add max_workers, textgrids

libritts-aligned.py

@@ -12,6 +12,7 @@ from alignments.datasets.libritts import LibrittsDataset
 from tqdm.contrib.concurrent import process_map
 from tqdm.auto import tqdm
 from multiprocessing import cpu_count
+import multiprocessing as mp
 from phones.convert import Converter
 import torchaudio
 import torchaudio.transforms as AT
@@ -22,7 +23,12 @@ _PHONESET = "arpabet"
 
 _VERBOSE = os.environ.get("LIBRITTS_VERBOSE", True)
 _MAX_WORKERS = os.environ.get("LIBRITTS_MAX_WORKERS", cpu_count())
+_MAX_WORKERS = int(_MAX_WORKERS)
 _PATH = os.environ.get("LIBRITTS_PATH", os.environ.get("HF_DATASETS_CACHE", None))
+_DOWNLOAD_SPLITS = os.environ.get(
+    "LIBRITTS_DOWNLOAD_SPLITS",
+    "train-clean-100,train-clean-360,train-other-500,dev-clean,dev-other,test-clean,test-other",
+).split(",")
 if _PATH is not None and not os.path.exists(_PATH):
     os.makedirs(_PATH)
 
@@ -56,6 +62,7 @@ _URLS = {
     "train-clean-360": _URL + "train-clean-360.tar.gz",
     "train-other-500": _URL + "train-other-500.tar.gz",
 }
+_URLS = {k: v for k, v in _URLS.items() if k in _DOWNLOAD_SPLITS}
 
 
 class LibriTTSAlignConfig(datasets.BuilderConfig):
@@ -72,11 +79,16 @@ class LibriTTSAlignConfig(datasets.BuilderConfig):
         self.sampling_rate = sampling_rate
         self.hop_length = hop_length
         self.win_length = win_length
-        
+
         if _PATH is None:
-            raise ValueError("Please set the environment variable LIBRITTS_PATH to point to the LibriTTS dataset directory.")
+            raise ValueError(
+                "Please set the environment variable LIBRITTS_PATH to point to the LibriTTS dataset directory."
+            )
         elif _PATH == os.environ.get("HF_DATASETS_CACHE", None):
-            logger.warning("Please set the environment variable LIBRITTS_PATH to point to the LibriTTS dataset directory. Using HF_DATASETS_CACHE as a fallback.")
+            logger.warning(
+                "Please set the environment variable LIBRITTS_PATH to point to the LibriTTS dataset directory. Using HF_DATASETS_CACHE as a fallback."
+            )
+
 
 class LibriTTSAlign(datasets.GeneratorBasedBuilder):
     """LibriTTSAlign dataset."""
@@ -99,7 +111,7 @@ class LibriTTSAlign(datasets.GeneratorBasedBuilder):
             "phones": datasets.Sequence(datasets.Value("string")),
             "phone_durations": datasets.Sequence(datasets.Value("int32")),
             # audio feature
-            "audio": datasets.Value("string")
+            "audio": datasets.Value("string"),
         }
 
         return datasets.DatasetInfo(
@@ -117,64 +129,98 @@ class LibriTTSAlign(datasets.GeneratorBasedBuilder):
             ds_dict[name] = self._create_alignments_ds(name, url)
         splits = [
             datasets.SplitGenerator(
-                name=key.replace("-", "."),
-                gen_kwargs={"ds": self._create_data(value)}
-            ) 
+                name=key.replace("-", "."), gen_kwargs={"ds": self._create_data(value)}
+            )
             for key, value in ds_dict.items()
         ]
         # dataframe with all data
-        data_train = self._create_data([ds_dict["train-clean-100"], ds_dict["train-clean-360"], ds_dict["train-other-500"]])
-        data_dev = self._create_data([ds_dict["dev-clean"], ds_dict["dev-other"]])
-        data_test = self._create_data([ds_dict["test-clean"], ds_dict["test-other"]])
-        data_all = pd.concat([data_train, data_dev, data_test])
-        splits += [
-            datasets.SplitGenerator(
-                name="train.all",
-                gen_kwargs={
-                    "ds": data_all,
-                }
-            ),
-            datasets.SplitGenerator(
-                name="dev.all",
-                gen_kwargs={
-                    "ds": data_dev,
-                }
-            ),
-            datasets.SplitGenerator(
-                name="test.all",
-                gen_kwargs={
-                    "ds": data_test,
-                }
-            ),
-        ]
-        # move last row for each speaker from data_all to dev dataframe
-        data_dev = data_all.copy()
-        data_dev = data_dev.sort_values(by=["speaker", "audio"])
-        data_dev = data_dev.groupby("speaker").tail(1)
-        data_dev = data_dev.reset_index()
-        # remove last row for each speaker from data_all
-        data_all = data_all[~data_all["audio"].isin(data_dev["audio"])]
-        splits += [
-            datasets.SplitGenerator(
-                name="train",
-                gen_kwargs={
-                    "ds": data_all,
-                }
-            ),
-            datasets.SplitGenerator(
-                name="dev",
-                gen_kwargs={
-                    "ds": data_dev,
-                }
-            ),
-        ]
+        data_train, data_dev, data_test, data_all = None, None, None, None
+        if (
+            "train-clean-100" in _URLS
+            and "train-clean-360" in _URLS
+            and "train-other-500" in _URLS
+        ):
+            data_train = self._create_data(
+                [
+                    ds_dict["train-clean-100"],
+                    ds_dict["train-clean-360"],
+                    ds_dict["train-other-500"],
+                ]
+            )
+        if "dev-clean" in _URLS and "dev-other" in _URLS:
+            data_dev = self._create_data([ds_dict["dev-clean"], ds_dict["dev-other"]])
+        if "test-clean" in _URLS and "test-other" in _URLS:
+            data_test = self._create_data(
+                [ds_dict["test-clean"], ds_dict["test-other"]]
+            )
+        if (
+            "train-clean-100" in _URLS
+            and "train-clean-360" in _URLS
+            and "train-other-500" in _URLS
+            and "dev-clean" in _URLS
+            and "dev-other" in _URLS
+            and "test-clean" in _URLS
+            and "test-other" in _URLS
+        ):
+            data_all = pd.concat([data_train, data_dev, data_test])
+        if data_all is not None:
+            splits.append(
+                datasets.SplitGenerator(
+                    name="train.all",
+                    gen_kwargs={
+                        "ds": data_all,
+                    },
+                )
+            )
+        if data_dev is not None:
+            splits.append(
+                datasets.SplitGenerator(
+                    name="dev.all",
+                    gen_kwargs={
+                        "ds": data_dev,
+                    },
+                )
+            )
+        if data_test is not None:
+            splits.append(
+                datasets.SplitGenerator(
+                    name="test.all",
+                    gen_kwargs={
+                        "ds": data_test,
+                    },
+                )
+            )
+        if data_dev is not None and data_all is not None:
+            # move last row for each speaker from data_all to dev dataframe
+            data_dev = data_all.copy()
+            data_dev = data_dev.sort_values(by=["speaker", "audio"])
+            data_dev = data_dev.groupby("speaker").tail(1)
+            data_dev = data_dev.reset_index()
+            # remove last row for each speaker from data_all
+            data_all = data_all[~data_all["audio"].isin(data_dev["audio"])]
+            splits += [
+                datasets.SplitGenerator(
+                    name="train",
+                    gen_kwargs={
+                        "ds": data_all,
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="dev",
+                    gen_kwargs={
+                        "ds": data_dev,
+                    },
+                ),
+            ]
         self.alignments_ds = None
         self.data = None
         return splits
 
