Reapply "Full implementation of multi-dictionary support (openvpi#238)"

This reverts commit 9957ba1.

Author: agentasteriski

basics/base_binarizer.py

@@ -13,9 +13,8 @@
 from utils.hparams import hparams
 from utils.indexed_datasets import IndexedDatasetBuilder
 from utils.multiprocess_utils import chunked_multiprocess_run
-from utils.phoneme_utils import build_phoneme_list, locate_dictionary
+from utils.phoneme_utils import load_phoneme_dictionary
 from utils.plot import distribution_to_figure
-from utils.text_encoder import TokenTextEncoder
 
 
 class BinarizationError(Exception):
@@ -44,73 +43,88 @@ class BaseBinarizer:
             the phoneme set.
     """
 
-    def __init__(self, data_dir=None, data_attrs=None):
-        if data_dir is None:
-            data_dir = hparams['raw_data_dir']
-        if not isinstance(data_dir, list):
-            data_dir = [data_dir]
-
-        self.raw_data_dirs = [pathlib.Path(d) for d in data_dir]
+    def __init__(self, datasets=None, data_attrs=None):
+        if datasets is None:
+            datasets = hparams['datasets']
+        self.datasets = datasets
+        self.raw_data_dirs = [pathlib.Path(ds['raw_data_dir']) for ds in self.datasets]
         self.binary_data_dir = pathlib.Path(hparams['binary_data_dir'])
         self.data_attrs = [] if data_attrs is None else data_attrs
 
         self.binarization_args = hparams['binarization_args']
         self.augmentation_args = hparams.get('augmentation_args', {})
         self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
-        self.spk_map = None
-        self.spk_ids = hparams['spk_ids']
-        self.speakers = hparams['speakers']
+        self.spk_map = {}
+        self.spk_ids = None
         self.build_spk_map()
 
+        self.lang_map = {}
+        self.dictionaries = hparams['dictionaries']
+        self.build_lang_map()
+
         self.items = {}
         self.item_names: list = None
         self._train_item_names: list = None
         self._valid_item_names: list = None
 
-        self.phone_encoder = TokenTextEncoder(vocab_list=build_phoneme_list())
+        self.phoneme_dictionary = load_phoneme_dictionary()
         self.timestep = hparams['hop_size'] / hparams['audio_sample_rate']
 
     def build_spk_map(self):
-        assert isinstance(self.speakers, list), 'Speakers must be a list'
-        assert len(self.speakers) == len(self.raw_data_dirs), \
-            'Number of raw data dirs must equal number of speaker names!'
-        if len(self.spk_ids) == 0:
-            self.spk_ids = list(range(len(self.raw_data_dirs)))
-        else:
-            assert len(self.spk_ids) == len(self.raw_data_dirs), \
-                'Length of explicitly given spk_ids must equal the number of raw datasets.'
-        assert max(self.spk_ids) < hparams['num_spk'], \
-            f'Index in spk_id sequence {self.spk_ids} is out of range. All values should be smaller than num_spk.'
-
-        self.spk_map = {}
-        for spk_name, spk_id in zip(self.speakers, self.spk_ids):
+        spk_ids = [ds.get('spk_id') for ds in self.datasets]
+        assigned_spk_ids = {spk_id for spk_id in spk_ids if spk_id is not None}
+        idx = 0
+        for i in range(len(spk_ids)):
+            if spk_ids[i] is not None:
+                continue
+            while idx in assigned_spk_ids:
+                idx += 1
+            spk_ids[i] = idx
+            assigned_spk_ids.add(idx)
+        assert max(spk_ids) < hparams['num_spk'], \
+            f'Index in spk_id sequence {spk_ids} is out of range. All values should be smaller than num_spk.'
+
+        for spk_id, dataset in zip(spk_ids, self.datasets):
+            spk_name = dataset['speaker']
             if spk_name in self.spk_map and self.spk_map[spk_name] != spk_id:
                 raise ValueError(f'Invalid speaker ID assignment. Name \'{spk_name}\' is assigned '
                                  f'with different speaker IDs: {self.spk_map[spk_name]} and {spk_id}.')
             self.spk_map[spk_name] = spk_id
+        self.spk_ids = spk_ids
 
         print("| spk_map: ", self.spk_map)
 
