sond pipeline

egs/mars/sd/conf/SOND_ECAPATDNN_None_Dot_SAN_L4N512_FSMN_L6N512_n16k2.yaml

@@ -0,0 +1,121 @@
+model: sond
+model_conf:
+    lsm_weight: 0.0
+    length_normalized_loss: true
+    max_spk_num: 16
+
+# speech encoder
+encoder: ecapa_tdnn
+encoder_conf:
+    # pass by model, equal to feature dim
+    # input_size: 80
+    pool_size: 20
+    stride: 1
+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
+
+# minibatch related
+batch_type: length
+# 16s * 16k * 16 samples
+batch_bins: 4096000
+num_workers: 8
+
+# optimization related
+accum_grad: 1
+grad_clip: 5
+max_epoch: 50
+val_scheduler_criterion:
+    - valid
+    - acc
+best_model_criterion:
+-   - valid
+    - der
+    - min
+-   - valid
+    - forward_steps
+    - max
+keep_nbest_models: 10
+
+optim: adam
+optim_conf:
+   lr: 0.001
+scheduler: warmuplr
+scheduler_conf:
+   warmup_steps: 10000
+
+# without spec aug
+specaug: null
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 30
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_range:
+    - 0
+    - 40
+    num_time_mask: 2
+
+log_interval: 50
+# without normalize
+normalize: None

