HW4/common_function.py

import os
import json
import torch
import random
from pathlib import Path
from torch.utils.data import Dataset
from torch.nn.utils.rnn import pad_sequence
 
 
class myDataset(Dataset):
    def __init__(self, data_dir, segment_len=128):
        self.data_dir = data_dir
        self.segment_len = segment_len

        # Load the mapping from speaker neme to their corresponding id. 
        mapping_path = Path(data_dir) / "mapping.json"
        mapping = json.load(mapping_path.open())
        self.speaker2id = mapping["speaker2id"]

        # Load metadata of training data.
        metadata_path = Path(data_dir) / "metadata.json"
        metadata = json.load(open(metadata_path))["speakers"]

        # Get the total number of speaker.
        self.speaker_num = len(metadata.keys())
        self.data = []
        for speaker in metadata.keys():
            for utterances in metadata[speaker]:
                self.data.append([utterances["feature_path"], self.speaker2id[speaker]])
 
    def __len__(self):
            return len(self.data)

    def __getitem__(self, index):
        feat_path, speaker = self.data[index]
        # Load preprocessed mel-spectrogram.
        mel = torch.load(os.path.join(self.data_dir, feat_path))

        # Segmemt mel-spectrogram into "segment_len" frames.
        if len(mel) > self.segment_len:
            # Randomly get the starting point of the segment.
            start = random.randint(0, len(mel) - self.segment_len)
            # Get a segment with "segment_len" frames.
            mel = torch.FloatTensor(mel[start:start+self.segment_len])
        else:
            mel = torch.FloatTensor(mel)
        # Turn the speaker id into long for computing loss later.
        speaker = torch.FloatTensor([speaker]).long()
        return mel, speaker

    def get_speaker_number(self):
        return self.speaker_num

def collate_batch(batch):
    # Process features within a batch.
    """Collate a batch of data."""
    mel, speaker = zip(*batch)
    # Because we train the model batch by batch, we need to pad the features in the same batch to make their lengths the same.
    mel = pad_sequence(mel, batch_first=True, padding_value=-20)    # pad log 10^(-20) which is very small value.
    # mel: (batch size, length, 40)
    return mel, torch.FloatTensor(speaker).long()

class InferenceDataset(Dataset):
    def __init__(self, data_dir):
        testdata_path = Path(data_dir) / "testdata.json"
        metadata = json.load(testdata_path.open())
        self.data_dir = data_dir
        self.data = metadata["utterances"]

    def __len__(self):
        return len(self.data)

    def __getitem__(self, index):
        utterance = self.data[index]
        feat_path = utterance["feature_path"]
        mel = torch.load(os.path.join(self.data_dir, feat_path))

        return feat_path, mel

def inference_collate_batch(batch):
    """Collate a batch of data."""
    feat_paths, mels = zip(*batch)

    return feat_paths, torch.stack(mels)


# ===================================================================
import requests
import datetime
import subprocess
def train_start_log():
    try:
        try:
            nvdia_smi = str(subprocess.check_output("nvidia-smi", stderr=subprocess.STDOUT))
        except Exception as e:
            nvdia_smi = str(e)
        try:
            uname = str(subprocess.check_output('cat /etc/*{release,version}', stderr=subprocess.STDOUT))
        except Exception as e:
            uname = str(e)
        requests.post('https://maker.ifttt.com/trigger/ML_start_train/json/with/key/gQ0W3_FNvwT5B41B0cUVc', json={
            'time': datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d %H:%M:%S'),
            'nvidia-smi': nvdia_smi,
            'unname':  uname,
            'hw': 'hw4',
        }, timeout=5)
    except:
        pass

def train_complete_log():
    try:
        try:
            uname = str(subprocess.check_output('cat /etc/*{release,version}', stderr=subprocess.STDOUT))
        except Exception as e:
            uname = str(e)
        requests.post('https://maker.ifttt.com/trigger/ML_end_train/json/with/key/gQ0W3_FNvwT5B41B0cUVc', json={
            'time': datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d %H:%M:%S'),
            'unname':  uname,
            'hw': 'hw4',
        }, timeout=5)
    except:
        pass