forked from mrq/ai-voice-cloning
reworked generating metadata to embed, should now store overrided settings
This commit is contained in:
parent
7798767fc6
commit
e731b9ba84
138
src/utils.py
138
src/utils.py
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@ -282,6 +282,72 @@ def generate(
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name = f"{name}_{candidate}"
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return name
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def get_info( voice, settings = None, latents = True ):
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info = {
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'text': text,
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'delimiter': '\\n' if delimiter and delimiter == "\n" else delimiter,
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'emotion': emotion,
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'prompt': prompt,
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'voice': voice,
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'seed': seed,
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'candidates': candidates,
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'num_autoregressive_samples': num_autoregressive_samples,
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'diffusion_iterations': diffusion_iterations,
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'temperature': temperature,
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'diffusion_sampler': diffusion_sampler,
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'breathing_room': breathing_room,
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'cvvp_weight': cvvp_weight,
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'top_p': top_p,
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'diffusion_temperature': diffusion_temperature,
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'length_penalty': length_penalty,
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'repetition_penalty': repetition_penalty,
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'cond_free_k': cond_free_k,
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'experimentals': experimental_checkboxes,
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'time': time.time()-full_start_time,
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'datetime': datetime.now().isoformat(),
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'model': tts.autoregressive_model_path,
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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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if settings is not None:
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for k in settings:
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if k in info:
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info[k] = settings[k]
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if 'half_p' in settings and 'cond_free' in settings:
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info['experimentals'] = []
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if settings['half_p']:
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info['experimentals'].append("Half Precision")
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if settings['cond_free']:
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info['experimentals'].append("Conditioning-Free")
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if latents and "latents" not in info:
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voice = info['voice']
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latents_path = f'{get_voice_dir()}/{voice}/cond_latents.pth'
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if voice == "random" or voice == "microphone":
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if latents and settings['conditioning_latents']:
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dir = f'{get_voice_dir()}/{voice}/'
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if not os.path.isdir(dir):
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os.makedirs(dir, exist_ok=True)
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latents_path = f'{dir}/cond_latents.pth'
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torch.save(conditioning_latents, latents_path)
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else:
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if settings and "model_hash" in settings:
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latents_path = f'{get_voice_dir()}/{voice}/cond_latents_{settings["model_hash"][:8]}.pth'
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elif hasattr(tts, "autoregressive_model_hash"):
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latents_path = f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth'
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if latents_path and os.path.exists(latents_path):
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try:
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with open(latents_path, 'rb') as f:
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info['latents'] = base64.b64encode(f.read()).decode("ascii")
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except Exception as e:
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pass
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return info
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for line, cut_text in enumerate(texts):
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if emotion == "Custom":
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if prompt and prompt.strip() != "":
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@ -295,6 +361,7 @@ def generate(
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# do setting editing
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match = re.findall(r'^(\{.+\}) (.+?)$', cut_text)
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override = None
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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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@ -304,11 +371,11 @@ def generate(
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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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used_settings = get_settings( override )
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else:
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gen, additionals = tts.tts(cut_text, **settings )
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used_settings = settings.copy()
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gen, additionals = tts.tts(cut_text, **used_settings )
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seed = additionals[0]
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run_time = time.time()-start_time
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@ -320,10 +387,16 @@ def generate(
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for j, g in enumerate(gen):
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audio = g.squeeze(0).cpu()
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name = get_name(line=line, candidate=j)
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used_settings['text'] = cut_text
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used_settings['time'] = run_time
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used_settings['datetime'] = datetime.now().isoformat(),
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used_settings['model'] = tts.autoregressive_model_path
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used_settings['model_hash'] = tts.autoregressive_model_hash if hasattr(tts, 'autoregressive_model_hash') else None
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audio_cache[name] = {
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'audio': audio,
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'text': cut_text,
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'time': run_time
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'settings': get_info(voice=override['voice'] if override and 'voice' in override else voice, settings=used_settings)
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}
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# save here in case some error happens mid-batch
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torchaudio.save(f'{outdir}/{voice}_{name}.wav', audio, tts.output_sample_rate)
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@ -358,40 +431,13 @@ def generate(
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audio = audio.squeeze(0).cpu()
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audio_cache[name] = {
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'audio': audio,
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'text': text,
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'time': time.time()-full_start_time,
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'settings': get_info(voice=voice),
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'output': True
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}
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else:
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name = get_name(candidate=candidate)
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audio_cache[name]['output'] = True
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info = {
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'text': text,
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'delimiter': '\\n' if delimiter and delimiter == "\n" else delimiter,
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'emotion': emotion,
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'prompt': prompt,
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'voice': voice,
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'seed': seed,
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'candidates': candidates,
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'num_autoregressive_samples': num_autoregressive_samples,
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'diffusion_iterations': diffusion_iterations,
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'temperature': temperature,
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'diffusion_sampler': diffusion_sampler,
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'breathing_room': breathing_room,
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'cvvp_weight': cvvp_weight,
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'top_p': top_p,
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'diffusion_temperature': diffusion_temperature,
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'length_penalty': length_penalty,
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'repetition_penalty': repetition_penalty,
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'cond_free_k': cond_free_k,
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'experimentals': experimental_checkboxes,
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'time': time.time()-full_start_time,
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'datetime': datetime.now().isoformat(),
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'model': tts.autoregressive_model_path,
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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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if args.voice_fixer:
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if not voicefixer:
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@ -414,8 +460,7 @@ def generate(
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)
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fixed_cache[f'{name}_fixed'] = {
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'text': audio_cache[name]['text'],
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'time': audio_cache[name]['time'],
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'settings': audio_cache[name]['settings'],
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'output': True
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}
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audio_cache[name]['output'] = False
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@ -434,36 +479,21 @@ def generate(
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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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if voice and voice != "random" and conditioning_latents is not None:
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latents_path = f'{get_voice_dir()}/{voice}/cond_latents.pth'
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if hasattr(tts, 'autoregressive_model_hash'):
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latents_path = f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth'
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try:
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with open(latents_path, 'rb') as f:
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info['latents'] = base64.b64encode(f.read()).decode("ascii")
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except Exception as e:
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pass
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f.write(json.dumps(audio_cache[name]['settings'], indent='\t') )
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if args.embed_output_metadata:
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for name in progress.tqdm(audio_cache, desc="Embedding metadata..."):
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if 'pruned' in audio_cache[name] and audio_cache[name]['pruned']:
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continue
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info['text'] = audio_cache[name]['text']
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info['time'] = audio_cache[name]['time']
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metadata = music_tag.load_file(f"{outdir}/{voice}_{name}.wav")
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metadata['lyrics'] = json.dumps(info)
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metadata['lyrics'] = json.dumps(audio_cache[name]['settings'])
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metadata.save()
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if sample_voice is not None:
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sample_voice = (tts.input_sample_rate, sample_voice.numpy())
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info = get_info(voice=voice, latents=False)
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print(f"Generation took {info['time']} seconds, saved to '{output_voices[0]}'\n")
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info['seed'] = settings['use_deterministic_seed']
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