add vector csv dataset format, test=doc

examples/voxceleb/sv0/conf/ecapa_tdnn.yaml

@@ -4,9 +4,9 @@
 # we should explicitly specify the wav path of vox2 audio data converted from m4a
 vox2_base_path: 
 augment: True
-batch_size: 16
+batch_size: 32
 num_workers: 2
-num_speakers: 7205 # 1211 vox1, 5994 vox2, 7205 vox1+2, test speakers: 41
+num_speakers: 1211 # 1211 vox1, 5994 vox2, 7205 vox1+2, test speakers: 41
 shuffle: True
 skip_prep: False
 split_ratio: 0.9

examples/voxceleb/sv0/local/data.sh

@@ -12,7 +12,7 @@
 # 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.
-stage=7
+stage=0
 stop_stage=100
 
 . ${MAIN_ROOT}/utils/parse_options.sh || exit -1;
@@ -32,7 +32,7 @@ mkdir -p ${dir}
 
 # Generally the `MAIN_ROOT` refers to the root of PaddleSpeech,
 # which is defined in the path.sh
-# And we will download the 
+# And we will download the voxceleb data and rirs noise to ${MAIN_ROOT}/dataset
 TARGET_DIR=${MAIN_ROOT}/dataset
 mkdir -p ${TARGET_DIR}
 
@@ -98,7 +98,7 @@ if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
    # generate the vox csv file
    # Currently, our training system use csv file for dataset
    echo "convert the json format to csv format to be compatible with training process"
-   python3 local/make_csv_dataset_from_json.py\
+   python3 local/make_vox_csv_dataset_from_json.py\
       --train "${dir}/vox1/manifest.dev" \
       --test "${dir}/vox1/manifest.test" \
       --target_dir "${dir}/vox/" \

examples/voxceleb/sv0/local/make_rirs_noise_csv_dataset_from_json.py

@@ -20,31 +20,29 @@ import csv
 import os
 from typing import List
 
-import paddle
 import tqdm
 from yacs.config import CfgNode
 
-from paddlespeech.s2t.utils.log import Log
-from paddlespeech.vector.training.seeding import seed_everything
-logger = Log(__name__).getlog()
 from paddleaudio import load as load_audio
-from paddleaudio import save as save_wav
-
+from paddlespeech.s2t.utils.log import Log
+from paddlespeech.vector.utils.utils import get_chunks
 
-def get_chunks(seg_dur, audio_id, audio_duration):
-    num_chunks = int(audio_duration / seg_dur)  # all in milliseconds
+logger = Log(__name__).getlog()
 
-    chunk_lst = [
-        audio_id + "_" + str(i * seg_dur) + "_" + str(i * seg_dur + seg_dur)
-        for i in range(num_chunks)
-    ]
-    return chunk_lst
 
+def get_chunks_list(wav_file: str,
+                    split_chunks: bool,
+                    base_path: str,
+                    chunk_duration: float=3.0) -> List[List[str]]:
+    """Get the single audio file info 
 
-def get_audio_info(wav_file: str,
-                   split_chunks: bool,
-                   base_path: str,
-                   chunk_duration: float=3.0) -> List[List[str]]:
+    Args:
+        wav_file (list): the wav audio file and get this audio segment info list
+        split_chunks (bool): audio split flag
+        base_path (str): the audio base path 
+        chunk_duration (float): the chunk duration. 
+                                if set the split_chunks, we split the audio into multi-chunks segment.
+    """
     waveform, sr = load_audio(wav_file)
     audio_id = wav_file.split("/rir_noise/")[-1].split(".")[0]
     audio_duration = waveform.shape[0] / sr
@@ -57,13 +55,16 @@ def get_audio_info(wav_file: str,
             s, e = chunk.split("_")[-2:]  # Timestamps of start and end
             start_sample = int(float(s) * sr)
             end_sample = int(float(e) * sr)
-            new_wav_file = os.path.join(base_path,
-                                        audio_id + f'_chunk_{idx+1:02}.wav')
-            save_wav(waveform[start_sample:end_sample], sr, new_wav_file)
-            # id, duration, new_wav
-            ret.append([chunk, chunk_duration, new_wav_file])
+
+            # currently, all vector csv data format use one representation
+            # id, duration, wav, start, stop, spk_id
+            ret.append([
+                chunk, audio_duration, wav_file, start_sample, end_sample,
+                "noise"
+            ])
     else:  # Keep whole audio.
-        ret.append([audio_id, audio_duration, wav_file])
+        ret.append(
+            [audio_id, audio_duration, wav_file, 0, waveform.shape[0], "noise"])
     return ret
 
