added settings editing (will add a guide on what to do later, and an example)
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parent
119ac50c58
commit
7798767fc6
244
src/utils.py
244
src/utils.py
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@ -90,46 +90,59 @@ def generate(
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do_gc()
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if voice != "microphone":
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voices = [voice]
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else:
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voices = []
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voices = {}
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if voice == "microphone":
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if mic_audio is None:
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raise Exception("Please provide audio from mic when choosing `microphone` as a voice input")
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mic = load_audio(mic_audio, tts.input_sample_rate)
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voice_samples, conditioning_latents = [mic], None
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elif voice == "random":
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voice_samples, conditioning_latents = None, tts.get_random_conditioning_latents()
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else:
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progress(0, desc="Loading voice...")
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# nasty check for users that, for whatever reason, updated the web UI but not mrq/tortoise-tts
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if hasattr(tts, 'autoregressive_model_hash'):
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voice_samples, conditioning_latents = load_voice(voice, model_hash=tts.autoregressive_model_hash)
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voice_samples = None
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conditioning_latents =None
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sample_voice = None
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def fetch_voice( requested ):
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voice = requested
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if voice in voices:
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return voices[voice]
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print(f"Loading voice: {voice}")
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if voice == "microphone":
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if mic_audio is None:
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raise Exception("Please provide audio from mic when choosing `microphone` as a voice input")
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voice_samples, conditioning_latents = [load_audio(mic_audio, tts.input_sample_rate)], None
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elif voice == "random":
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voice_samples, conditioning_latents = None, tts.get_random_conditioning_latents()
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else:
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voice_samples, conditioning_latents = load_voice(voice)
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if voice_samples and len(voice_samples) > 0:
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sample_voice = torch.cat(voice_samples, dim=-1).squeeze().cpu()
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conditioning_latents = tts.get_conditioning_latents(voice_samples, return_mels=not args.latents_lean_and_mean, progress=progress, slices=voice_latents_chunks, force_cpu=args.force_cpu_for_conditioning_latents)
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if len(conditioning_latents) == 4:
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conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
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if voice != "microphone":
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progress(0, desc=f"Loading voice: {voice}")
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# nasty check for users that, for whatever reason, updated the web UI but not mrq/tortoise-tts
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if hasattr(tts, 'autoregressive_model_hash'):
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torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth')
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voice_samples, conditioning_latents = load_voice(voice, model_hash=tts.autoregressive_model_hash)
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else:
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torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
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voice_samples = None
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else:
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if conditioning_latents is not None:
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sample_voice, _ = load_voice(voice, load_latents=False)
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if sample_voice and len(sample_voice) > 0:
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sample_voice = torch.cat(sample_voice, dim=-1).squeeze().cpu()
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voice_samples, conditioning_latents = load_voice(voice)
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if voice_samples and len(voice_samples) > 0:
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sample_voice = torch.cat(voice_samples, dim=-1).squeeze().cpu()
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conditioning_latents = tts.get_conditioning_latents(voice_samples, return_mels=not args.latents_lean_and_mean, progress=progress, slices=voice_latents_chunks, force_cpu=args.force_cpu_for_conditioning_latents)
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if len(conditioning_latents) == 4:
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conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
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if voice != "microphone":
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if hasattr(tts, 'autoregressive_model_hash'):
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torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth')
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else:
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torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
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voice_samples = None
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else:
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sample_voice = None
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if conditioning_latents is not None:
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sample_voice, _ = load_voice(voice, load_latents=False)
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if sample_voice and len(sample_voice) > 0:
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sample_voice = torch.cat(sample_voice, dim=-1).squeeze().cpu()
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else:
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sample_voice = None
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voices[voice] = (voice_samples, conditioning_latents, sample_voice)
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return voices[voice]
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voice_samples, conditioning_latents, sample_voice = fetch_voice(voice)
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if seed == 0:
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seed = None
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@ -138,42 +151,80 @@ def generate(
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print("Requesting weighing against CVVP weight, but voice latents are missing some extra data. Please regenerate your voice latents.")
