Open StillTravelling opened 1 month ago
Sorry I'm hopeless with git but I think the below changes will help when switching between hifigan and non hifigan .pths....
Modules\Tortoise-tts\tortoise\utils\audio.py def load_voice(voice, extra_voice_dirs=[], load_latents=True, sample_rate=22050, device='cpu', model_hash=None, use_hifigan=False): if voice == 'random': return None, None print(f"hifigan = {use_hifigan}, voice={voice}") voices = _get_voices(dirs=[get_voice_dir()] + extra_voice_dirs, load_latents=load_latents) paths = voices[voice] mtime = 0 latent = None voices = [] for path in paths: filename = os.path.basename(path) if filename[-4:] == ".pth" and use_hifigan == False and filename[:12] == "cond_latents": if not model_hash and filename == "cond_latents.pth": latent = path elif model_hash and filename == f"cond_latents_{model_hash[:8]}.pth": latent = path elif filename[-4:] == ".pth" and use_hifigan == True and filename[:20] == "hifigan_cond_latents": if not model_hash and filename == "hifigan_cond_latents.pth": latent = path elif model_hash and filename == f"hifigan_cond_latents_{model_hash[:8]}.pth": latent = path else: voices.append(path) mtime = max(mtime, os.path.getmtime(path)) if load_latents and latent is not None: #if os.path.getmtime(latent) > mtime: print(f"Reading from latent: {latent}") return None, torch.load(latent, map_location=device) #print(f"Latent file out of date: {latent}") samples = [] for path in voices: c = load_audio(path, sample_rate) samples.append(c) return samples, None src\utils.py def fetch_voice( voice ): cache_key = f'{voice}:{tts.autoregressive_model_hash[:8]}' if cache_key in voice_cache: return voice_cache[cache_key] print(f"Loading voice: {voice} with model {tts.autoregressive_model_hash[:8]}") sample_voice = None if voice == "microphone": if parameters['mic_audio'] is None: raise Exception("Please provide audio from mic when choosing `microphone` as a voice input") voice_samples, conditioning_latents = [load_audio(parameters['mic_audio'], tts.input_sample_rate)], None elif voice == "random": voice_samples, conditioning_latents = None, tts.get_random_conditioning_latents() else: if progress is not None: notify_progress(f"Loading voice: {voice}", progress=progress) voice_samples, conditioning_latents = load_voice(voice, model_hash=tts.autoregressive_model_hash, use_hifigan=args.use_hifigan) if voice_samples and len(voice_samples) > 0: if conditioning_latents is None: conditioning_latents = compute_latents(voice=voice, voice_samples=voice_samples, voice_latents_chunks=parameters['voice_latents_chunks']) sample_voice = torch.cat(voice_samples, dim=-1).squeeze().cpu() voice_samples = None voice_cache[cache_key] = (voice_samples, conditioning_latents, sample_voice) return voice_cache[cache_key] ### def get_info( voice, settings = None, latents = True ): info = {} info.update(parameters) info['time'] = time.time()-full_start_time info['datetime'] = datetime.now().isoformat() info['model'] = tts.autoregressive_model_path info['model_hash'] = tts.autoregressive_model_hash info['progress'] = None del info['progress'] if info['delimiter'] == "\n": info['delimiter'] = "\\n" if settings is not None: for k in settings: if k in info: info[k] = settings[k] if 'half_p' in settings and 'cond_free' in settings: info['experimentals'] = [] if settings['half_p']: info['experimentals'].append("Half Precision") if settings['cond_free']: info['experimentals'].append("Conditioning-Free") if latents and "latents" not in info: voice = info['voice'] model_hash = settings["model_hash"][:8] if settings is not None and "model_hash" in settings else tts.autoregressive_model_hash[:8] dir = f'{get_voice_dir()}/{voice}/' if args.use_hifigan: latents_path = f'{dir}/cond_latents_{model_hash}.pth' else: latents_path = f'{dir}/hifigan_cond_latents_{model_hash}.pth' if voice == "random" or voice == "microphone": if args.use_hifigan: if latents