 
@@ -71,12 +72,20 @@ def generate_csv(wav_files,
                  output_file: str,
                  base_path: str,
                  split_chunks: bool=True):
-    print(f'Generating csv: {output_file}')
-    header = ["id", "duration", "wav"]
+    """Prepare the csv file according the wav files
+
+    Args:
+        wav_files (list): all the audio list to prepare the csv file
+        output_file (str): the output csv file
+        config (CfgNode): yaml configuration content
+        split_chunks (bool): audio split flag
+    """
+    logger.info(f'Generating csv: {output_file}')
+    header = ["utt_id", "duration", "wav", "start", "stop", "lab_id"]
     csv_lines = []
     for item in tqdm.tqdm(wav_files):
         csv_lines.extend(
-            get_audio_info(
+            get_chunks_list(
                 item, base_path=base_path, split_chunks=split_chunks))
 
     if not os.path.exists(os.path.dirname(output_file)):
@@ -91,11 +100,12 @@ def generate_csv(wav_files,
 
 
 def prepare_data(args, config):
-    # stage0: set the cpu device, 
-    # all data prepare process will be done in cpu mode
-    paddle.device.set_device("cpu")
-    # set the random seed, it is a must for multiprocess training
-    seed_everything(config.seed)
+    """Convert the jsonline format to csv format
+
+    Args:
+        args (argparse.Namespace): scripts args
+        config (CfgNode): yaml configuration content
+    """
     # if external config set the skip_prep flat, we will do nothing
     if config.skip_prep:
         return
@@ -119,6 +129,7 @@ def prepare_data(args, config):
             noise_files.append(os.path.join(base_path, noise_file))
 
     csv_path = os.path.join(args.data_dir, 'csv')
+    logger.info(f"csv path: {csv_path}")
     generate_csv(
         rir_files, os.path.join(csv_path, 'rir.csv'), base_path=base_path)
     generate_csv(

examples/voxceleb/sv0/local/make_vox_csv_dataset_from_json.py

@@ -21,51 +21,34 @@ import json
 import os
 import random
 
-import paddle
 import tqdm
 from yacs.config import CfgNode
 
 from paddleaudio import load as load_audio
 from paddlespeech.s2t.utils.log import Log
-from paddlespeech.vector.training.seeding import seed_everything
-logger = Log(__name__).getlog()
-
-
-def get_chunks(seg_dur, audio_id, audio_duration):
-    """Get all chunk segments from a utterance
+from paddlespeech.vector.utils.utils import get_chunks
 
-    Args:
-        seg_dur (float): segment chunk duration
-        audio_id (str): utterance name
-        audio_duration (float): utterance duration
-
-    Returns:
-        List: all the chunk segments 
-    """
-    num_chunks = int(audio_duration / seg_dur)  # all in milliseconds
-    chunk_lst = [
-        audio_id + "_" + str(i * seg_dur) + "_" + str(i * seg_dur + seg_dur)
-        for i in range(num_chunks)
-    ]
-    return chunk_lst
+logger = Log(__name__).getlog()
 
 
 def prepare_csv(wav_files, output_file, config, split_chunks=True):
     """Prepare the csv file according the wav files
 
