Add test for LibriSpeech dataset (#825)

Co-authored-by: Edward Lu <edlu@fb.com>

test/assets/LibriSpeech/dev-clean/1272/128104/1272-128104.trans.txt

-1272-128104-0000 MISTER QUILTER

test/datasets/datasets_test.py

 from torchaudio.datasets.commonvoice import COMMONVOICE
-from torchaudio.datasets.librispeech import LIBRISPEECH
 from torchaudio.datasets.speechcommands import SPEECHCOMMANDS
 from torchaudio.datasets.utils import diskcache_iterator, bg_iterator
 from torchaudio.datasets.vctk import VCTK
@@ -20,10 +19,6 @@ class TestDatasets(TorchaudioTestCase):
         data = VCTK(self.path)
         data[0]
 
-    def test_librispeech(self):
-        data = LIBRISPEECH(self.path, "dev-clean")
-        data[0]
-
     def test_ljspeech(self):
         data = LJSPEECH(self.path)
         data[0]

test/datasets/librispeech_test.py

+import os
+
+from torchaudio.datasets import librispeech
+
+from ..common_utils import (
+    TempDirMixin,
+    TorchaudioTestCase,
+    get_whitenoise,
+    save_wav,
+    normalize_wav,
+)
+
+# Used to generate a unique utterance for each dummy audio file
+NUMBERS = [
+    'ZERO',
+    'ONE',
+    'TWO',
+    'THREE',
+    'FOUR',
+    'FIVE',
+    'SIX',
+    'SEVEN',
+    'EIGHT',
+    'NINE'
+]
+
+
+class TestLibriSpeech(TempDirMixin, TorchaudioTestCase):
+    backend = 'default'
+
+    root_dir = None
+    samples = []
+
+    @classmethod
+    def setUpClass(cls):
+        cls.root_dir = cls.get_base_temp_dir()
+        dataset_dir = os.path.join(
+            cls.root_dir, librispeech.FOLDER_IN_ARCHIVE, librispeech.URL
+        )
+        os.makedirs(dataset_dir, exist_ok=True)
+        sample_rate = 16000  # 16kHz
+        seed = 0
+
+        for speaker_id in range(5):
+            speaker_path = os.path.join(dataset_dir, str(speaker_id))
+            os.makedirs(speaker_path, exist_ok=True)
+
+            for chapter_id in range(3):
+                chapter_path = os.path.join(speaker_path, str(chapter_id))
+                os.makedirs(chapter_path, exist_ok=True)
+                trans_content = []
+
+                for utterance_id in range(10):
+                    filename = f'{speaker_id}-{chapter_id}-{utterance_id:04d}.wav'
+                    path = os.path.join(chapter_path, filename)
+
+                    utterance = ' '.join(
+                        [NUMBERS[x] for x in [speaker_id, chapter_id, utterance_id]]
+                    )
+                    trans_content.append(
+                        f'{speaker_id}-{chapter_id}-{utterance_id:04d} {utterance}'
+                    )
+
+                    data = get_whitenoise(
+                        sample_rate=sample_rate,
+                        duration=0.01,
+                        n_channels=1,
+                        dtype='float32',
+                        seed=seed
+                    )
+                    save_wav(path, data, sample_rate)
+                    sample = (
+                        normalize_wav(data),
+                        sample_rate,
+                        utterance,
+                        speaker_id,
+                        chapter_id,
+                        utterance_id
+                    )
+                    cls.samples.append(sample)
+
+                    seed += 1
+
+                trans_filename = f'{speaker_id}-{chapter_id}.trans.txt'
+                trans_path = os.path.join(chapter_path, trans_filename)
+                with open(trans_path, 'w') as f:
+                    f.write('\n'.join(trans_content))
+
+    @classmethod
+    def tearDownClass(cls):
+        # In case of test failure
+        librispeech.LIBRISPEECH._ext_audio = '.flac'
+
+    def test_librispeech(self):
+        librispeech.LIBRISPEECH._ext_audio = '.wav'
+        dataset = librispeech.LIBRISPEECH(self.root_dir)
+        print(dataset._path)
+
+        num_samples = 0
+        for i, (
+            data, sample_rate, utterance, speaker_id, chapter_id, utterance_id
+        ) in enumerate(dataset):
+            self.assertEqual(data, self.samples[i][0], atol=5e-5, rtol=1e-8)
+            assert sample_rate == self.samples[i][1]
+            assert utterance == self.samples[i][2]
+            assert speaker_id == self.samples[i][3]
+            assert chapter_id == self.samples[i][4]
+            assert utterance_id == self.samples[i][5]
+            num_samples += 1
+
+        assert num_samples == len(self.samples)
+        librispeech.LIBRISPEECH._ext_audio = '.flac'