updated bark support, it'll also query for vocos, it actually works (I don't know what specifically was the issue)

src/utils.py

@@ -86,6 +86,7 @@ if VALLE_ENABLED:
 	TTSES.append('vall-e')
 
 try:
+	from bark import text_to_semantic
 	from bark.generation import SAMPLE_RATE as BARK_SAMPLE_RATE, ALLOWED_PROMPTS, preload_models, codec_decode, generate_coarse, generate_fine, generate_text_semantic, load_codec_model
 	from bark.api import generate_audio as bark_generate_audio
 	from encodec.utils import convert_audio
@@ -98,12 +99,48 @@ except Exception as e:
 		raise e
 	pass
 
+if BARK_ENABLED:
+	try:
+		from vocos import Vocos
+		VOCOS_ENABLED = True
+	except Exception as e:
+		VOCOS_ENABLED = False
+
+	try:
+		from hubert.hubert_manager import HuBERTManager
+
+		HUBERT_ENABLED = True
+	except Exception as e:
+		HUBERT_ENABLED = False
+
 if BARK_ENABLED:
 	TTSES.append('bark')
+
+	def semantic_to_audio_tokens(
+	    semantic_tokens,
+	    history_prompt = None,
+	    temp = 0.7,
+	    silent = False,
+	    output_full = False,
+	):
+	    coarse_tokens = generate_coarse(
+	        semantic_tokens, history_prompt=history_prompt, temp=temp, silent=silent, use_kv_caching=True
+	    )
+	    fine_tokens = generate_fine(coarse_tokens, history_prompt=history_prompt, temp=0.5)
+
+	    if output_full:
+	        full_generation = {
+	            "semantic_prompt": semantic_tokens,
+	            "coarse_prompt": coarse_tokens,
+	            "fine_prompt": fine_tokens,
+	        }
+	        return full_generation
+	    return fine_tokens
+
 	class Bark_TTS():
 		def __init__(self, small=False):
 			self.input_sample_rate = BARK_SAMPLE_RATE
-			self.output_sample_rate = args.output_sample_rate
+			self.output_sample_rate = BARK_SAMPLE_RATE # args.output_sample_rate
 
 			preload_models(
 				text_use_gpu=True,
@@ -118,7 +155,12 @@ if BARK_ENABLED:
 				force_reload=False
 			)
 
-		def create_voice( self, voice, device='cuda' ):
+			self.device = get_device_name()
+
+			if VOCOS_ENABLED:
+				self.vocos = Vocos.from_pretrained("charactr/vocos-encodec-24khz").to(self.device)
+
+		def create_voice( self, voice ):
 			transcription_json = f'./training/{voice}/whisper.json'
 			if not os.path.exists(transcription_json):
 				raise f"Transcription for voice not found: {voice}"
@@ -146,29 +188,75 @@ if BARK_ENABLED:
 			model = load_codec_model(use_gpu=True)
 			wav, sr = torchaudio.load(audio_filepath)
 			wav = convert_audio(wav, sr, model.sample_rate, model.channels)
-			wav = wav.unsqueeze(0).to(device)
-
-			# Extract discrete codes from EnCodec
-			with torch.no_grad():
-				encoded_frames = model.encode(wav)
-			codes = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1).squeeze().cpu().numpy()  # [n_q, T]
 
