Fix examples/speech_recognition while using multi num_workers on mult…

…i GPU setup (#1454)

Summary:
#1308
tgt in AsrDataset is list of torch tensors and it cause SIGSEGV error because tgt has too many objects to create shared memory in multiprocessing of dataloaders.
Pull Request resolved: #1454

Differential Revision: D18929874

Pulled By: okhonko

fbshipit-source-id: 5582b126890a93177258f5e053f32d5c6d32e9ab

examples/speech_recognition/data/asr_dataset.py

@@ -56,6 +56,11 @@ def __init__(
         self.frame_length = frame_length
         self.frame_shift = frame_shift
 
+        self.s2s_collater = Seq2SeqCollater(
+            0, 1, pad_index=self.tgt_dict.pad(),
+            eos_index=self.tgt_dict.eos(), move_eos_to_beginning=True
+        )
+
     def __getitem__(self, index):
         import torchaudio
         import torchaudio.compliance.kaldi as kaldi
@@ -72,10 +77,6 @@ def __getitem__(self, index):
             frame_shift=self.frame_shift
         )
         output_cmvn = data_utils.apply_mv_norm(output)
-        self.s2s_collater = Seq2SeqCollater(
-            0, 1, pad_index=self.tgt_dict.pad(),
-            eos_index=self.tgt_dict.eos(), move_eos_to_beginning=True
-        )
 
         return {"id": index, "data": [output_cmvn.detach(), tgt_item]}
 

examples/speech_recognition/data/collaters.py

@@ -76,6 +76,8 @@ def collate(self, samples):
             target = s["data"][self.label_index]
             if isinstance(target, (np.ndarray, np.generic)):
                 target = torch.from_numpy(target).long()
+            elif isinstance(target, list):
+                target = torch.LongTensor(target)
 
             parsed_sample = {"id": s["id"], "source": source, "target": target}
             parsed_samples.append(parsed_sample)

examples/speech_recognition/tasks/speech_recognition.py

@@ -56,11 +56,11 @@ def get_asr_dataset_from_json(data_json_path, tgt_dict):
             speakers.append(m.group(1) + "_" + m.group(2))
         frame_sizes = [s[1]["input"]["length_ms"] for s in sorted_samples]
         tgt = [
-            torch.LongTensor([int(i) for i in s[1]["output"]["tokenid"].split(", ")])
+            [int(i) for i in s[1]["output"]["tokenid"].split(", ")]
             for s in sorted_samples
         ]
         # append eos
-        tgt = [torch.cat([t, torch.LongTensor([tgt_dict.eos()])]) for t in tgt]
+        tgt = [[*t, tgt_dict.eos()] for t in tgt]
         return AsrDataset(aud_paths, frame_sizes, tgt, tgt_dict, ids, speakers)