-    def load_meta_data(self, raw_data_dir: pathlib.Path, ds_id, spk_id):
+    def build_lang_map(self):
+        assert len(self.dictionaries.keys()) <= hparams['num_lang'], \
+            'Number of languages must not be greater than num_lang!'
+        for dataset in self.datasets:
+            assert dataset['language'] in self.dictionaries, f'Unrecognized language name: {dataset["language"]}'
+
+        for lang_id, lang_name in enumerate(sorted(self.dictionaries.keys()), start=1):
+            self.lang_map[lang_name] = lang_id
+
+        print("| lang_map: ", self.lang_map)
+
+    def load_meta_data(self, raw_data_dir: pathlib.Path, ds_id, spk, lang) -> dict:
         raise NotImplementedError()
 
-    def split_train_valid_set(self, item_names):
+    def split_train_valid_set(self, prefixes: list):
         """
         Split the dataset into training set and validation set.
         :return: train_item_names, valid_item_names
         """
-        prefixes = {str(pr): 1 for pr in hparams['test_prefixes']}
+        prefixes = {str(pr): 1 for pr in prefixes}
         valid_item_names = {}
         # Add prefixes that specified speaker index and matches exactly item name to test set
         for prefix in deepcopy(prefixes):
-            if prefix in item_names:
+            if prefix in self.item_names:
                 valid_item_names[prefix] = 1
                 prefixes.pop(prefix)
         # Add prefixes that exactly matches item name without speaker id to test set
         for prefix in deepcopy(prefixes):
             matched = False
-            for name in item_names:
+            for name in self.item_names:
                 if name.split(':')[-1] == prefix:
                     valid_item_names[name] = 1
                     matched = True
@@ -119,15 +133,15 @@ def split_train_valid_set(self, item_names):
         # Add names with one of the remaining prefixes to test set
         for prefix in deepcopy(prefixes):
             matched = False
-            for name in item_names:
+            for name in self.item_names:
                 if name.startswith(prefix):
                     valid_item_names[name] = 1
                     matched = True
             if matched:
                 prefixes.pop(prefix)
         for prefix in deepcopy(prefixes):
             matched = False
-            for name in item_names:
+            for name in self.item_names:
                 if name.split(':')[-1].startswith(prefix):
                     valid_item_names[name] = 1
                     matched = True
@@ -143,7 +157,7 @@ def split_train_valid_set(self, item_names):
 
         valid_item_names = list(valid_item_names.keys())
         assert len(valid_item_names) > 0, 'Validation set is empty!'
-        train_item_names = [x for x in item_names if x not in set(valid_item_names)]
+        train_item_names = [x for x in self.item_names if x not in set(valid_item_names)]
         assert len(train_item_names) > 0, 'Training set is empty!'
 
         return train_item_names, valid_item_names
@@ -167,21 +181,34 @@ def meta_data_iterator(self, prefix):
 
     def process(self):
         # load each dataset
-        for ds_id, spk_id, data_dir in zip(range(len(self.raw_data_dirs)), self.spk_ids, self.raw_data_dirs):
-            self.load_meta_data(pathlib.Path(data_dir), ds_id=ds_id, spk_id=spk_id)
+        test_prefixes = []
+        for ds_id, dataset in enumerate(self.datasets):
+            items = self.load_meta_data(
+                pathlib.Path(dataset['raw_data_dir']),
+                ds_id=ds_id, spk=dataset['speaker'], lang=dataset['language']
+            )
+            self.items.update(items)
+            test_prefixes.extend(
+                f'{ds_id}:{prefix}'
+                for prefix in dataset.get('test_prefixes', [])
+            )
         self.item_names = sorted(list(self.items.keys()))
-        self._train_item_names, self._valid_item_names = self.split_train_valid_set(self.item_names)
+        self._train_item_names, self._valid_item_names = self.split_train_valid_set(test_prefixes)
 
         if self.binarization_args['shuffle']:
             random.shuffle(self.item_names)
 
         self.binary_data_dir.mkdir(parents=True, exist_ok=True)
 
-        # Copy spk_map and dictionary to binary data dir
+        # Copy spk_map, lang_map and dictionary to binary data dir
         spk_map_fn = self.binary_data_dir / 'spk_map.json'
         with open(spk_map_fn, 'w', encoding='utf-8') as f:
-            json.dump(self.spk_map, f)
-        shutil.copy(locate_dictionary(), self.binary_data_dir / 'dictionary.txt')
+            json.dump(self.spk_map, f, ensure_ascii=False)
+        lang_map_fn = self.binary_data_dir / 'lang_map.json'
+        with open(lang_map_fn, 'w', encoding='utf-8') as f:
+            json.dump(self.lang_map, f, ensure_ascii=False)
+        for lang, dict_path in hparams['dictionaries'].items():
+            shutil.copy(dict_path, self.binary_data_dir / f'dictionary-{lang}.txt')
         self.check_coverage()
 
