also do text similarities (dont know what use I'll have for this)

vall_e/emb/similar.py

@@ -40,14 +40,9 @@ def load_audio( path ):
 def process(
 	input_speaker,
 	yaml,
+	text=False,
 
 	audio_backend="encodec",
-	output_dataset="training",
-	raise_exceptions=False,
-	stride=0,
-	stride_offset=0,
-	slice="auto",
-
 	device="cuda",
 	dtype="float16",
 	amp=False,
@@ -55,7 +50,7 @@ def process(
 	verbose=False,
 ):
 	cfg.set_audio_backend(audio_backend)
-	audio_extension = cfg.audio_backend_extension
+	artifact_extension = cfg.audio_backend_extension
 
 	cfg.inference.weight_dtype = dtype # "bfloat16"
 	cfg.inference.amp = amp # False
@@ -74,18 +69,38 @@ def process(
 	for filename in tqdm(os.listdir(f'./{input_speaker}/'), desc="Encoding...", disable=not verbose):
 		extension = filename.split(".")[-1]
 
-		# treat embeddings as features, if provided quantized audio
-		if extension in audio_extension:
+
+		if text:
+			if extension not in artifact_extension:
+				raise Exception("!")
+
 			artifact = np.load(f'./{input_speaker}/{filename}', allow_pickle=True)[()]
-			qnt = torch.from_numpy(artifact["codes"].astype(int))[0].t().to(dtype=torch.int16, device=device)
-			qnt = trim( qnt, int( cfg.dataset.frames_per_second * 3 ) )
+
+			lang = artifact["metadata"]["language"] if "language" in artifact["metadata"]["language"] else "en"
+			if "phonemes" in artifact["metadata"]:
+				phn = artifact["metadata"]["phonemes"]
+			elif "text" in artifact["metadata"]:
+				txt = artifact["metadata"]["text"]
+				phn = phonemize( txt, language=lang )
 			
-			features[filename] = tts.audio_embedding( qnt )
-		# try and extract features from the raw audio itself
+			phn = phn.replace("(en)", "")
+			if lang != "en":
+				phn = phn.replace(f"({metadata['language']})", "")
+
+			features[filename] = tts.text_embedding( phn )
 		else:
-			# qnt = tts.encode_audio(f'./{input_speaker}/{filename}', trim_length=3.0).to(device)
-			wav, sr = load_audio( f'./{input_speaker}/{filename}' )
-			features[filename] = mfcc(wav.to(device))[0].t()
+			# treat embeddings as features, if provided quantized audio
+			if extension in artifact_extension:
+				artifact = np.load(f'./{input_speaker}/{filename}', allow_pickle=True)[()]
+				qnt = torch.from_numpy(artifact["codes"].astype(int))[0].t().to(dtype=torch.int16, device=device)
+				qnt = trim( qnt, int( cfg.dataset.frames_per_second * 3 ) )
+				
+				features[filename] = tts.audio_embedding( qnt )
+			# try and extract features from the raw audio itself
+			else:
+				# qnt = tts.encode_audio(f'./{input_speaker}/{filename}', trim_length=3.0).to(device)
+				wav, sr = load_audio( f'./{input_speaker}/{filename}' )
+				features[filename] = mfcc(wav.to(device))[0].t()
 
 	# calculate pairs, flattened because it makes tqdm nicer
 	for filename_a, embedding_a in features.items():
@@ -144,21 +159,21 @@ def main():
 
 	parser.add_argument("--input-speaker", type=Path)
 	parser.add_argument("--yaml", type=Path)
+	parser.add_argument("--text", action="store_true")
+
 	parser.add_argument("--audio-backend", type=str, default="encodec")
 	parser.add_argument("--dtype", type=str, default="bfloat16")
 	parser.add_argument("--amp", action="store_true")
 	parser.add_argument("--device", type=str, default="cuda")
-	parser.add_argument("--raise-exceptions", action="store_true")
 	
 	args = parser.parse_args()
 
 	process(
 		input_speaker=args.input_speaker,
 		yaml=args.yaml,
+		text=args.text,
 
 		audio_backend=args.audio_backend,
-		raise_exceptions=args.raise_exceptions,
-
 		device=args.device,
 		dtype=args.dtype,
 		amp=args.amp,

vall_e/inference.py

@@ -123,6 +123,22 @@ class TTS():
 		
 		return res
 
+	@torch.inference_mode()
+	def text_embedding( self, input, prom=False ):
+		model = None
+
+		for name, engine in self.engines.items():
+			model = engine.module
+			break
+
+		if isinstance( input, str ):
+			input = cfg.tokenizer.encode(input)
+
+		if isinstance( input, list ):
+			input = torch.tensor( input, dtype=torch.uint8, device=self.device )
+
+		return model.text_emb( input )
+
 	@torch.inference_mode()
 	def audio_embedding( self, input, prom=False ):
 		model = None