Fix CommonVoice for French (#1126)

Resolves #1125 where dataset metadata does not contain an extension.

test/torchaudio_unittest/datasets/commonvoice_test.py

-import os
 import csv
+import os
 from pathlib import Path
+from typing import Tuple, Dict
 
-from torchaudio.datasets import COMMONVOICE
+from torch import Tensor
 from torchaudio_unittest.common_utils import (
     TempDirMixin,
     TorchaudioTestCase,
@@ -11,47 +12,96 @@ from torchaudio_unittest.common_utils import (
     normalize_wav,
 )
 
+from torchaudio.datasets import COMMONVOICE
 
-class TestCommonVoice(TempDirMixin, TorchaudioTestCase):
-    backend = 'default'
+_ORIGINAL_EXT_AUDIO = COMMONVOICE._ext_audio
+_SAMPLE_RATE = 48000
+_HEADERS = [u"client_ids", u"path", u"sentence", u"up_votes", u"down_votes", u"age", u"gender", u"accent"]
 
-    root_dir = None
-    data = []
-    _headers = [u"client_ids", u"path", u"sentence", u"up_votes", u"down_votes", u"age", u"gender", u"accent"]
+
+def get_mock_dataset_en(root_dir) -> Tuple[Tensor, int, Dict[str, str]]:
+    mocked_data = []
     # Note: extension is changed to wav for the sake of test
     # Note: the first content is missing values for `age`, `gender` and `accent` as in the original data.
-    _train_csv_contents = [
+    _en_train_csv_contents = [
         ["9d16c5d980247861130e0480e2719f448be73d86a496c36d01a477cbdecd8cfd1399403d7a77bf458d211a70711b2da0845c",
-            "common_voice_en_18885784.wav",
-            "He was accorded a State funeral, and was buried in Drayton and Toowoomba Cemetery.", "2", "0", "", "", ""],
+         "common_voice_en_18885784.wav",
+         "He was accorded a State funeral, and was buried in Drayton and Toowoomba Cemetery.", "2", "0", "", "",
+         ""],
         ["c82eb9291328620f06025a1f8112b909099e447e485e99236cb87df008650250e79fea5ca772061fb6a370830847b9c44d20",
-            "common_voice_en_556542.wav", "Once more into the breach", "2", "0", "thirties", "male", "us"],
+         "common_voice_en_556542.wav", "Once more into the breach", "2", "0", "thirties", "male", "us"],
         ["f74d880c5ad4c5917f314a604d3fc4805159d255796fb9f8defca35333ecc002bdf53dc463503c12674ea840b21b4a507b7c",
-            "common_voice_en_18607573.wav",
-            "Caddy, show Miss Clare and Miss Summerson their rooms.", "2", "0", "twenties", "male", "canada"],
+         "common_voice_en_18607573.wav",
+         "Caddy, show Miss Clare and Miss Summerson their rooms.", "2", "0", "twenties", "male", "canada"],
+    ]
+    # Tsv file name difference does not mean different subset, testing as a whole dataset here
+    tsv_filename = os.path.join(root_dir, "train.tsv")
+    audio_base_path = os.path.join(root_dir, "clips")
+    os.makedirs(audio_base_path, exist_ok=True)
+    with open(tsv_filename, "w", newline='') as tsv:
+        writer = csv.writer(tsv, delimiter='\t')
+        writer.writerow(_HEADERS)
+        for i, content in enumerate(_en_train_csv_contents):
+            writer.writerow(content)
+            # Generate and store audio
+            audio_path = os.path.join(audio_base_path, content[1])
+            data = get_whitenoise(sample_rate=_SAMPLE_RATE, duration=1, n_channels=1, seed=i, dtype='float32')
+            save_wav(audio_path, data, _SAMPLE_RATE)
+            # Append data entry
+            mocked_data.append((normalize_wav(data), _SAMPLE_RATE, dict(zip(_HEADERS, content))))
+    return mocked_data
+
+
+def get_mock_dataset_fr(root_dir) -> Tuple[Tensor, int, Dict[str, str]]:
+    mocked_data = []
+    _fr_train_csv_contents = [
+        [
+            "a2e8e1e1cc74d08c92a53d7b9ff84e077eb90410edd85b8882f16fd037cecfcb6a19413c6c63ce6458cfea9579878fa91cef"
+            "18343441c601cae0597a4b0d3144",
+            "89e67e7682b36786a0b4b4022c4d42090c86edd96c78c12d30088e62522b8fe466ea4912e6a1055dfb91b296a0743e0a2bbe"
+            "16cebac98ee5349e3e8262cb9329",
+            "Or sur ce point nous n’avons aucune réponse de votre part.", "2", "0", "twenties", "male", "france"],
+        [
+            "a2e8e1e1cc74d08c92a53d7b9ff84e077eb90410edd85b8882f16fd037cecfcb6a19413c6c63ce6458cfea9579878fa91cef18"
+            "343441c601cae0597a4b0d3144",
+            "87d71819a26179e93acfee149d0b21b7bf5e926e367d80b2b3792d45f46e04853a514945783ff764c1fc237b4eb0ee2b0a7a7"
+            "cbd395acbdfcfa9d76a6e199bbd",
+            "Monsieur de La Verpillière, laissez parler le ministre", "2", "0", "twenties", "male", "france"],
+
     ]
-    sample_rate = 48000
+    # Tsv file name difference does not mean different subset, testing as a whole dataset here
+    tsv_filename = os.path.join(root_dir, "train.tsv")
+    audio_base_path = os.path.join(root_dir, "clips")
+    os.makedirs(audio_base_path, exist_ok=True)
+    with open(tsv_filename, "w", newline='') as tsv:
+        writer = csv.writer(tsv, delimiter='\t')
+        writer.writerow(_HEADERS)
+        for i, content in enumerate(_fr_train_csv_contents):
+            content[2] = str(content[2].encode("utf-8"))
+            writer.writerow(content)
+            # Generate and store audio
+            audio_path = os.path.join(audio_base_path, content[1] + _ORIGINAL_EXT_AUDIO)
+            data = get_whitenoise(sample_rate=_SAMPLE_RATE, duration=1, n_channels=1, seed=i, dtype='float32')
+            save_wav(audio_path, data, _SAMPLE_RATE)
+
+            # Append data entry
+            mocked_data.append((normalize_wav(data), _SAMPLE_RATE, dict(zip(_HEADERS, content))))
+    return mocked_data
+
+
+class TestCommonVoiceEN(TempDirMixin, TorchaudioTestCase):
+    backend = 'default'
+    root_dir = None
 