         # Process valid set and train set
@@ -197,40 +224,47 @@ def process(self):
 
     def check_coverage(self):
         # Group by phonemes in the dictionary.
-        ph_required = set(build_phoneme_list())
-        phoneme_map = {}
-        for ph in ph_required:
-            phoneme_map[ph] = 0
-        ph_occurred = []
+        ph_idx_required = set(range(1, len(self.phoneme_dictionary)))
+        ph_idx_occurred = set()
+        ph_idx_count_map = {
+            idx: 0
+            for idx in ph_idx_required
+        }
 
         # Load and count those phones that appear in the actual data
         for item_name in self.items:
-            ph_occurred += self.items[item_name]['ph_seq']
-            if len(ph_occurred) == 0:
-                raise BinarizationError(f'Empty tokens in {item_name}.')
-        for ph in ph_occurred:
-            if ph not in ph_required:
-                continue
-            phoneme_map[ph] += 1
-        ph_occurred = set(ph_occurred)
+            ph_idx_occurred.update(self.items[item_name]['ph_seq'])
+            for idx in self.items[item_name]['ph_seq']:
+                ph_idx_count_map[idx] += 1
+        ph_count_map = {
+            self.phoneme_dictionary.decode_one(idx, scalar=False): count
+            for idx, count in ph_idx_count_map.items()
+        }
+
+        def display_phoneme(phoneme):
+            if isinstance(phoneme, tuple):
+                return f'({", ".join(phoneme)})'
+            return phoneme
 
         print('===== Phoneme Distribution Summary =====')
-        for i, key in enumerate(sorted(phoneme_map.keys())):
-            if i == len(ph_required) - 1:
+        keys = sorted(ph_count_map.keys(), key=lambda v: v[0] if isinstance(v, tuple) else v)
+        for i, key in enumerate(keys):
+            if i == len(ph_count_map) - 1:
                 end = '\n'
             elif i % 10 == 9:
                 end = ',\n'
             else:
                 end = ', '
-            print(f'\'{key}\': {phoneme_map[key]}', end=end)
+            key_disp = display_phoneme(key)
+            print(f'{key_disp}: {ph_count_map[key]}', end=end)
 
         # Draw graph.
-        x = sorted(phoneme_map.keys())
-        values = [phoneme_map[k] for k in x]
+        xs = [display_phoneme(k) for k in keys]
+        ys = [ph_count_map[k] for k in keys]
         plt = distribution_to_figure(
             title='Phoneme Distribution Summary',
             x_label='Phoneme', y_label='Number of occurrences',
-            items=x, values=values
+            items=xs, values=ys, rotate=len(self.dictionaries) > 1
         )
         filename = self.binary_data_dir / 'phoneme_distribution.jpg'
         plt.savefig(fname=filename,
@@ -239,19 +273,21 @@ def check_coverage(self):
         print(f'| save summary to \'{filename}\'')
 
         # Check unrecognizable or missing phonemes
-        if ph_occurred != ph_required:
-            unrecognizable_phones = ph_occurred.difference(ph_required)
-            missing_phones = ph_required.difference(ph_occurred)
-            raise BinarizationError('transcriptions and dictionary mismatch.\n'
-                                    f' (+) {sorted(unrecognizable_phones)}\n'
-                                    f' (-) {sorted(missing_phones)}')
+        if ph_idx_occurred != ph_idx_required:
+            missing_phones = sorted({
+                self.phoneme_dictionary.decode_one(idx, scalar=False)
+                for idx in ph_idx_required.difference(ph_idx_occurred)
+            }, key=lambda v: v[0] if isinstance(v, tuple) else v)
+            raise BinarizationError(
+                f'The following phonemes are not covered in transcriptions: {missing_phones}'
+            )
 
     def process_dataset(self, prefix, num_workers=0, apply_augmentation=False):
         args = []
         builder = IndexedDatasetBuilder(self.binary_data_dir, prefix=prefix, allowed_attr=self.data_attrs)
         total_sec = {k: 0.0 for k in self.spk_map}
         total_raw_sec = {k: 0.0 for k in self.spk_map}
-        extra_info = {'names': {}, 'spk_ids': {}, 'spk_names': {}, 'lengths': {}}
+        extra_info = {'names': {}, 'ph_texts': {}, 'spk_ids': {}, 'spk_names': {}, 'lengths': {}}
         max_no = -1
 