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cvvp_weight = 0
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def get_settings( override=None ):
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settings = {
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'temperature': float(temperature),
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settings = {
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'temperature': float(temperature),
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'top_p': float(top_p),
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'diffusion_temperature': float(diffusion_temperature),
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'length_penalty': float(length_penalty),
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'repetition_penalty': float(repetition_penalty),
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'cond_free_k': float(cond_free_k),
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'top_p': float(top_p),
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'diffusion_temperature': float(diffusion_temperature),
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'length_penalty': float(length_penalty),
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'repetition_penalty': float(repetition_penalty),
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'cond_free_k': float(cond_free_k),
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'num_autoregressive_samples': num_autoregressive_samples,
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'sample_batch_size': args.sample_batch_size,
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'diffusion_iterations': diffusion_iterations,
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'num_autoregressive_samples': num_autoregressive_samples,
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'sample_batch_size': args.sample_batch_size,
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'diffusion_iterations': diffusion_iterations,
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'voice_samples': voice_samples,
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'conditioning_latents': conditioning_latents,
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'voice_samples': voice_samples,
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'conditioning_latents': conditioning_latents,
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'use_deterministic_seed': seed,
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'return_deterministic_state': True,
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'k': candidates,
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'diffusion_sampler': diffusion_sampler,
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'breathing_room': breathing_room,
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'progress': progress,
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'half_p': "Half Precision" in experimental_checkboxes,
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'cond_free': "Conditioning-Free" in experimental_checkboxes,
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'cvvp_amount': cvvp_weight,
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}
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'use_deterministic_seed': seed,
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'return_deterministic_state': True,
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'k': candidates,
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'diffusion_sampler': diffusion_sampler,
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'breathing_room': breathing_room,
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'progress': progress,
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'half_p': "Half Precision" in experimental_checkboxes,
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'cond_free': "Conditioning-Free" in experimental_checkboxes,
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'cvvp_amount': cvvp_weight,
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'autoregressive_model': args.autoregressive_model,
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}
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# clamp it down for the insane users who want this
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# it would be wiser to enforce the sample size to the batch size, but this is what the user wants
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sample_batch_size = args.sample_batch_size
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if not sample_batch_size:
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sample_batch_size = tts.autoregressive_batch_size
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if num_autoregressive_samples < sample_batch_size:
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settings['sample_batch_size'] = num_autoregressive_samples
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# could be better to just do a ternary on everything above, but i am not a professional
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if override is not None:
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if 'voice' in override:
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voice = override['voice']
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if delimiter is None:
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if "autoregressive_model" in override and override["autoregressive_model"] == "auto":
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dir = f'./training/{voice}-finetune/models/'
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if os.path.exists(f'./training/finetunes/{voice}.pth'):
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override["autoregressive_model"] = f'./training/finetunes/{voice}.pth'
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elif os.path.isdir(dir):
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counts = sorted([ int(d[:-8]) for d in os.listdir(dir) if d[-8:] == "_gpt.pth" ])
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names = [ f'./{dir}/{d}_gpt.pth' for d in counts ]
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override["autoregressive_model"] = names[-1]
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else:
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override["autoregressive_model"] = None
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# necessary to ensure the right model gets loaded for the latents
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tts.load_autoregressive_model( override["autoregressive_model"] )
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fetched = fetch_voice(voice)
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settings['voice_samples'] = fetched[0]
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settings['conditioning_latents'] = fetched[1]
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for k in override:
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if k not in settings:
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continue
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settings[k] = override[k]
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if hasattr(tts, 'autoregressive_model_path') and tts.autoregressive_model_path != settings["autoregressive_model"]:
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tts.load_autoregressive_model( settings["autoregressive_model"] )
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# clamp it down for the insane users who want this
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# it would be wiser to enforce the sample size to the batch size, but this is what the user wants
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sample_batch_size = args.sample_batch_size
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if not sample_batch_size:
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sample_batch_size = tts.autoregressive_batch_size
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if num_autoregressive_samples < sample_batch_size:
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settings['sample_batch_size'] = num_autoregressive_samples
