lang fixes / reworked phoneme symmap validation

vall_e/data.py

@@ -411,6 +411,8 @@ def get_lang_symmap():
 	return {
 		"en": 0,
 		"ja": 1,
+		"de": 2,
+		"fr": 3,
 	}
 
 def get_tone_symmap():
@@ -751,14 +753,14 @@ class Dataset(_Dataset):
 		res = cfg.get_spkr_group(path)
 		return res
 
-	def get_language(self, speaker_group):
-		lang = "en"
+	# this isn't really necessary since our data/metadata contains markers for languages, but this is still in in-case it's needed to force a language setting (for example, whisperX's lang isn't that accurate at times)
+	def get_language(self, speaker_group, lang="en"):
 		for k, v in cfg.dataset.speaker_languages.items():
 			if speaker_group in v:
 				lang = k
 				break
 
-		return lang
+		return lang.lower()
 
 	@cached_property
 	def spkrs(self):
@@ -877,7 +879,7 @@ class Dataset(_Dataset):
 
 	def get_similar_utterance(self, spkr_name, reference, offset=0 ):
 		# lots of boilerplate checks
-		metadata_path = Path(f"{metadata_root}/{speaker_name}.json")
+		metadata_path = cfg.metadata_dir / f"{spkr_name}.json"
 		if not metadata_path.exists():
 			return None
 		metadata = json_read( metadata_path )
@@ -890,7 +892,8 @@ class Dataset(_Dataset):
 			offset = -1
 		metadata_keys = list(metadata.keys())
 		index = reference_metadata["similar"][offset]
-		return metadata_keys[index]
+		name = metadata_keys[index]
+		return name
 
 	def sample_prompts(self, spkr_name, reference, should_trim=True):
 		if not cfg.dataset.prompt_duration_range or cfg.dataset.prompt_duration_range[-1] == 0:
@@ -996,8 +999,7 @@ class Dataset(_Dataset):
 			tone = metadata["tone"] if "tone" in metadata else None
 			text_string = metadata["text"] if "text" in metadata else None
 
-		if not lang:
-			lang = self.get_language(spkr_group)
+		lang = self.get_language(spkr_group) if not lang else lang.lower()
 		
 		if not tone:
 			tone = "neutral"
@@ -1642,9 +1644,11 @@ if __name__ == "__main__":
 
 		missing = set()
 
-		for i in range(len( train_dl.dataset )):
-			batch = train_dl.dataset[i]
+		dataset = train_dl.dataset
 
+		for index in tqdm(range(len( dataset )), desc="Processing dataset..."):
+			"""
+			batch = train_dl.dataset[i]
 			text = batch['text']
 			phonemes = batch['metadata']['phonemes']
 
@@ -1657,6 +1661,47 @@ if __name__ == "__main__":
 
 				_logger.info( f"{batch['text']}: {batch['metadata']['phonemes']}" )
 
+				missing |= set([phone])
+			"""
+
+			if dataset.sampler_type == "group":
+				spkr_group = dataset.spkr_groups[index]
+				#spkr_group_id = dataset.spkr_group_symmap[spkr_group]
+				spkr_name = dataset.spkr_samplers[spkr_group].sample()
+				spkr_id = dataset.spkr_symmap[spkr_name]
+				path = dataset.samplers[spkr_name].sample()
+			elif dataset.sampler_type == "speaker":
+				spkr_name = dataset.spkrs[index]
+				spkr_id = dataset.spkr_symmap[spkr_name]
+				path = dataset.samplers[spkr_name].sample()
+				spkr_group = dataset.get_speaker_group(path)
+				#spkr_group_id = dataset.spkr_group_symmap[spkr_group]
+			else:
+				path = dataset.paths[index]
+				spkr_name = dataset.get_speaker(path)
+				spkr_id = dataset.spkr_symmap[spkr_name]
+				spkr_group = dataset.get_speaker_group(path)
+				#spkr_group_id = dataset.spkr_group_symmap[spkr_group]
+
+			if cfg.dataset.use_hdf5:
+				key = _get_hdf5_path(path)
+				if key not in cfg.hdf5:
+					continue
+				metadata = { f'{k}': f'{v}' for k, v in cfg.hdf5[key].attrs.items() }
+				text = cfg.hdf5[key]["text"][:]				
+			else:
+				_, metadata = _load_quants(path, return_metadata=True)
+				text = tokenize( phonemes )
+			phonemes = metadata["phonemes"]
+
+			for i, token in enumerate(text):
+				if token != "<unk>":
+					continue
+				
+				phone = phonemes[i]
+
+				_logger.info( f"{path}: {phonemes}: {phone}" )
+
 				missing |= set([phone])
 
 		_logger.info( f"Missing tokens: {missing}" )

vall_e/emb/similar.py

@@ -189,6 +189,7 @@ def process(
 	if top_k == 0:
 		return
 
+	# fill any missing keys with a null embedding to keep the order the same
 	null_embedding = torch.zeros( (1024,), device=tts.device, dtype=tts.dtype )
 	embeddings = torch.stack( [ feature if feature is not None else null_embedding for feature in features.values()  ] )
 	sorted_similarities = {}