     Args:
-        dataset_list (list): all the dataset to get the test utterances
-        verification_file (str): voxceleb1 trial file
+        wav_files (list): all the audio list to prepare the csv file
+        output_file (str): the output csv file
+        config (CfgNode): yaml configuration content
+        split_chunks (bool): audio split flag
     """
     if not os.path.exists(os.path.dirname(output_file)):
         os.makedirs(os.path.dirname(output_file))
     csv_lines = []
-    header = ["id", "duration", "wav", "start", "stop", "spk_id"]
+    header = ["utt_id", "duration", "wav", "start", "stop", "lab_id"]
     # voxceleb meta info for each training utterance segment
     # we extract a segment from a utterance to train 
     # and the segment' period is between start and stop time point in the original wav file
     # each field in the meta means as follows:
-    # id: the utterance segment name
+    # utt_id: the utterance segment name
     # duration: utterance segment time
     # wav: utterance file path
     # start: start point in the original wav file
@@ -194,11 +177,9 @@ def prepare_data(args, config):
         args (argparse.Namespace): scripts args
         config (CfgNode): yaml configuration content
     """
-    # stage0: set the cpu device, 
-    # all data prepare process will be done in cpu mode
-    paddle.device.set_device("cpu")
-    # set the random seed, it is a must for multiprocess training
-    seed_everything(config.seed)
+    # stage0: set the random seed
+    random.seed(config.seed)
+
     # if external config set the skip_prep flat, we will do nothing
     if config.skip_prep:
         return

paddlespeech/vector/exps/ecapa_tdnn/train.py

@@ -29,7 +29,7 @@ from paddlespeech.vector.io.augment import waveform_augment
 from paddlespeech.vector.io.batch import batch_pad_right
 from paddlespeech.vector.io.batch import feature_normalize
 from paddlespeech.vector.io.batch import waveform_collate_fn
-from paddlespeech.vector.io.dataset import VoxCelebDataset
+from paddlespeech.vector.io.dataset import CSVDataset
 from paddlespeech.vector.models.ecapa_tdnn import EcapaTdnn
 from paddlespeech.vector.modules.loss import AdditiveAngularMargin
 from paddlespeech.vector.modules.loss import LogSoftmaxWrapper
@@ -55,11 +55,11 @@ def main(args, config):
 
     # stage2: data prepare, such vox1 and vox2 data, and augment noise data and pipline
     # note: some cmd must do in rank==0, so wo will refactor the data prepare code
-    train_dataset = VoxCelebDataset(
+    train_dataset = CSVDataset(
         csv_path=os.path.join(args.data_dir, "vox/csv/train.csv"),
         spk_id2label_path=os.path.join(args.data_dir,
                                        "vox/meta/spk_id2label.txt"))
-    dev_dataset = VoxCelebDataset(
+    dev_dataset = CSVDataset(
         csv_path=os.path.join(args.data_dir, "vox/csv/dev.csv"),
         spk_id2label_path=os.path.join(args.data_dir,
                                        "vox/meta/spk_id2label.txt"))
@@ -74,7 +74,7 @@ def main(args, config):
 
     # stage4: build the speaker verification train instance with backbone model
     model = SpeakerIdetification(
-        backbone=ecapa_tdnn, num_class=VoxCeleb.num_speakers)
+        backbone=ecapa_tdnn, num_class=config.num_speakers)
 
     # stage5: build the optimizer, we now only construct the AdamW optimizer
     #         140000 is single gpu steps
@@ -148,6 +148,7 @@ def main(args, config):
         train_reader_cost = 0.0
         train_feat_cost = 0.0
         train_run_cost = 0.0
+        train_misce_cost = 0.0
 
         reader_start = time.time()
         for batch_idx, batch in enumerate(train_loader):
@@ -203,12 +204,14 @@ def main(args, config):
             train_run_cost += time.time() - train_start
 
             # stage 9-8: Calculate average loss per batch
-            avg_loss += loss.numpy()[0]
+            train_misce_start = time.time()
+            avg_loss = loss.item()
 
             # stage 9-9: Calculate metrics, which is one-best accuracy
             preds = paddle.argmax(logits, axis=1)
             num_corrects += (preds == labels).numpy().sum()
             num_samples += feats.shape[0]
+
             timer.count()  # step plus one in timer
 
