(**args.label_aggregator_conf)
+ else:
+ label_aggregator = None
+
+ # 9. Build model
+ model_class = model_choices.get_class(args.model)
+ model = model_class(
+ vocab_size=vocab_size,
+ frontend=frontend,
+ specaug=specaug,
+ normalize=normalize,
+ label_aggregator=label_aggregator,
+ encoder=encoder,
+ speaker_encoder=speaker_encoder,
+ ci_scorer=ci_scorer,
+ cd_scorer=cd_scorer,
+ decoder=decoder,
+ token_list=token_list,
+ **args.model_conf,
+ )
+
+ # 10. Initialize
+ if args.init is not None:
+ initialize(model, args.init)
+
+ assert check_return_type(model)
+ return model
+
+ # ~~~~~~~~~ The methods below are mainly used for inference ~~~~~~~~~
+ @classmethod
+ def build_model_from_file(
+ cls,
+ config_file: Union[Path, str] = None,
+ model_file: Union[Path, str] = None,
+ cmvn_file: Union[Path, str] = None,
+ device: str = "cpu",
+ ):
+ """Build model from the files.
+
+ This method is used for inference or fine-tuning.
+
+ Args:
+ config_file: The yaml file saved when training.
+ model_file: The model file saved when training.
+ cmvn_file: The cmvn file for front-end
+ device: Device type, "cpu", "cuda", or "cuda:N".
+
+ """
+ assert check_argument_types()
+ if config_file is None:
+ assert model_file is not None, (
+ "The argument 'model_file' must be provided "
+ "if the argument 'config_file' is not specified."
+ )
+ config_file = Path(model_file).parent / "config.yaml"
+ else:
+ config_file = Path(config_file)
+
+ with config_file.open("r", encoding="utf-8") as f:
+ args = yaml.safe_load(f)
+ if cmvn_file is not None:
+ args["cmvn_file"] = cmvn_file
+ args = argparse.Namespace(**args)
+ model = cls.build_model(args)
+ if not isinstance(model, AbsESPnetModel):
+ raise RuntimeError(
+ f"model must inherit {AbsESPnetModel.__name__}, but got {type(model)}"
+ )
+ model.to(device)
+ model_dict = dict()
+ model_name_pth = None
+ if model_file is not None:
+ logging.info("model_file is {}".format(model_file))
+ if device == "cuda":
+ device = f"cuda:{torch.cuda.current_device()}"
+ model_dir = os.path.dirname(model_file)
+ model_name = os.path.basename(model_file)
+ if "model.ckpt-" in model_name or ".bin" in model_name:
+ if ".bin" in model_name:
+ model_name_pth = os.path.join(model_dir, model_name.replace('.bin', '.pb'))
+ else:
+ model_name_pth = os.path.join(model_dir, "{}.pth".format(model_name))
+ if os.path.exists(model_name_pth):
+ logging.info("model_file is load from pth: {}".format(model_name_pth))
+ model_dict = torch.load(model_name_pth, map_location=device)
+ else:
+ model_dict = cls.convert_tf2torch(model, model_file)
+ model.load_state_dict(model_dict)
+ else:
+ model_dict = torch.load(model_file, map_location=device)
+ model.load_state_dict(model_dict)
+ if model_name_pth is not None and not os.path.exists(model_name_pth):
+ torch.save(model_dict, model_name_pth)
+ logging.info("model_file is saved to pth: {}".format(model_name_pth))
+
+ return model, args
+
+ @classmethod
+ def convert_tf2torch(
+ cls,
+ model,
+ ckpt,
+ ):
+ logging.info("start convert tf model to torch model")
+ from funasr.modules.streaming_utils.load_fr_tf import load_tf_dict
+ var_dict_tf = load_tf_dict(ckpt)
+ var_dict_torch = model.state_dict()
+ var_dict_torch_update = dict()
+ # speech encoder
+ var_dict_torch_update_local = model.encoder.convert_tf2torch(var_dict_tf, var_dict_torch)
+ var_dict_torch_update.update(var_dict_torch_update_local)
+ # speaker encoder
+ var_dict_torch_update_local = model.speaker_encoder.convert_tf2torch(var_dict_tf, var_dict_torch)
+ var_dict_torch_update.update(var_dict_torch_update_local)
+ # cd scorer
+ var_dict_torch_update_local = model.cd_scorer.convert_tf2torch(var_dict_tf, var_dict_torch)
+ var_dict_torch_update.update(var_dict_torch_update_local)
+ # ci scorer
+ var_dict_torch_update_local = model.ci_scorer.convert_tf2torch(var_dict_tf, var_dict_torch)
+ var_dict_torch_update.update(var_dict_torch_update_local)
+ # decoder
+ var_dict_torch_update_local = model.decoder.convert_tf2torch(var_dict_tf, var_dict_torch)
+ var_dict_torch_update.update(var_dict_torch_update_local)
+
+ return var_dict_torch_update
diff --git a/funasr/utils/job_runner.py b/funasr/utils/job_runner.py
new file mode 100644
index 0000000..a35d49c
--- /dev/null
+++ b/funasr/utils/job_runner.py
@@ -0,0 +1,103 @@
+from __future__ import print_function
+from multiprocessing import Pool
+import argparse
+from tqdm import tqdm
+import math
+
+
+class MultiProcessRunner:
+ def __init__(self, fn):
+ self.args = None
+ self.process = fn
+
+ def run(self):
+ parser = argparse.ArgumentParser("")
+ # Task-independent options
+ parser.add_argument("--nj", type=int, default=16)
+ parser.add_argument("--debug", action="store_true", default=False)
+ parser.add_argument("--no_pbar", action="store_true", default=False)
+ parser.add_argument("--verbose", action="store_ture", default=False)
+
+ task_list, args = self.prepare(parser)
+ result_list = self.pool_run(task_list, args)
+ self.post(result_list, args)
+
+ def prepare(self, parser):
+ raise NotImplementedError("Please implement the prepare function.")
