uses gitmylo/bark-voice-cloning-HuBERT-quantizer for creating custom voices (it slightly works better over the base method, but still not very good desu)
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68
src/utils.py
68
src/utils.py
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@ -42,9 +42,6 @@ from tortoise.utils.audio import load_audio, load_voice, load_voices, get_voice_
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from tortoise.utils.text import split_and_recombine_text
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from tortoise.utils.device import get_device_name, set_device_name, get_device_count, get_device_vram, get_device_batch_size, do_gc
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from whisper.normalizers.english import EnglishTextNormalizer
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from whisper.normalizers.basic import BasicTextNormalizer
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from whisper.tokenizer import LANGUAGES
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MODELS['dvae.pth'] = "https://huggingface.co/jbetker/tortoise-tts-v2/resolve/3704aea61678e7e468a06d8eea121dba368a798e/.models/dvae.pth"
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@ -68,6 +65,19 @@ MAX_TRAINING_DURATION = 11.6097505669
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VALLE_ENABLED = False
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BARK_ENABLED = False
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VERBOSE_DEBUG = True
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try:
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from whisper.normalizers.english import EnglishTextNormalizer
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from whisper.normalizers.basic import BasicTextNormalizer
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from whisper.tokenizer import LANGUAGES
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print("Whisper detected")
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except Exception as e:
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if VERBOSE_DEBUG:
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print("Error:", e)
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pass
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try:
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from vall_e.emb.qnt import encode as valle_quantize
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from vall_e.emb.g2p import encode as valle_phonemize
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@ -76,10 +86,11 @@ try:
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import soundfile
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print("VALL-E detected")
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VALLE_ENABLED = True
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except Exception as e:
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if False: # args.tts_backend == "vall-e":
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raise e
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if VERBOSE_DEBUG:
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print("Error:", e)
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pass
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if VALLE_ENABLED:
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@ -93,27 +104,39 @@ try:
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from scipy.io.wavfile import write as write_wav
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print("Bark detected")
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BARK_ENABLED = True
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except Exception as e:
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if False: # args.tts_backend == "bark":
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raise e
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if VERBOSE_DEBUG:
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print("Error:", e)
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pass
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if BARK_ENABLED:
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try:
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from vocos import Vocos
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VOCOS_ENABLED = True
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print("Vocos detected")
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except Exception as e:
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if VERBOSE_DEBUG:
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print("Error:", e)
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VOCOS_ENABLED = False
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try:
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from hubert.hubert_manager import HuBERTManager
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from hubert.pre_kmeans_hubert import CustomHubert
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from hubert.customtokenizer import CustomTokenizer
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hubert_manager = HuBERTManager()
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hubert_manager.make_sure_hubert_installed()
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hubert_manager.make_sure_tokenizer_installed()
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HUBERT_ENABLED = True
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print("HuBERT detected")
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except Exception as e:
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if VERBOSE_DEBUG:
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print("Error:", e)
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HUBERT_ENABLED = False
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if BARK_ENABLED:
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TTSES.append('bark')
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def semantic_to_audio_tokens(
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@ -192,7 +215,7 @@ if BARK_ENABLED:
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# generate semantic tokens
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if HUBERT_ENABLED:
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wav = wav.to(device)
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wav = wav.to(self.device)
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# Extract discrete codes from EnCodec
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with torch.no_grad():
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@ -202,23 +225,20 @@ if BARK_ENABLED:
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# get seconds of audio
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seconds = wav.shape[-1] / model.sample_rate
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hubert_manager = HuBERTManager()
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hubert_manager.make_sure_hubert_installed()
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hubert_manager.make_sure_tokenizer_installed()
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from hubert.pre_kmeans_hubert import CustomHubert
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from hubert.customtokenizer import CustomTokenizer
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# Load the HuBERT model
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hubert_model = CustomHubert(checkpoint_path='./models/hubert/hubert.pt').to(device)
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hubert_model = CustomHubert(checkpoint_path='./data/models/hubert/hubert.pt').to(self.device)
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# Load the CustomTokenizer model
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tokenizer = CustomTokenizer.load_from_checkpoint('./models/hubert/tokenizer.pth').to(device)
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tokenizer = CustomTokenizer.load_from_checkpoint('./data/models/hubert/tokenizer.pth').to(self.device)
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semantic_vectors = hubert_model.forward(wav, input_sample_hz=model.sample_rate)
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semantic_tokens = tokenizer.get_token(semantic_vectors)
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# move codes to cpu
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codes = codes.cpu().numpy()
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# move semantic tokens to cpu
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semantic_tokens = semantic_tokens.cpu().numpy()
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else:
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# Load and pre-process the audio waveform
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model = load_codec_model(use_gpu=True)
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wav, sr = torchaudio.load(audio_filepath)
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wav = convert_audio(wav, sr, model.sample_rate, model.channels)
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wav = wav.unsqueeze(0).to(self.device)
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# Extract discrete codes from EnCodec
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@ -1358,6 +1378,10 @@ def compute_latents(voice=None, voice_samples=None, voice_latents_chunks=0, orig
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if hasattr(tts, "loading") and tts.loading:
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raise Exception("TTS is still initializing...")
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if args.tts_backend == "bark":
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tts.create_voice( voice )
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return
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if args.autoregressive_model == "auto":
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tts.load_autoregressive_model(deduce_autoregressive_model(voice))
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@ -169,10 +169,6 @@ def reset_generate_settings_proxy():
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return tuple(res)
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def compute_latents_proxy(voice, voice_latents_chunks, original_ar, original_diffusion, progress=gr.Progress(track_tqdm=True)):
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if args.tts_backend == "bark":
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global tts
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tts.create_voice( voice )
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return voice
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compute_latents( voice=voice, voice_latents_chunks=voice_latents_chunks, original_ar=original_ar, original_diffusion=original_diffusion )
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return voice
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