egs/mars/sd/local_run.sh

@@ -0,0 +1,171 @@
+#!/usr/bin/env bash
+
+. ./path.sh || exit 1;
+
+# machines configuration
+CUDA_VISIBLE_DEVICES="6,7"
+gpu_num=2
+count=1
+gpu_inference=true  # Whether to perform gpu decoding, set false for cpu decoding
+# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
+njob=5
+train_cmd=utils/run.pl
+infer_cmd=utils/run.pl
+
+# general configuration
+feats_dir="." #feature output dictionary
+exp_dir="."
+lang=zh
+dumpdir=dump/raw
+feats_type=raw
+token_type=char
+scp=wav.scp
+type=kaldi_ark
+stage=3
+stop_stage=4
+
+# feature configuration
+feats_dim=
+sample_frequency=16000
+nj=32
+speed_perturb=
+
+# exp tag
+tag="exp1"
+
+. utils/parse_options.sh || exit 1;
+
+# Set bash to 'debug' mode, it will exit on :
+# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
+set -e
+set -u
+set -o pipefail
+
+train_set=train
+valid_set=dev
+test_sets="dev test"
+
+asr_config=conf/train_asr_conformer.yaml
+model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
+
+inference_config=conf/decode_asr_transformer.yaml
+inference_asr_model=valid.acc.ave_10best.pth
+
+# you can set gpu num for decoding here
+gpuid_list=$CUDA_VISIBLE_DEVICES  # set gpus for decoding, the same as training stage by default
+ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')
+
+if ${gpu_inference}; then
+    inference_nj=$[${ngpu}*${njob}]
+    _ngpu=1
+else
+    inference_nj=$njob
+    _ngpu=0
+fi
+
+feat_train_dir=${feats_dir}/${dumpdir}/train; mkdir -p ${feat_train_dir}
+feat_dev_dir=${feats_dir}/${dumpdir}/dev; mkdir -p ${feat_dev_dir}
+feat_test_dir=${feats_dir}/${dumpdir}/test; mkdir -p ${feat_test_dir}
+
+# Training Stage
+world_size=$gpu_num  # run on one machine
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+    echo "stage 3: Training"
+    mkdir -p ${exp_dir}/exp/${model_dir}
+    mkdir -p ${exp_dir}/exp/${model_dir}/log
+    INIT_FILE=${exp_dir}/exp/${model_dir}/ddp_init
+    if [ -f $INIT_FILE ];then
+        rm -f $INIT_FILE
+    fi 
+    init_method=file://$(readlink -f $INIT_FILE)
+    echo "$0: init method is $init_method"
+    for ((i = 0; i < $gpu_num; ++i)); do
+        {
+            rank=$i
+            local_rank=$i
+            gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
+            asr_train.py \
+                --gpu_id $gpu_id \
+                --use_preprocessor true \
+                --token_type char \
+                --token_list $token_list \
+                --train_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${train_set}/${scp},speech,${type} \
+                --train_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${train_set}/text,text,text \
+                --train_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${train_set}/speech_shape \
+                --train_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${train_set}/text_shape.char \
+                --valid_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${valid_set}/${scp},speech,${type} \
+                --valid_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${valid_set}/text,text,text \
+                --valid_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}/speech_shape \
+                --valid_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}/text_shape.char  \
+                --resume true \
+                --output_dir ${exp_dir}/exp/${model_dir} \
+                --config $asr_config \
+                --input_size $feats_dim \
+                --ngpu $gpu_num \
+                --num_worker_count $count \
+                --multiprocessing_distributed true \
+                --dist_init_method $init_method \
+                --dist_world_size $world_size \
+                --dist_rank $rank \
+                --local_rank $local_rank 1> ${exp_dir}/exp/${model_dir}/log/train.log.$i 2>&1
+        } &
+        done
+        wait
+fi
+
+# Testing Stage
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+    echo "stage 4: Inference"
+    for dset in ${test_sets}; do
+        asr_exp=${exp_dir}/exp/${model_dir}
+        inference_tag="$(basename "${inference_config}" .yaml)"
+        _dir="${asr_exp}/${inference_tag}/${inference_asr_model}/${dset}"
+        _logdir="${_dir}/logdir"
+        if [ -d ${_dir} ]; then
+            echo "${_dir} is already exists. if you want to decode again, please delete this dir first."
+            exit 0
+        fi
+        mkdir -p "${_logdir}"
+        _data="${feats_dir}/${dumpdir}/${dset}"
+        key_file=${_data}/${scp}
+        num_scp_file="$(<${key_file} wc -l)"
+        _nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
+        split_scps=
+        for n in $(seq "${_nj}"); do
+            split_scps+=" ${_logdir}/keys.${n}.scp"
+        done
+        # shellcheck disable=SC2086
+        utils/split_scp.pl "${key_file}" ${split_scps}
+        _opts=
+        if [ -n "${inference_config}" ]; then
+            _opts+="--config ${inference_config} "
+        fi
+        ${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1: "${_nj}" "${_logdir}"/asr_inference.JOB.log \
+            python -m funasr.bin.asr_inference_launch \
+                --batch_size 1 \
+                --ngpu "${_ngpu}" \
+                --njob ${njob} \
+                --gpuid_list ${gpuid_list} \
+                --data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
+                --key_file "${_logdir}"/keys.JOB.scp \
+                --asr_train_config "${asr_exp}"/config.yaml \
+                --asr_model_file "${asr_exp}"/"${inference_asr_model}" \
+                --output_dir "${_logdir}"/output.JOB \
+                --mode asr \
+                ${_opts}
+
+        for f in token token_int score text; do
+            if [ -f "${_logdir}/output.1/1best_recog/${f}" ]; then
+                for i in $(seq "${_nj}"); do
+                    cat "${_logdir}/output.${i}/1best_recog/${f}"
+                done | sort -k1 >"${_dir}/${f}"
+            fi
+        done
+        python utils/proce_text.py ${_dir}/text ${_dir}/text.proc
+        python utils/proce_text.py ${_data}/text ${_data}/text.proc
+        python utils/compute_wer.py ${_data}/text.proc ${_dir}/text.proc ${_dir}/text.cer
+        tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt
+        cat ${_dir}/text.cer.txt
+    done
+fi
+