     @classmethod
     def setUpClass(cls):
         cls.root_dir = cls.get_base_temp_dir()
-        # Tsv file name difference does not mean different subset, testing as a whole dataset here
-        tsv_filename = os.path.join(cls.root_dir, "train.tsv")
-        audio_base_path = os.path.join(cls.root_dir, "clips")
-        os.makedirs(audio_base_path, exist_ok=True)
-        with open(tsv_filename, "w", newline='') as tsv:
-            writer = csv.writer(tsv, delimiter='\t')
-            writer.writerow(cls._headers)
-            for i, content in enumerate(cls._train_csv_contents):
-                writer.writerow(content)
-
-                # Generate and store audio
-                audio_path = os.path.join(audio_base_path, content[1])
-                data = get_whitenoise(sample_rate=cls.sample_rate, duration=1, n_channels=1, seed=i, dtype='float32')
-                save_wav(audio_path, data, cls.sample_rate)
-
-                # Append data entry
-                cls.data.append((normalize_wav(data), cls.sample_rate, dict(zip(cls._headers, content))))
+        cls.data = get_mock_dataset_en(cls.root_dir)
+        COMMONVOICE._ext_audio = ".wav"
+
+    @classmethod
+    def tearDownClass(cls):
+        COMMONVOICE._ext_audio = _ORIGINAL_EXT_AUDIO
 
