convert dataset format to paddlespeech, test=doc

examples/ami/sd0/local/compute_embdding.py

@@ -19,7 +19,6 @@ import sys
 
 import numpy as np
 import paddle
-from ami_dataset import AMIDataset
 from paddle.io import BatchSampler
 from paddle.io import DataLoader
 from tqdm.contrib import tqdm
@@ -28,6 +27,7 @@ from yacs.config import CfgNode
 from paddlespeech.s2t.utils.log import Log
 from paddlespeech.vector.cluster.diarization import EmbeddingMeta
 from paddlespeech.vector.io.batch import batch_feature_normalize
+from paddlespeech.vector.io.dataset_from_json import JSONDataset
 from paddlespeech.vector.models.ecapa_tdnn import EcapaTdnn
 from paddlespeech.vector.modules.sid_model import SpeakerIdetification
 from paddlespeech.vector.training.seeding import seed_everything
@@ -65,7 +65,7 @@ def create_dataloader(json_file, batch_size):
     """
 
     # create datasets
-    dataset = AMIDataset(
+    dataset = JSONDataset(
         json_file=json_file,
         feat_type='melspectrogram',
         n_mels=config.n_mels,
@@ -93,8 +93,7 @@ def main(args, config):
     ecapa_tdnn = EcapaTdnn(**config.model)
 
     # stage2: build the speaker verification eval instance with backbone model
-    model = SpeakerIdetification(
-        backbone=ecapa_tdnn, num_class=1)
+    model = SpeakerIdetification(backbone=ecapa_tdnn, num_class=1)
 
     # stage3: load the pre-trained model
     #         we get the last model from the epoch and save_interval

examples/ami/sd0/local/process.sh

@@ -4,7 +4,6 @@ stage=0
 set=L
 
 . ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
-set -u
 set -o pipefail
 
 data_folder=$1
@@ -12,6 +11,7 @@ manual_annot_folder=$2
 save_folder=$3
 pretrained_model_dir=$4
 conf_path=$5
+device=$6
 
 ref_rttm_dir=${save_folder}/ref_rttms
 meta_data_dir=${save_folder}/metadata
@@ -35,7 +35,7 @@ if [ ${stage} -le 1 ]; then
     for name in dev eval; do
         python local/compute_embdding.py --config ${conf_path} \
                 --data-dir ${save_folder} \
-                --device gpu:0 \
+                --device ${device} \
                 --dataset ${name} \
                 --load-checkpoint ${pretrained_model_dir}
     done

examples/ami/sd0/run.sh

@@ -3,8 +3,7 @@
 . ./path.sh || exit 1;
 set -e
 
-stage=1
-stop_stage=50
+stage=0
 
 #TARGET_DIR=${MAIN_ROOT}/dataset/ami
 TARGET_DIR=/home/dataset/AMI
@@ -12,15 +11,14 @@ data_folder=${TARGET_DIR}/amicorpus #e.g., /path/to/amicorpus/
 manual_annot_folder=${TARGET_DIR}/ami_public_manual_1.6.2 #e.g., /path/to/ami_public_manual_1.6.2/
 
 save_folder=./save
-pretraind_model_dir=${save_folder}/model
-
+pretraind_model_dir=${save_folder}/sv0_ecapa_tdnn_voxceleb12_ckpt_0_1_1/model
 conf_path=conf/ecapa_tdnn.yaml
-
+device=gpu
 
 . ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
 
-if [ $stage -le 0 ] && [ ${stop_stage} -ge 0 ]; then
-    # Prepare data and model
+if [ $stage -le 0 ]; then
+    # Prepare data
     # Download AMI corpus, You need around 10GB of free space to get whole data
     # The signals are too large to package in this way,
     # so you need to use the chooser to indicate which ones you wish to download
@@ -29,12 +27,20 @@ if [ $stage -le 0 ] && [ ${stop_stage} -ge 0 ]; then
     echo "Signals: "
     echo "1) Select one or more AMI meetings: the IDs please follow ./ami_split.py"
     echo "2) Select media streams: Just select Headset mix"
-    # Download the pretrained Model from HuggingFace or other pretrained model
-    echo "Please download the pretrained ECAPA-TDNN Model and put the pretrainde model in given path: "${pretraind_model_dir}
 fi
 