and settings is not None and torch.any(settings['conditioning_latents']): os.makedirs(dir, exist_ok=True) torch.save(conditioning_latents, latents_path) else: if latents and settings is not None and settings['conditioning_latents']: os.makedirs(dir, exist_ok=True) torch.save(conditioning_latents, latents_path) if latents_path and os.path.exists(latents_path): try: with open(latents_path, 'rb') as f: info['latents'] = base64.b64encode(f.read()).decode("ascii") except Exception as e: pass return info ### settings = get_settings( override=override ) #print(settings) #This line changed to comment out try: if args.use_hifigan: gen = tts.tts(cut_text, **settings) else: gen, additionals = tts.tts(cut_text, **settings ) parameters['seed'] = additionals[0] except Exception as e: raise RuntimeError(f'Possible latent mismatch: click the "(Re)Compute Voice Latents" button and then try again. Error: {e}') ### def compute_latents(voice=None, voice_samples=None, voice_latents_chunks=0, original_ar=False, original_diffusion=False): global tts global args unload_whisper() unload_voicefixer() if not tts: if tts_loading: raise Exception("TTS is still initializing...") load_tts() if hasattr(tts, "loading") and tts.loading: raise Exception("TTS is still initializing...") if args.tts_backend == "bark": tts.create_voice( voice ) return if args.autoregressive_model == "auto": tts.load_autoregressive_model(deduce_autoregressive_model(voice)) if voice: load_from_dataset = voice_latents_chunks == 0 if load_from_dataset: dataset_path = f'./training/{voice}/train.txt' if not os.path.exists(dataset_path): load_from_dataset = False else: with open(dataset_path, 'r', encoding="utf-8") as f: lines = f.readlines() print("Leveraging dataset for computing latents") voice_samples = [] max_length = 0 for line in lines: filename = f'./training/{voice}/{line.split("|")[0]}' waveform = load_audio(filename, 22050) max_length = max(max_length, waveform.shape[-1]) voice_samples.append(waveform) for i in range(len(voice_samples)): voice_samples[i] = pad_or_truncate(voice_samples[i], max_length) voice_latents_chunks = len(voice_samples) if voice_latents_chunks == 0: print("Dataset is empty!") load_from_dataset = True if not load_from_dataset: voice_samples, _ = load_voice(voice, load_latents=False, use_hifigan=args.use_hifigan) #This line changed if voice_samples is None: return conditioning_latents = tts.get_conditioning_latents(voice_samples, return_mels=not args.latents_lean_and_mean, slices=voice_latents_chunks, force_cpu=args.force_cpu_for_conditioning_latents, original_ar=original_ar, original_diffusion=original_diffusion) if len(conditioning_latents) == 4: conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None) if args.use_hifigan: #newsection outfile = f'{get_voice_dir()}/{voice}/hifigan_cond_latents_{tts.autoregressive_model_hash[:8]}.pth' else: outfile = f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth' #end newsection torch.save(conditioning_latents, outfile) print(f'Saved voice latents: {outfile}') return conditioning_latents ### def reload_tts(): unload_tts() load_tts() def change_hifigan(newvalue=True): #newsection args.use_hifigan=newvalue save_args_settings() do_gc() reload_tts() return args.use_hifigan def get_hifigan(): return args.use_hifigan #endnewsection src\webui.py EXEC_SETTINGS['autoregressive_model'].change( fn=update_autoregressive_model, inputs=EXEC_SETTINGS['autoregressive_model'], outputs=None, api_name="set_autoregressive_model" ) EXEC_SETTINGS['use_hifigan'].change( #newsection fn=change_hifigan, inputs=EXEC_SETTINGS['use_hifigan'], outputs=EXEC_SETTINGS['use_hifigan'], api_name="use_hifigan" ) EXEC_SETTINGS['use_hifigan'].select( fn=get_hifigan, outputs=EXEC_SETTINGS['use_hifigan'], api_name="get_hifigan" ) #endnewsection
Sorry I'm hopeless with git but I think the below changes will help when switching between hifigan and non hifigan .pths....