New dataset Kusal from Mycroft

datasets/Kusal.py

@@ -0,0 +1,157 @@
+import os
+import glob
+import random
+import numpy as np
+import collections
+import librosa
+import torch
+from torch.utils.data import Dataset
+
+from utils.text import text_to_sequence
+from utils.data import (prepare_data, pad_per_step,
+                            prepare_tensor, prepare_stop_target)
+
+
+class MyDataset(Dataset):
+
+    def __init__(self, root_dir, csv_file, outputs_per_step,
+                 text_cleaner, ap, min_seq_len=0):
+        self.root_dir = root_dir
+        self.wav_dir = os.path.join(root_dir, 'wavs')
+        self.wav_files = glob.glob(os.path.join(self.wav_dir, '*.wav'))
+        self._create_file_dict()
+        self.csv_dir = os.path.join(root_dir, csv_file)
+        with open(self.csv_dir, "r", encoding="utf8") as f:
+            self.frames = [line.split('\t') for line in f if line.split('\t')[0] in self.wav_files_dict.keys()]
+        self.outputs_per_step = outputs_per_step
+        self.sample_rate = ap.sample_rate
+        self.cleaners = text_cleaner
+        self.min_seq_len = min_seq_len
+        self.ap = ap
+        print(" > Reading Kusal from - {}".format(root_dir))
+        print(" | > Number of instances : {}".format(len(self.frames)))
+        self._sort_frames()
+
+    def load_wav(self, filename):
+        """ Load audio and trim silence """
+        try:
+            audio = librosa.core.load(filename, sr=self.sample_rate)[0]
+            margin = int(self.sample_rate * 0.1)
+            audio = audio[margin:-margin]
+            return self._trim_silence(audio)
+        except RuntimeError as e:
+            print(" !! Cannot read file : {}".format(filename))
+
+    def _trim_silence(self, wav):
+         return librosa.effects.trim(
+             wav, top_db=40,
+             frame_length=1024,
+             hop_length=256)[0]
+
+    def _create_file_dict(self):
+        self.wav_files_dict = {}
+        for fn in self.wav_files:
+            parts = fn.split('-')
+            key = parts[1]
+            value = fn
+            try:
+                self.wav_files_dict[key].append(value)
+            except:
+                self.wav_files_dict[key] = [value]
+
+    def _sort_frames(self):
+        r"""Sort sequences in ascending order"""
+        lengths = np.array([len(ins[2]) for ins in self.frames])
+
+        print(" | > Max length sequence {}".format(np.max(lengths)))
+        print(" | > Min length sequence {}".format(np.min(lengths)))
+        print(" | > Avg length sequence {}".format(np.mean(lengths)))
+
+        idxs = np.argsort(lengths)
+        new_frames = []
+        ignored = []
+        for i, idx in enumerate(idxs):
+            length = lengths[idx]
+            if length < self.min_seq_len:
+                ignored.append(idx)
+            else:
+                new_frames.append(self.frames[idx])
+        print(" | > {} instances are ignored by min_seq_len ({})".format(
+            len(ignored), self.min_seq_len))
+        self.frames = new_frames
+
+    def __len__(self):
+        return len(self.frames)
+
+    def __getitem__(self, idx):
+        sidx = self.frames[idx][0]
+        sidx_files = self.wav_files_dict[sidx]
+        file_name = random.choice(sidx_files)
+        wav_name = os.path.join(self.wav_dir,
+                                file_name)
+        text = self.frames[idx][2]
+        text = np.asarray(text_to_sequence(
+            text, [self.cleaners]), dtype=np.int32)
+        wav = np.asarray(self.load_wav(wav_name), dtype=np.float32)
+        sample = {'text': text, 'wav': wav, 'item_idx': self.frames[idx][0]}
+        return sample
+
+    def collate_fn(self, batch):
+        r"""
+            Perform preprocessing and create a final data batch:
+            1. PAD sequences with the longest sequence in the batch
+            2. Convert Audio signal to Spectrograms.
+            3. PAD sequences that can be divided by r.
+            4. Convert Numpy to Torch tensors.
+        """
+
+        # Puts each data field into a tensor with outer dimension batch size
+        if isinstance(batch[0], collections.Mapping):
+            keys = list()
+
+            wav = [d['wav'] for d in batch]
+            item_idxs = [d['item_idx'] for d in batch]
+            text = [d['text'] for d in batch]
+
+            text_lenghts = np.array([len(x) for x in text])
+            max_text_len = np.max(text_lenghts)
+
+            linear = [self.ap.spectrogram(w).astype('float32') for w in wav]
+            mel = [self.ap.melspectrogram(w).astype('float32') for w in wav]
+            mel_lengths = [m.shape[1] + 1 for m in mel]  # +1 for zero-frame
+
+            # compute 'stop token' targets
+            stop_targets = [np.array([0.]*(mel_len-1))
+                            for mel_len in mel_lengths]
+
+            # PAD stop targets
+            stop_targets = prepare_stop_target(
+                stop_targets, self.outputs_per_step)
+
+            # PAD sequences with largest length of the batch
+            text = prepare_data(text).astype(np.int32)
+            wav = prepare_data(wav)
+
+            # PAD features with largest length + a zero frame
+            linear = prepare_tensor(linear, self.outputs_per_step)
+            mel = prepare_tensor(mel, self.outputs_per_step)
+            assert mel.shape[2] == linear.shape[2]
+            timesteps = mel.shape[2]
+
+            # B x T x D
+            linear = linear.transpose(0, 2, 1)
+            mel = mel.transpose(0, 2, 1)
+
+            # convert things to pytorch
+            text_lenghts = torch.LongTensor(text_lenghts)
+            text = torch.LongTensor(text)
+            linear = torch.FloatTensor(linear)
+            mel = torch.FloatTensor(mel)
+            mel_lengths = torch.LongTensor(mel_lengths)
+            stop_targets = torch.FloatTensor(stop_targets)
+
+            return text, text_lenghts, linear, mel, mel_lengths, stop_targets, item_idxs[0]
+
+        raise TypeError(("batch must contain tensors, numbers, dicts or lists;\
+                         found {}"
+                         .format(type(batch[0]))))