         for item_name, meta_data in self.meta_data_iterator(prefix):
@@ -271,6 +307,7 @@ def postprocess(_item):
                         extra_info[k] = {}
                     extra_info[k][item_no] = v.shape[0]
             extra_info['names'][item_no] = _item['name'].split(':', 1)[-1]
+            extra_info['ph_texts'][item_no] = _item['ph_text']
             extra_info['spk_ids'][item_no] = _item['spk_id']
             extra_info['spk_names'][item_no] = _item['spk_name']
             extra_info['lengths'][item_no] = _item['length']
@@ -287,6 +324,7 @@ def postprocess(_item):
                             extra_info[k] = {}
                         extra_info[k][aug_item_no] = v.shape[0]
                 extra_info['names'][aug_item_no] = aug_item['name'].split(':', 1)[-1]
+                extra_info['ph_texts'][aug_item_no] = aug_item['ph_text']
                 extra_info['spk_ids'][aug_item_no] = aug_item['spk_id']
                 extra_info['spk_names'][aug_item_no] = aug_item['spk_name']
                 extra_info['lengths'][aug_item_no] = aug_item['length']
@@ -315,6 +353,7 @@ def postprocess(_item):
         builder.finalize()
         if prefix == "train":
             extra_info.pop("names")
+            extra_info.pop('ph_texts')
             extra_info.pop("spk_names")
         with open(self.binary_data_dir / f"{prefix}.meta", "wb") as f:
             # noinspection PyTypeChecker

basics/base_exporter.py

@@ -1,4 +1,6 @@
 import json
+import pathlib
+import shutil
 from pathlib import Path
 from typing import Union
 
@@ -31,6 +33,18 @@ def build_spk_map(self) -> dict:
         else:
             return {}
 
+    # noinspection PyMethodMayBeStatic
+    def build_lang_map(self) -> dict:
+        lang_map_fn = pathlib.Path(hparams['work_dir']) / 'lang_map.json'
+        if lang_map_fn.exists():
+            with open(lang_map_fn, 'r', encoding='utf8') as f:
+                lang_map = json.load(f)
+            assert isinstance(lang_map, dict) and len(lang_map) > 0, 'Invalid or empty language map!'
+            assert len(lang_map) == len(set(lang_map.values())), 'Duplicate language id in language map!'
+            return lang_map
+        else:
+            return {}
+
     def build_model(self) -> nn.Module:
         """
         Creates an instance of nn.Module and load its state dict on the target device.
@@ -44,6 +58,19 @@ def export_model(self, path: Path):
         """
         raise NotImplementedError()
 
+    # noinspection PyMethodMayBeStatic
+    def export_dictionaries(self, path: Path):
+        dicts = hparams.get('dictionaries')
+        if dicts is not None:
+            for lang in dicts.keys():
+                fn = f'dictionary-{lang}.txt'
+                shutil.copy(pathlib.Path(hparams['work_dir']) / fn, path)
+                print(f'| export dictionary => {path / fn}')
+        else:
+            fn = 'dictionary.txt'
+            shutil.copy(pathlib.Path(hparams['work_dir']) / fn, path)
+            print(f'| export dictionary => {path / fn}')
+
     def export_attachments(self, path: Path):
         """
         Exports related files and configs (e.g. the dictionary) to the target directory.

basics/base_svs_infer.py

@@ -29,6 +29,7 @@ def __init__(self, device=None):
         self.device = device
         self.timestep = hparams['hop_size'] / hparams['audio_sample_rate']
         self.spk_map = {}
+        self.lang_map = {}
         self.model: torch.nn.Module = None
 
     def build_model(self, ckpt_steps=None) -> torch.nn.Module:
@@ -50,7 +51,11 @@ def load_speaker_mix(self, param_src: dict, summary_dst: dict,
         spk_mix_map = param_src.get(param_key)  # { spk_name: value } or { spk_name: "value value value ..." }
         dynamic = False
         if spk_mix_map is None:
-            # Get the first speaker
+            assert len(self.spk_map) == 1, (
+                "This is a multi-speaker model. "
+                "Please specify a speaker or speaker mix by --spk option."
+            )
+            # Get the only speaker
             for name in self.spk_map.keys():
                 spk_mix_map = {name: 1.0}
                 break