Efficient Conformer

requirements.txt

+torch
+numpy
+torchaudio
+tqdm
+sentencepiece
+jiwer
+tensorboard
+https://github.com/kpu/kenlm/archive/master.zip
+warp_rnnt
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utils/datasets.py

+# Copyright 2021, Maxime Burchi.
+#
+# 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.
+
+# PyTorch
+import torch
+import torchaudio
+
+# Sentencepiece
+import sentencepiece as spm
+
+# Other
+import glob
+from tqdm import tqdm
+
+# Librispeech 292.367 samples
+class LibriSpeechDataset(torch.utils.data.Dataset): 
+    def __init__(self, dataset_path, training_params, tokenizer_params, split, args):
+
+        self.names = glob.glob(dataset_path + split + "*/*/*/*.flac")
+        self.vocab_type = tokenizer_params["vocab_type"]
+        self.vocab_size = str(tokenizer_params["vocab_size"])
+        self.lm_mode = training_params.get("lm_mode", False)
+
+        if split.split("-")[0] == "train":
+            self.names = self.filter_lengths(training_params["train_audio_max_length"], training_params["train_label_max_length"], args.rank)
+        else:
+            self.names = self.filter_lengths(training_params["eval_audio_max_length"], training_params["eval_audio_max_length"], args.rank)
+
+    def __getitem__(self, i):
+
+        if self.lm_mode:
+            return [torch.load(self.names[i].split(".flac")[0].split("_")[0] + "." + self.vocab_type + "_" + self.vocab_size)]
+        else:
+            return [torchaudio.load(self.names[i])[0], torch.load(self.names[i].split(".flac")[0].split("_")[0] + "." + self.vocab_type + "_" + self.vocab_size)]
+
+    def __len__(self):
+
+        return len(self.names)
+
+    def filter_lengths(self, audio_max_length, label_max_length, rank=0):
+
+        if audio_max_length is None or label_max_length is None:
+            return self.names
+
+        if rank == 0:
+            print("LibriSpeech dataset filtering")
+            print("Audio maximum length : {} / Label sequence maximum length : {}".format(audio_max_length, label_max_length))
+            self.names = tqdm(self.names)
+
+        return [name for name in self.names if torch.load(name + "_len") <= audio_max_length and torch.load(name.replace("flac", self.vocab_type + "_" + self.vocab_size + "_len")) <= label_max_length]
+            
+# Librispeech Corpus 40.418.261 samples
+class LibriSpeechCorpusDataset(torch.utils.data.Dataset): 
+    def __init__(self, dataset_path, training_params, tokenizer_params, split, args):
+
+        # Dataset Params
+        self.tokenizer = spm.SentencePieceProcessor(tokenizer_params["tokenizer_path"])
+        self.corpus = open(dataset_path, 'r').readlines()
+        self.max_len = training_params["train_label_max_length"]
+
+    def __getitem__(self, i):
+
+        if self.max_len:
+            while len(self.tokenizer.encode(self.corpus[i][:-1].lower())) > self.max_len:
+                i = torch.randint(0, self.__len__(), [])
+
+        return [torch.LongTensor(self.tokenizer.encode(self.corpus[i][:-1].lower()))]
+
+    def __len__(self):
+
+        return len(self.corpus)
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