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import os
import pandas as pd
import torch
import torchaudio
import torch.nn.utils.rnn as rnn_utils
import whisper
def collate_fn(batch):
(seq, label) = zip(*batch)
seql = [x.reshape(-1,) for x in seq]
data = rnn_utils.pad_sequence(seql, batch_first=True, padding_value=0)
label = torch.tensor(list(label))
return data, label
def collate_mel_fn(batch):
(seq, label) = zip(*batch)
data = torch.stack([x.reshape(80, -1) for x in seq])
label = torch.tensor(list(label))
return data, label
class S2IDataset(torch.utils.data.Dataset):
def __init__(self, csv_path=None, wav_dir_path=None):
self.df = pd.read_csv(csv_path)
self.wav_dir = wav_dir_path
self.resmaple = torchaudio.transforms.Resample(8000, 16000)
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
row = self.df.iloc[idx]
intent_class = row["intent_class"]
wav_path = os.path.join(self.wav_dir, row["audio_path"])
speaker_id = row["speaker_id"]
template = row["template"]
wav_tensor, _= torchaudio.load(wav_path)
wav_tensor = self.resmaple(wav_tensor)
intent_class = int(intent_class)
return wav_tensor, intent_class
class S2IMELDataset(torch.utils.data.Dataset):
def __init__(self, csv_path=None, wav_dir_path=None):
self.df = pd.read_csv(csv_path)
self.wav_dir = wav_dir_path
self.resmaple = torchaudio.transforms.Resample(8000, 16000)
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
row = self.df.iloc[idx]
intent_class = row["intent_class"]
wav_path = os.path.join(self.wav_dir, row["audio_path"])
speaker_id = row["speaker_id"]
template = row["template"]
wav_tensor, _= torchaudio.load(wav_path)
wav_tensor = self.resmaple(wav_tensor)
wav_tensor = whisper.pad_or_trim(wav_tensor.flatten())
mel = whisper.log_mel_spectrogram(wav_tensor)
intent_class = int(intent_class)
return mel, intent_class
if __name__ == "__main__":
dataset = S2IMELDataset(
csv_path="/root/Speech2Intent/dataset/speech-to-intent/train.csv",
wav_dir_path="/root/Speech2Intent/dataset/speech-to-intent/",
sr=16000)
wav_tensor, intent_class = dataset[0]
print(wav_tensor.shape, intent_class)
trainloader = torch.utils.data.DataLoader(
dataset,
batch_size=3,
shuffle=True,
num_workers=4,
collate_fn = collate_mel_fn,
)
x, y = next(iter(trainloader))
print(x.shape)
print(y.shape)
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