+
+ def post(self, result_list, args):
+ raise NotImplementedError("Please implement the post function.")
+
+ def pool_run(self, tasks, args):
+ results = []
+ if args.debug:
+ one_result = self.process(tasks[0])
+ results.append(one_result)
+ else:
+ pool = Pool(args.nj)
+ for one_result in tqdm(pool.imap(self.process, tasks), total=len(tasks), ascii=True, disable=args.no_pbar):
+ results.append(one_result)
+ pool.close()
+
+ return results
+
+
+class MultiProcessRunnerV2:
+ def __init__(self, fn):
+ self.args = None
+ self.process = fn
+
+ def run(self):
+ parser = argparse.ArgumentParser("")
+ # Task-independent options
+ parser.add_argument("--nj", type=int, default=16)
+ parser.add_argument("--debug", action="store_true", default=False)
+ parser.add_argument("--no_pbar", action="store_true", default=False)
+ parser.add_argument("--verbose", action="store_true", default=False)
+
+ task_list, args = self.prepare(parser)
+ chunk_size = int(math.ceil(float(len(task_list)) / args.nj))
+ if args.verbose:
+ print("Split {} tasks into {} sub-tasks with chunk_size {}".format(len(task_list), args.nj, chunk_size))
+ subtask_list = [task_list[i*chunk_size: (i+1)*chunk_size] for i in range(args.nj)]
+ result_list = self.pool_run(subtask_list, args)
+ self.post(result_list, args)
+
+ def prepare(self, parser):
+ raise NotImplementedError("Please implement the prepare function.")
+
+ def post(self, result_list, args):
+ raise NotImplementedError("Please implement the post function.")
+
+ def pool_run(self, tasks, args):
+ results = []
+ if args.debug:
+ one_result = self.process(tasks[0])
+ results.append(one_result)
+ else:
+ pool = Pool(args.nj)
+ for one_result in tqdm(pool.imap(self.process, tasks), total=len(tasks), ascii=True, disable=args.no_pbar):
+ results.append(one_result)
+ pool.close()
+
+ return results
+
+
+class MultiProcessRunnerV3(MultiProcessRunnerV2):
+ def run(self):
+ parser = argparse.ArgumentParser("")
+ # Task-independent options
+ parser.add_argument("--nj", type=int, default=16)
+ parser.add_argument("--debug", action="store_true", default=False)
+ parser.add_argument("--no_pbar", action="store_true", default=False)
+ parser.add_argument("--verbose", action="store_true", default=False)
+ parser.add_argument("--sr", type=int, default=16000)
+
+ task_list, shared_param, args = self.prepare(parser)
+ chunk_size = int(math.ceil(float(len(task_list)) / args.nj))
+ if args.verbose:
+ print("Split {} tasks into {} sub-tasks with chunk_size {}".format(len(task_list), args.nj, chunk_size))
+ subtask_list = [(i, task_list[i * chunk_size: (i + 1) * chunk_size], shared_param, args)
+ for i in range(args.nj)]
+ result_list = self.pool_run(subtask_list, args)
+ self.post(result_list, args)
diff --git a/funasr/utils/misc.py b/funasr/utils/misc.py
new file mode 100644
index 0000000..f27a63c
--- /dev/null
+++ b/funasr/utils/misc.py
@@ -0,0 +1,48 @@
+import io
+from collections import OrderedDict
+import numpy as np
+
+
+def statistic_model_parameters(model, prefix=None):
+ var_dict = model.state_dict()
+ numel = 0
+ for i, key in enumerate(sorted(list([x for x in var_dict.keys() if "num_batches_tracked" not in x]))):
+ if prefix is None or key.startswith(prefix):
+ numel += var_dict[key].numel()
+ return numel
+
+
+def int2vec(x, vec_dim=8, dtype=np.int):
+ b = ('{:0' + str(vec_dim) + 'b}').format(x)
+ # little-endian order: lower bit first
+ return (np.array(list(b)[::-1]) == '1').astype(dtype)
+
+
+def seq2arr(seq, vec_dim=8):
+ return np.row_stack([int2vec(int(x), vec_dim) for x in seq])
+
+
+def load_scp_as_dict(scp_path, value_type='str', kv_sep=" "):
+ with io.open(scp_path, 'r', encoding='utf-8') as f:
+ ret_dict = OrderedDict()
+ for one_line in f.readlines():
+ one_line = one_line.strip()
+ pos = one_line.find(kv_sep)
+ key, value = one_line[:pos], one_line[pos + 1:]
+ if value_type == 'list':
+ value = value.split(' ')
+ ret_dict[key] = value
+ return ret_dict
+
+
+def load_scp_as_list(scp_path, value_type='str', kv_sep=" "):
+ with io.open(scp_path, 'r', encoding='utf8') as f:
+ ret_dict = []
+ for one_line in f.readlines():
+ one_line = one_line.strip()
+ pos = one_line.find(kv_sep)
+ key, value = one_line[:pos], one_line[pos + 1:]
+ if value_type == 'list':
+ value = value.split(' ')
+ ret_dict.append((key, value))
+ return ret_dict
--
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