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@ -11,18 +11,38 @@
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import collections
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from dataclasses import dataclass
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from dataclasses import fields
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from paddle.io import Dataset
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from paddleaudio import load as load_audio
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from paddlespeech.s2t.utils.log import Log
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logger = Log(__name__).getlog()
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# the audio meta info in the vector CSVDataset
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# utt_id: the utterance segment name
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# duration: utterance segment time
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# wav: utterance file path
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# start: start point in the original wav file
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# stop: stop point in the original wav file
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# lab_id: the utterance segment's label id
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@dataclass
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class meta_info:
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utt_id: str
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duration: float
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wav: str
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start: int
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stop: int
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lab_id: str
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class VoxCelebDataset(Dataset):
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meta_info = collections.namedtuple(
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'META_INFO', ('id', 'duration', 'wav', 'start', 'stop', 'spk_id'))
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def __init__(self, csv_path, spk_id2label_path, config):
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class CSVDataset(Dataset):
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# meta_info = collections.namedtuple(
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# 'META_INFO', ('id', 'duration', 'wav', 'start', 'stop', 'spk_id'))
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def __init__(self, csv_path, spk_id2label_path=None, config=None):
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super().__init__()
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self.csv_path = csv_path
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self.spk_id2label_path = spk_id2label_path
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@ -32,34 +52,41 @@ class VoxCelebDataset(Dataset):
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def load_data_csv(self):
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data = []
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with open(self.csv_path, 'r') as rf:
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for line in rf.readlines()[1:]:
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audio_id, duration, wav, start, stop, spk_id = line.strip(
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).split(',')
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data.append(
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self.meta_info(audio_id,
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float(duration), wav,
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int(start), int(stop), spk_id))
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meta_info(audio_id,
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float(duration), wav,
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int(start), int(stop), spk_id))
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return data
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def load_speaker_to_label(self):
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if not self.spk_id2label_path:
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logger.warning("No speaker id to label file")
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return
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spk_id2label = {}
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with open(self.spk_id2label_path, 'r') as f:
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for line in f.readlines():
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spk_id, label = line.strip().split(' ')
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self.spk_id2label[spk_id] = int(label)
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spk_id2label[spk_id] = int(label)
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return spk_id2label
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def convert_to_record(self, idx: int):
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sample = self.data[idx]
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record = {}
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# To show all fields in a namedtuple: `type(sample)._fields`
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for field in type(sample)._fields:
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record[field] = getattr(sample, field)
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for field in fields(sample):
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record[field.name] = getattr(sample, field.name)
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waveform, sr = load_audio(record['wav'])
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# random select a chunk audio samples from the audio
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if self.config.random_chunk:
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if self.config and self.config.random_chunk:
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num_wav_samples = waveform.shape[0]
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num_chunk_samples = int(self.config.chunk_duration * sr)
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start = random.randint(0, num_wav_samples - num_chunk_samples - 1)
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@ -71,46 +98,9 @@ class VoxCelebDataset(Dataset):
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# we only return the waveform as feat
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waveform = waveform[start:stop]
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record.update({'feat': waveform})
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record.update({'label': self.spk_id2label[record['spk_id']]})
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return record
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def __getitem__(self, idx):
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return self.convert_to_record(idx)
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def __len__(self):
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return len(self.data)
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class RIRSNoiseDataset(Dataset):
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meta_info = collections.namedtuple('META_INFO', ('id', 'duration', 'wav'))
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def __init__(self, csv_path):
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super().__init__()
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self.csv_path = csv_path
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self.data = self.load_csv_data()
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if self.spk_id2label:
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record.update({'label': self.spk_id2label[record['lab_id']]})
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def load_csv_data(self):
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data = []
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with open(self.csv_path, 'r') as rf:
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for line in rf.readlines()[1:]:
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audio_id, duration, wav = line.strip().split(',')
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data.append(self.meta_info(audio_id, float(duration), wav))
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random.shuffle(data)
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return data
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def convert_to_record(self, idx: int):
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sample = self.data[idx]
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record = {}
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# To show all fields in a namedtuple: `type(sample)._fields`
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for field in type(sample)._fields:
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record[field] = getattr(sample, field)
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waveform, sr = load_audio(record['wav'])
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record.update({'feat': waveform})
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return record
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def __getitem__(self, idx):
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xiongxinlei
3 years ago