             # stage 9-10: print the log information only on 0-rank per log-freq batchs
@@ -227,6 +230,7 @@ def main(args, config):
                     train_feat_cost / config.log_interval)
                 print_msg += ' avg_train_cost: {:.5f} sec,'.format(
                     train_run_cost / config.log_interval)
+
                 print_msg += ' lr={:.4E} step/sec={:.2f} | ETA {}'.format(
                     lr, timer.timing, timer.eta)
                 logger.info(print_msg)

paddlespeech/vector/io/augment.py

@@ -14,6 +14,7 @@
 # this is modified from SpeechBrain
 # https://github.com/speechbrain/speechbrain/blob/085be635c07f16d42cd1295045bc46c407f1e15b/speechbrain/lobes/augment.py
 import math
+import os
 from typing import List
 
 import numpy as np
@@ -22,13 +23,12 @@ import paddle.nn as nn
 import paddle.nn.functional as F
 
 from paddlespeech.s2t.utils.log import Log
-from paddlespeech.vector.io.dataset import RIRSNoiseDataset
+from paddlespeech.vector.io.dataset import CSVDataset
 from paddlespeech.vector.io.signal_processing import compute_amplitude
 from paddlespeech.vector.io.signal_processing import convolve1d
 from paddlespeech.vector.io.signal_processing import dB_to_amplitude
 from paddlespeech.vector.io.signal_processing import notch_filter
 from paddlespeech.vector.io.signal_processing import reverberate
-# from paddleaudio.datasets.rirs_noises import OpenRIRNoise
 
 logger = Log(__name__).getlog()
 
@@ -510,7 +510,7 @@ class AddNoise(nn.Layer):
                     assert w >= 0, f'Target length {target_length} is less than origin length {x.shape[0]}'
                     return np.pad(x, [0, w], mode=mode, **kwargs)
 
-                ids = [item['id'] for item in batch]
+                ids = [item['utt_id'] for item in batch]
                 lengths = np.asarray([item['feat'].shape[0] for item in batch])
                 waveforms = list(
                     map(lambda x: pad(x, max(max_length, lengths.max().item())),
@@ -590,7 +590,7 @@ class AddReverb(nn.Layer):
                 assert w >= 0, f'Target length {target_length} is less than origin length {x.shape[0]}'
                 return np.pad(x, [0, w], mode=mode, **kwargs)
 
-            ids = [item['id'] for item in batch]
+            ids = [item['utt_id'] for item in batch]
             lengths = np.asarray([item['feat'].shape[0] for item in batch])
             waveforms = list(
                 map(lambda x: pad(x, lengths.max().item()),
@@ -840,10 +840,10 @@ def build_augment_pipeline(target_dir=None) -> List[paddle.nn.Layer]:
         List[paddle.nn.Layer]: all augment process
     """
     logger.info("start to build the augment pipeline")
-    noise_dataset = RIRSNoiseDataset(csv_path=os.path.join(
-        target_dir, "rir_noise/csv/noise.csv"))
-    rir_dataset = OpenRIRNoise(csv_path=os.path.join(target_dir,
-                                                     "rir_noise/csv/rir.csv"))
+    noise_dataset = CSVDataset(csv_path=os.path.join(target_dir,
+                                                     "rir_noise/csv/noise.csv"))
+    rir_dataset = CSVDataset(csv_path=os.path.join(target_dir,
+                                                   "rir_noise/csv/rir.csv"))
 
     wavedrop = TimeDomainSpecAugment(
         sample_rate=16000,

paddlespeech/vector/io/dataset.py

@@ -11,18 +11,38 @@
 # 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 collections
-
+from dataclasses import dataclass
+from dataclasses import fields
 from paddle.io import Dataset
 
 from paddleaudio import load as load_audio
+from paddlespeech.s2t.utils.log import Log
+logger = Log(__name__).getlog()
+
+# the audio meta info in the vector CSVDataset
+# utt_id: the utterance segment name
+# duration: utterance segment time
+# wav: utterance file path
+# start: start point in the original wav file
+# stop: stop point in the original wav file
+# lab_id: the utterance segment's label id
+
 
+@dataclass
+class meta_info:
+    utt_id: str
+    duration: float
+    wav: str
+    start: int
+    stop: int
+    lab_id: str
 
-class VoxCelebDataset(Dataset):
-    meta_info = collections.namedtuple(
-        'META_INFO', ('id', 'duration', 'wav', 'start', 'stop', 'spk_id'))
 