-if [ $stage -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+if [ $stage -le 1 ]; then
+    # Download the pretrained model
+    wget https://paddlespeech.bj.bcebos.com/vector/voxceleb/sv0_ecapa_tdnn_voxceleb12_ckpt_0_1_1.tar.gz
+    mkdir -p ${save_folder} && tar -xvf sv0_ecapa_tdnn_voxceleb12_ckpt_0_1_1.tar.gz -C ${save_folder}
+    rm -rf sv0_ecapa_tdnn_voxceleb12_ckpt_0_1_1.tar.gz
+    echo "download the pretrained ECAPA-TDNN Model to path: "${pretraind_model_dir}
+fi
+
+if [ $stage -le 2 ]; then
     # Tune hyperparams on dev set and perform final diarization on dev and eval with best hyperparams.
-    bash ./local/process.sh ${data_folder} ${manual_annot_folder} ${save_folder} ${pretraind_model_dir} ${conf_path} || exit 1
+    echo ${data_folder} ${manual_annot_folder} ${save_folder} ${pretraind_model_dir} ${conf_path}
+    bash ./local/process.sh ${data_folder} ${manual_annot_folder} \
+        ${save_folder} ${pretraind_model_dir} ${conf_path} ${device} || exit 1
 fi
 

paddlespeech/vector/io/dataset_from_json.py

+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import json
+
+from dataclasses import dataclass
+from dataclasses import fields
+from paddle.io import Dataset
+
+from paddleaudio import load as load_audio
+from paddleaudio.compliance.librosa import melspectrogram
+from paddleaudio.compliance.librosa import mfcc
+
+
+@dataclass
+class meta_info:
+    """the audio meta info in the vector JSONDataset
+    Args:
+        id (str): the segment name
+        duration (float): segment time
+        wav (str): wav file path
+        start (int): start point in the original wav file
+        stop (int): stop point in the original wav file
+        lab_id (str): the record id
+    """
+    id: str
+    duration: float
+    wav: str
+    start: int
+    stop: int
+    record_id: str
+
+
+# json dataset support feature type
+feat_funcs = {
+    'raw': None,
+    'melspectrogram': melspectrogram,
+    'mfcc': mfcc,
+}
+
+
+class JSONDataset(Dataset):
+    """
+    dataset from json file.
+    """
+
+    def __init__(self, json_file: str, feat_type: str='raw', **kwargs):
+        """
+        Ags:
+            json_file (:obj:`str`): Data prep JSON file.
+            labels (:obj:`List[int]`): Labels of audio files.
+            feat_type (:obj:`str`, `optional`, defaults to `raw`):
+                It identifies the feature type that user wants to extrace of an audio file.
+        """
+        if feat_type not in feat_funcs.keys():
+            raise RuntimeError(
+                f"Unknown feat_type: {feat_type}, it must be one in {list(feat_funcs.keys())}"
+            )
+
+        self.json_file = json_file
+        self.feat_type = feat_type
+        self.feat_config = kwargs
+        self._data = self._get_data()
+        super(JSONDataset, self).__init__()
+
+    def _get_data(self):
+        with open(self.json_file, "r") as f:
+            meta_data = json.load(f)
+        data = []
+        for key in meta_data:
+            sub_seg = meta_data[key]["wav"]
+            wav = sub_seg["file"]
+            duration = sub_seg["duration"]
+            start = sub_seg["start"]
+            stop = sub_seg["stop"]
+            rec_id = str(key).rsplit("_", 2)[0]
+            data.append(
+                meta_info(
+                    str(key),
+                    float(duration), wav, int(start), int(stop), str(rec_id)))
+        return data
+
+    def _convert_to_record(self, idx: int):
+        sample = self._data[idx]
+
+        record = {}
+        # To show all fields in a namedtuple
+        for field in fields(sample):
+            record[field.name] = getattr(sample, field.name)
+
+        waveform, sr = load_audio(record['wav'])
+        waveform = waveform[record['start']:record['stop']]
+
+        feat_func = feat_funcs[self.feat_type]
+        feat = feat_func(
+            waveform, sr=sr, **self.feat_config) if feat_func else waveform
+
+        record.update({'feat': feat})
+
+        return record
+
+    def __getitem__(self, idx):
+        return self._convert_to_record(idx)
+
+    def __len__(self):
+        return len(self._data)