egs/mars/sd/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

egs/mars/sd/scripts/calculate_shapes.py

@@ -0,0 +1,45 @@
+import logging
+import numpy as np
+import soundfile
+import kaldiio
+from funasr.utils.job_runner import MultiProcessRunnerV3
+from funasr.utils.misc import load_scp_as_list, load_scp_as_dict
+import os
+import argparse
+from collections import OrderedDict
+
+
+class MyRunner(MultiProcessRunnerV3):
+
+    def prepare(self, parser: argparse.ArgumentParser):
+        parser.add_argument("--input_scp", type=str, required=True)
+        parser.add_argument("--out_path")
+        args = parser.parse_args()
+
+        if not os.path.exists(os.path.dirname(args.out_path)):
+            os.makedirs(os.path.dirname(args.out_path))
+
+        task_list = load_scp_as_list(args.input_scp)
+        return task_list, None, args
+
+    def post(self, result_list, args):
+        fd = open(args.out_path, "wt", encoding="utf-8")
+        for results in result_list:
+            for uttid, shape in results:
+                fd.write("{} {}\n".format(uttid, ",".join(shape)))
+        fd.close()
+
+
+def process(task_args):
+    task_idx, task_list, _, args = task_args
+    rst = []
+    for uttid, file_path in task_list:
+        data = kaldiio.load_mat(file_path)
+        shape = [str(x) for x in data.shape]
+        rst.append((uttid, shape))
+    return rst
+
+
+if __name__ == '__main__':
+    my_runner = MyRunner(process)
+    my_runner.run()

funasr/models/e2e_diar_sond.py

@@ -90,6 +90,7 @@ class DiarSondModel(AbsESPnetModel):
         self.int_token_arr = torch.from_numpy(np.array(self.token_list).astype(int)[np.newaxis, np.newaxis, :])
         self.speaker_discrimination_loss_weight = speaker_discrimination_loss_weight
         self.inter_score_loss_weight = inter_score_loss_weight
+        self.forward_steps = 0
 
     def generate_pse_embedding(self):
         embedding = np.zeros((len(self.token_list), self.max_spk_num), dtype=np.float)
@@ -123,7 +124,7 @@ class DiarSondModel(AbsESPnetModel):
         """
         assert speech.shape[0] == binary_labels.shape[0], (speech.shape, binary_labels.shape)
         batch_size = speech.shape[0]
-
+        self.forward_steps = self.forward_steps + 1
         # 1. Network forward
         pred, inter_outputs = self.prediction_forward(
             speech, speech_lengths,
@@ -198,6 +199,7 @@ class DiarSondModel(AbsESPnetModel):
             cf=cf,
             acc=acc,
             der=der,
+            forward_steps=self.forward_steps,
         )
 
         loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
@@ -262,8 +264,10 @@ class DiarSondModel(AbsESPnetModel):
         self,
         speech: torch.Tensor,
         speech_lengths: torch.Tensor,
-        spk_labels: torch.Tensor = None,
-        spk_labels_lengths: torch.Tensor = None,
+        profile: torch.Tensor = None,
+        profile_lengths: torch.Tensor = None,
+        binary_labels: torch.Tensor = None,
+        binary_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}

funasr/models/encoder/ecapa_tdnn_encoder.py

@@ -528,8 +528,6 @@ class ECAPA_TDNN(torch.nn.Module):
 
     Arguments
     ---------
-    device : str
-        Device used, e.g., "cpu" or "cuda".
     activation : torch class
         A class for constructing the activation layers.
     channels : list of ints
@@ -555,7 +553,6 @@ class ECAPA_TDNN(torch.nn.Module):
     def __init__(
         self,
         input_size,
-        device="cpu",
         lin_neurons=192,
         activation=torch.nn.ReLU,
         channels=[512, 512, 512, 512, 1536],

funasr/tasks/diar.py

@@ -24,6 +24,7 @@ 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.ecapa_tdnn_encoder import ECAPA_TDNN
 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
@@ -123,6 +124,7 @@ encoder_choices = ClassChoices(
         resnet34=ResNet34Diar,
         sanm_chunk_opt=SANMEncoderChunkOpt,
         data2vec_encoder=Data2VecEncoder,
+        epaca_dtnn=ECAPA_TDNN,
     ),
     type_check=AbsEncoder,
     default="resnet34",