     def _create_alignments_ds(self, name, url):
         self.empty_textgrids = 0
-        ds_hash = hashlib.md5(os.path.join(_PATH, f"{name}-alignments").encode()).hexdigest()
+        ds_hash = hashlib.md5(
+            os.path.join(_PATH, f"{name}-alignments").encode()
+        ).hexdigest()
         pkl_path = os.path.join(_PATH, f"{ds_hash}.pkl")
         if os.path.exists(pkl_path):
             ds = pickle.load(open(pkl_path, "rb"))
@@ -190,9 +236,11 @@ class LibriTTSAlign(datasets.GeneratorBasedBuilder):
                 target_directory=tgt_dir,
                 source_directory=src_dir,
                 source_url=url,
+                textgrid_url=f"https://huggingface.co/datasets/cdminix/libritts-aligned/resolve/main/data/{name.replace('-', '_')}.tar.gz",
                 verbose=_VERBOSE,
                 tmp_directory=os.path.join(_PATH, f"{name}-tmp"),
                 chunk_size=1000,
+                n_workers=_MAX_WORKERS,
             )
             pickle.dump(ds, open(pkl_path, "wb"))
         return ds, ds_hash
@@ -209,7 +257,9 @@ class LibriTTSAlign(datasets.GeneratorBasedBuilder):
         del data
         for i, ds in enumerate(ds):
             if os.path.exists(os.path.join(_PATH, f"{hashes[i]}-entries.pkl")):
-                add_entries = pickle.load(open(os.path.join(_PATH, f"{hashes[i]}-entries.pkl"), "rb"))
+                add_entries = pickle.load(
+                    open(os.path.join(_PATH, f"{hashes[i]}-entries.pkl"), "rb")
+                )
             else:
                 add_entries = [
                     entry
@@ -223,7 +273,10 @@ class LibriTTSAlign(datasets.GeneratorBasedBuilder):
                     )
                     if entry is not None
                 ]
-                pickle.dump(add_entries, open(os.path.join(_PATH, f"{hashes[i]}-entries.pkl"), "wb"))
+                pickle.dump(
+                    add_entries,
+                    open(os.path.join(_PATH, f"{hashes[i]}-entries.pkl"), "wb"),
+                )
             entries += add_entries
         if self.empty_textgrids > 0:
             logger.warning(f"Found {self.empty_textgrids} empty textgrids")
@@ -259,9 +312,7 @@ class LibriTTSAlign(datasets.GeneratorBasedBuilder):
             if "[" not in phone:
                 o_phone = phone
                 if o_phone not in self.phone_cache:
-                    phone = self.phone_converter(
-                        phone, _PHONESET, lang=None
-                    )[0]
+                    phone = self.phone_converter(phone, _PHONESET, lang=None)[0]
                     self.phone_cache[o_phone] = phone
                 phone = self.phone_cache[o_phone]
             phones.append(phone)
@@ -304,4 +355,4 @@ class LibriTTSAlign(datasets.GeneratorBasedBuilder):
                         "audio": str(row["audio"]),
                     }
                     yield j, result
-                    j += 1
+                    j += 1