-			# get seconds of audio
-			seconds = wav.shape[-1] / model.sample_rate
 			# generate semantic tokens
-			semantic_tokens = generate_text_semantic(text, max_gen_duration_s=seconds, top_k=50, top_p=.95, temp=0.7)
+			
+			if HUBERT_ENABLED:
+				wav = wav.to(device)
+
+				# Extract discrete codes from EnCodec
+				with torch.no_grad():
+					encoded_frames = model.encode(wav.unsqueeze(0))
+				codes = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1).squeeze()  # [n_q, T]
+
+				# get seconds of audio
+				seconds = wav.shape[-1] / model.sample_rate
+
+				hubert_manager = HuBERTManager()
+				hubert_manager.make_sure_hubert_installed()
+				hubert_manager.make_sure_tokenizer_installed()
+
+				from hubert.pre_kmeans_hubert import CustomHubert
+				from hubert.customtokenizer import CustomTokenizer
+
+				# Load the HuBERT model
+				hubert_model = CustomHubert(checkpoint_path='./models/hubert/hubert.pt').to(device)
+
+				# Load the CustomTokenizer model
+				tokenizer = CustomTokenizer.load_from_checkpoint('./models/hubert/tokenizer.pth').to(device)
+			else:
+				# Load and pre-process the audio waveform
+				model = load_codec_model(use_gpu=True)
+				wav, sr = torchaudio.load(audio_filepath)
+				wav = convert_audio(wav, sr, model.sample_rate, model.channels)
+				wav = wav.unsqueeze(0).to(self.device)
+
+				# Extract discrete codes from EnCodec
+				with torch.no_grad():
+					encoded_frames = model.encode(wav)
+				codes = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1).squeeze().cpu().numpy()  # [n_q, T]
+
+				# get seconds of audio
+				seconds = wav.shape[-1] / model.sample_rate
+
+				# generate semantic tokens
+				semantic_tokens = generate_text_semantic(text, max_gen_duration_s=seconds, top_k=50, top_p=.95, temp=0.7)
 
 			output_path = './modules/bark/bark/assets/prompts/' + voice.replace("/", "_") + '.npz'
 			np.savez(output_path, fine_prompt=codes, coarse_prompt=codes[:2, :], semantic_prompt=semantic_tokens)
 
 		def inference( self, text, voice, text_temp=0.7, waveform_temp=0.7 ):
-			if not os.path.exists('./modules/bark/bark/assets/prompts/' + voice + '.npz'):
-				self.create_voice( voice )
-			voice = voice.replace("/", "_")
-			if voice not in ALLOWED_PROMPTS:
-				ALLOWED_PROMPTS.add( voice )
+			if voice == "random":
+				voice = None
+			else:
+				if not os.path.exists('./modules/bark/bark/assets/prompts/' + voice + '.npz'):
+					self.create_voice( voice )
+				voice = voice.replace("/", "_")
+				if voice not in ALLOWED_PROMPTS:
+					ALLOWED_PROMPTS.add( voice )
+
+			semantic_tokens = text_to_semantic(text, history_prompt=voice, temp=text_temp, silent=False)
+			audio_tokens = semantic_to_audio_tokens( semantic_tokens, history_prompt=voice, temp=waveform_temp, silent=False, output_full=False )
+
+			if VOCOS_ENABLED:
+				audio_tokens_torch = torch.from_numpy(audio_tokens).to(self.device)
+				features = self.vocos.codes_to_features(audio_tokens_torch)
+				wav = self.vocos.decode(features, bandwidth_id=torch.tensor([2], device=self.device))
+			else:
+				wav = codec_decode( audio_tokens )
 
-			return (bark_generate_audio(text, history_prompt=voice, text_temp=text_temp, waveform_temp=waveform_temp), BARK_SAMPLE_RATE)
+			return ( wav, BARK_SAMPLE_RATE )
+			# return (bark_generate_audio(text, history_prompt=voice, text_temp=text_temp, waveform_temp=waveform_temp), BARK_SAMPLE_RATE)
 
 args = None
 tts = None
@@ -371,8 +459,10 @@ def generate_bark(**kwargs):
 			settings['datetime'] = datetime.now().isoformat()
 
 			# save here in case some error happens mid-batch
-			#torchaudio.save(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav', wav.cpu(), sr)
-			write_wav(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav', sr, wav)
+			if VOCOS_ENABLED:
+				torchaudio.save(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav', wav.cpu(), sr)
+			else:
+				write_wav(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav', sr, wav)
 			wav, sr = torchaudio.load(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav')
 
 			audio_cache[name] = {