-    def __init__(self, csv_path, spk_id2label_path, config):
+class CSVDataset(Dataset):
+    # meta_info = collections.namedtuple(
+    #     'META_INFO', ('id', 'duration', 'wav', 'start', 'stop', 'spk_id'))
+
+    def __init__(self, csv_path, spk_id2label_path=None, config=None):
         super().__init__()
         self.csv_path = csv_path
         self.spk_id2label_path = spk_id2label_path
@@ -32,34 +52,41 @@ class VoxCelebDataset(Dataset):
 
     def load_data_csv(self):
         data = []
+
         with open(self.csv_path, 'r') as rf:
             for line in rf.readlines()[1:]:
                 audio_id, duration, wav, start, stop, spk_id = line.strip(
                 ).split(',')
                 data.append(
-                    self.meta_info(audio_id,
-                                   float(duration), wav,
-                                   int(start), int(stop), spk_id))
+                    meta_info(audio_id,
+                              float(duration), wav,
+                              int(start), int(stop), spk_id))
         return data
 
     def load_speaker_to_label(self):
+        if not self.spk_id2label_path:
+            logger.warning("No speaker id to label file")
+            return
+        spk_id2label = {}
         with open(self.spk_id2label_path, 'r') as f:
             for line in f.readlines():
                 spk_id, label = line.strip().split(' ')
-                self.spk_id2label[spk_id] = int(label)
+                spk_id2label[spk_id] = int(label)
+
+        return spk_id2label
 
     def convert_to_record(self, idx: int):
         sample = self.data[idx]
 
         record = {}
         # To show all fields in a namedtuple: `type(sample)._fields`
-        for field in type(sample)._fields:
-            record[field] = getattr(sample, field)
+        for field in fields(sample):
+            record[field.name] = getattr(sample, field.name)
 
         waveform, sr = load_audio(record['wav'])
 
         # random select a chunk audio samples from the audio
-        if self.config.random_chunk:
+        if self.config and self.config.random_chunk:
             num_wav_samples = waveform.shape[0]
             num_chunk_samples = int(self.config.chunk_duration * sr)
             start = random.randint(0, num_wav_samples - num_chunk_samples - 1)
@@ -71,46 +98,9 @@ class VoxCelebDataset(Dataset):
         # we only return the waveform as feat
         waveform = waveform[start:stop]
         record.update({'feat': waveform})
-        record.update({'label': self.spk_id2label[record['spk_id']]})
-
-        return record
-
-    def __getitem__(self, idx):
-        return self.convert_to_record(idx)
-
-    def __len__(self):
-        return len(self.data)
-
-
-class RIRSNoiseDataset(Dataset):
-    meta_info = collections.namedtuple('META_INFO', ('id', 'duration', 'wav'))
-
-    def __init__(self, csv_path):
-        super().__init__()
-        self.csv_path = csv_path
-        self.data = self.load_csv_data()
+        if self.spk_id2label:
+            record.update({'label': self.spk_id2label[record['lab_id']]})
 
-    def load_csv_data(self):
-        data = []
-        with open(self.csv_path, 'r') as rf:
-            for line in rf.readlines()[1:]:
-                audio_id, duration, wav = line.strip().split(',')
-                data.append(self.meta_info(audio_id, float(duration), wav))
-
-        random.shuffle(data)
-        return data
-
-    def convert_to_record(self, idx: int):
-        sample = self.data[idx]
-
-        record = {}
-        # To show all fields in a namedtuple: `type(sample)._fields`
-        for field in type(sample)._fields:
-            record[field] = getattr(sample, field)
-
-        waveform, sr = load_audio(record['wav'])
-
-        record.update({'feat': waveform})
         return record
 
     def __getitem__(self, idx):

paddlespeech/vector/utils/utils.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.
+
+
+def get_chunks(seg_dur, audio_id, audio_duration):
+    """Get all chunk segments from a utterance
+
+    Args:
+        seg_dur (float): segment chunk duration
+        audio_id (str): utterance name
+        audio_duration (float): utterance duration
+
+    Returns:
+        List: all the chunk segments 
+    """
+    num_chunks = int(audio_duration / seg_dur)  # all in milliseconds
+    chunk_lst = [
+        audio_id + "_" + str(i * seg_dur) + "_" + str(i * seg_dur + seg_dur)
+        for i in range(num_chunks)
+    ]
+    return chunk_lst