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return settings
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settings = get_settings()
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if not delimiter:
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delimiter = "\n"
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elif delimiter == "\\n":
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delimiter = "\n"
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@ -189,7 +240,6 @@ def generate(
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os.makedirs(outdir, exist_ok=True)
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audio_cache = {}
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resample = None
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if tts.output_sample_rate != args.output_sample_rate:
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@ -238,12 +288,28 @@ def generate(
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cut_text = f"[{prompt},] {cut_text}"
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elif emotion != "None":
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cut_text = f"[I am really {emotion.lower()},] {cut_text}"
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progress.msg_prefix = f'[{str(line+1)}/{str(len(texts))}]'
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print(f"{progress.msg_prefix} Generating line: {cut_text}")
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start_time = time.time()
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gen, additionals = tts.tts(cut_text, **settings )
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# do setting editing
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match = re.findall(r'^(\{.+\}) (.+?)$', cut_text)
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if match and len(match) > 0:
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match = match[0]
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try:
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override = json.loads(match[0])
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except Exception as e:
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print(e)
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raise Exception("Prompt settings editing requested, but received invalid JSON")
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cut_text = match[1].strip()
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new_settings = get_settings( override )
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gen, additionals = tts.tts(cut_text, **new_settings )
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else:
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gen, additionals = tts.tts(cut_text, **settings )
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seed = additionals[0]
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run_time = time.time()-start_time
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print(f"Generating line took {run_time} seconds")
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@ -327,19 +393,6 @@ def generate(
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'model_hash': tts.autoregressive_model_hash if hasattr(tts, 'autoregressive_model_hash') else None,
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}
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"""
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# kludgy yucky codesmells
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for name in audio_cache:
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if 'output' not in audio_cache[name]:
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continue
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#output_voices.append(f'{outdir}/{voice}_{name}.wav')
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output_voices.append(name)
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if not args.embed_output_metadata:
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with open(f'{outdir}/{voice}_{name}.json', 'w', encoding="utf-8") as f:
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f.write(json.dumps(info, indent='\t') )
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"""
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if args.voice_fixer:
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if not voicefixer:
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progress(0, "Loading voicefix...")
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@ -1057,8 +1110,8 @@ def prepare_dataset( files, outdir, language=None, skip_existings=False, progres
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files = sorted(files)
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previous_list = []
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parsed_list = []
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if skip_existings and os.path.exists(f'{outdir}/train.txt'):
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parsed_list = []
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with open(f'{outdir}/train.txt', 'r', encoding="utf-8") as f:
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parsed_list = f.readlines()
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@ -1103,20 +1156,13 @@ def prepare_dataset( files, outdir, language=None, skip_existings=False, progres
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line = f"{sliced_name}|{segment['text'].strip()}"
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transcription.append(line)
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with open(f'{outdir}/train.txt', 'a', encoding="utf-8") as f:
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f.write(f'{line}\n')
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f.write(f'\n{line}')
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do_gc()
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with open(f'{outdir}/whisper.json', 'w', encoding="utf-8") as f:
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f.write(json.dumps(results, indent='\t'))
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if len(parsed_list) > 0:
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transcription = parsed_list + transcription
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joined = '\n'.join(transcription)
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with open(f'{outdir}/train.txt', 'w', encoding="utf-8") as f:
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f.write(joined)
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unload_whisper()
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return f"Processed dataset to: {outdir}\n{joined}"
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@ -1688,6 +1734,22 @@ def read_generate_settings(file, read_latents=True):
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latents,
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)
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def version_check_tts( min_version ):
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global tts
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if not tts:
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raise Exception("TTS is not initialized")
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if not hasattr(tts, 'version'):
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return False
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if min_version[0] > tts.version[0]:
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return True
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if min_version[1] > tts.version[1]:
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return True
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if min_version[2] >= tts.version[2]:
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return True
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return False
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def load_tts( restart=False, model=None ):
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global args
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global tts
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