     def _test_commonvoice(self, dataset):
         n_ite = 0
@@ -59,7 +109,7 @@ class TestCommonVoice(TempDirMixin, TorchaudioTestCase):
             expected_dictionary = self.data[i][2]
             expected_data = self.data[i][0]
             self.assertEqual(expected_data, waveform, atol=5e-5, rtol=1e-8)
-            assert sample_rate == TestCommonVoice.sample_rate
+            assert sample_rate == _SAMPLE_RATE
             assert dictionary == expected_dictionary
             n_ite += 1
         assert n_ite == len(self.data)
@@ -71,3 +121,33 @@ class TestCommonVoice(TempDirMixin, TorchaudioTestCase):
     def test_commonvoice_path(self):
         dataset = COMMONVOICE(Path(self.root_dir))
         self._test_commonvoice(dataset)
+
+
+class TestCommonVoiceFR(TempDirMixin, TorchaudioTestCase):
+    backend = 'default'
+    root_dir = None
+
+    @classmethod
+    def setUpClass(cls):
+        cls.root_dir = cls.get_base_temp_dir()
+        cls.data = get_mock_dataset_fr(cls.root_dir)
+        COMMONVOICE._ext_audio = ".mp3"
+
+    @classmethod
+    def tearDownClass(cls):
+        COMMONVOICE._ext_audio = _ORIGINAL_EXT_AUDIO
+
+    def _test_commonvoice(self, dataset):
+        n_ite = 0
+        for i, (waveform, sample_rate, dictionary) in enumerate(dataset):
+            expected_dictionary = self.data[i][2]
+            expected_data = self.data[i][0]
+            self.assertEqual(expected_data, waveform, atol=5e-5, rtol=1e-8)
+            assert sample_rate == _SAMPLE_RATE
+            assert dictionary == expected_dictionary
+            n_ite += 1
+        assert n_ite == len(self.data)
+
+    def test_commonvoice_str(self):
+        dataset = COMMONVOICE(self.root_dir)
+        self._test_commonvoice(dataset)

test/torchaudio_unittest/datasets/utils_test.py

-from torchaudio.datasets import utils as dataset_utils
-from torchaudio.datasets.commonvoice import COMMONVOICE
-
 from torchaudio_unittest.common_utils import (
-    TempDirMixin,
     TorchaudioTestCase,
     get_asset_path,
 )
 
+from torchaudio.datasets import utils as dataset_utils
+from torchaudio.datasets.commonvoice import COMMONVOICE
+
+original_ext_audio = COMMONVOICE._ext_audio
+
 
 class TestIterator(TorchaudioTestCase):
+    @classmethod
+    def setUpClass(cls):
+        COMMONVOICE._ext_audio = ".wav"
+
+    @classmethod
+    def tearDownClass(cls):
+        COMMONVOICE._ext_audio = original_ext_audio
+
     backend = 'default'
     path = get_asset_path('CommonVoice', 'cv-corpus-4-2019-12-10', 'tt')
 

torchaudio/datasets/commonvoice.py

-import os
 import csv
+import os
 import warnings
 from pathlib import Path
 from typing import List, Dict, Tuple, Union, Optional
 
-import torchaudio
 from torch import Tensor
 from torch.utils.data import Dataset
 
+import torchaudio
+
 
 def load_commonvoice_item(line: List[str],
                           header: List[str],
                           path: str,
-                          folder_audio: str) -> Tuple[Tensor, int, Dict[str, str]]:
+                          folder_audio: str,
+                          ext_audio: str) -> Tuple[Tensor, int, Dict[str, str]]:
     # Each line as the following data:
     # client_id, path, sentence, up_votes, down_votes, age, gender, accent
 
     assert header[1] == "path"
     fileid = line[1]
-
     filename = os.path.join(path, folder_audio, fileid)
-
+    if not filename.endswith(ext_audio):
+        filename += ext_audio
     waveform, sample_rate = torchaudio.load(filename)
 
     dic = dict(zip(header, line))
@@ -95,7 +97,7 @@ class COMMONVOICE(Dataset):
             ``up_votes``, ``down_votes``, ``age``, ``gender`` and ``accent``.
         """
         line = self._walker[n]
-        return load_commonvoice_item(line, self._header, self._path, self._folder_audio)
+        return load_commonvoice_item(line, self._header, self._path, self._folder_audio, self._ext_audio)
 
     def __len__(self) -> int:
         return len(self._walker)