added settings editing (will add a guide on what to do later, and an example)

This commit is contained in:
mrq 2023-03-06 21:48:34 +00:00
parent 119ac50c58
commit 7798767fc6

View File

@ -90,46 +90,59 @@ def generate(
do_gc()
if voice != "microphone":
voices = [voice]
else:
voices = []
voices = {}
if voice == "microphone":
if mic_audio is None:
raise Exception("Please provide audio from mic when choosing `microphone` as a voice input")
mic = load_audio(mic_audio, tts.input_sample_rate)
voice_samples, conditioning_latents = [mic], None
elif voice == "random":
voice_samples, conditioning_latents = None, tts.get_random_conditioning_latents()
else:
progress(0, desc="Loading voice...")
# nasty check for users that, for whatever reason, updated the web UI but not mrq/tortoise-tts
if hasattr(tts, 'autoregressive_model_hash'):
voice_samples, conditioning_latents = load_voice(voice, model_hash=tts.autoregressive_model_hash)
voice_samples = None
conditioning_latents =None
sample_voice = None
def fetch_voice( requested ):
voice = requested
if voice in voices:
return voices[voice]
print(f"Loading voice: {voice}")
if voice == "microphone":
if mic_audio is None:
raise Exception("Please provide audio from mic when choosing `microphone` as a voice input")
voice_samples, conditioning_latents = [load_audio(mic_audio, tts.input_sample_rate)], None
elif voice == "random":
voice_samples, conditioning_latents = None, tts.get_random_conditioning_latents()
else:
voice_samples, conditioning_latents = load_voice(voice)
if voice_samples and len(voice_samples) > 0:
sample_voice = torch.cat(voice_samples, dim=-1).squeeze().cpu()
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)
if len(conditioning_latents) == 4:
conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
if voice != "microphone":
progress(0, desc=f"Loading voice: {voice}")
# nasty check for users that, for whatever reason, updated the web UI but not mrq/tortoise-tts
if hasattr(tts, 'autoregressive_model_hash'):
torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth')
voice_samples, conditioning_latents = load_voice(voice, model_hash=tts.autoregressive_model_hash)
else:
torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
voice_samples = None
else:
if conditioning_latents is not None:
sample_voice, _ = load_voice(voice, load_latents=False)
if sample_voice and len(sample_voice) > 0:
sample_voice = torch.cat(sample_voice, dim=-1).squeeze().cpu()
voice_samples, conditioning_latents = load_voice(voice)
if voice_samples and len(voice_samples) > 0:
sample_voice = torch.cat(voice_samples, dim=-1).squeeze().cpu()
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)
if len(conditioning_latents) == 4:
conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
if voice != "microphone":
if hasattr(tts, 'autoregressive_model_hash'):
torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth')
else:
torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
voice_samples = None
else:
sample_voice = None
if conditioning_latents is not None:
sample_voice, _ = load_voice(voice, load_latents=False)
if sample_voice and len(sample_voice) > 0:
sample_voice = torch.cat(sample_voice, dim=-1).squeeze().cpu()
else:
sample_voice = None
voices[voice] = (voice_samples, conditioning_latents, sample_voice)
return voices[voice]
voice_samples, conditioning_latents, sample_voice = fetch_voice(voice)
if seed == 0:
seed = None
@ -138,42 +151,80 @@ def generate(
print("Requesting weighing against CVVP weight, but voice latents are missing some extra data. Please regenerate your voice latents.")
cvvp_weight = 0
def get_settings( override=None ):
settings = {
'temperature': float(temperature),
settings = {
'temperature': float(temperature),
'top_p': float(top_p),
'diffusion_temperature': float(diffusion_temperature),
'length_penalty': float(length_penalty),
'repetition_penalty': float(repetition_penalty),
'cond_free_k': float(cond_free_k),
'top_p': float(top_p),
'diffusion_temperature': float(diffusion_temperature),
'length_penalty': float(length_penalty),
'repetition_penalty': float(repetition_penalty),
'cond_free_k': float(cond_free_k),
'num_autoregressive_samples': num_autoregressive_samples,
'sample_batch_size': args.sample_batch_size,
'diffusion_iterations': diffusion_iterations,
'num_autoregressive_samples': num_autoregressive_samples,
'sample_batch_size': args.sample_batch_size,
'diffusion_iterations': diffusion_iterations,
'voice_samples': voice_samples,
'conditioning_latents': conditioning_latents,
'voice_samples': voice_samples,
'conditioning_latents': conditioning_latents,
'use_deterministic_seed': seed,
'return_deterministic_state': True,
'k': candidates,
'diffusion_sampler': diffusion_sampler,
'breathing_room': breathing_room,
'progress': progress,
'half_p': "Half Precision" in experimental_checkboxes,
'cond_free': "Conditioning-Free" in experimental_checkboxes,
'cvvp_amount': cvvp_weight,
}
'use_deterministic_seed': seed,
'return_deterministic_state': True,
'k': candidates,
'diffusion_sampler': diffusion_sampler,
'breathing_room': breathing_room,
'progress': progress,
'half_p': "Half Precision" in experimental_checkboxes,
'cond_free': "Conditioning-Free" in experimental_checkboxes,
'cvvp_amount': cvvp_weight,
'autoregressive_model': args.autoregressive_model,
}
# clamp it down for the insane users who want this
# it would be wiser to enforce the sample size to the batch size, but this is what the user wants
sample_batch_size = args.sample_batch_size
if not sample_batch_size:
sample_batch_size = tts.autoregressive_batch_size
if num_autoregressive_samples < sample_batch_size:
settings['sample_batch_size'] = num_autoregressive_samples
# could be better to just do a ternary on everything above, but i am not a professional
if override is not None:
if 'voice' in override:
voice = override['voice']
if delimiter is None:
if "autoregressive_model" in override and override["autoregressive_model"] == "auto":
dir = f'./training/{voice}-finetune/models/'
if os.path.exists(f'./training/finetunes/{voice}.pth'):
override["autoregressive_model"] = f'./training/finetunes/{voice}.pth'
elif os.path.isdir(dir):
counts = sorted([ int(d[:-8]) for d in os.listdir(dir) if d[-8:] == "_gpt.pth" ])
names = [ f'./{dir}/{d}_gpt.pth' for d in counts ]
override["autoregressive_model"] = names[-1]
else:
override["autoregressive_model"] = None
# necessary to ensure the right model gets loaded for the latents
tts.load_autoregressive_model( override["autoregressive_model"] )
fetched = fetch_voice(voice)
settings['voice_samples'] = fetched[0]
settings['conditioning_latents'] = fetched[1]
for k in override:
if k not in settings:
continue
settings[k] = override[k]
if hasattr(tts, 'autoregressive_model_path') and tts.autoregressive_model_path != settings["autoregressive_model"]:
tts.load_autoregressive_model( settings["autoregressive_model"] )
# clamp it down for the insane users who want this
# it would be wiser to enforce the sample size to the batch size, but this is what the user wants
sample_batch_size = args.sample_batch_size
if not sample_batch_size:
sample_batch_size = tts.autoregressive_batch_size
if num_autoregressive_samples < sample_batch_size:
settings['sample_batch_size'] = num_autoregressive_samples
return settings
settings = get_settings()
if not delimiter:
delimiter = "\n"
elif delimiter == "\\n":
delimiter = "\n"
@ -189,7 +240,6 @@ def generate(
os.makedirs(outdir, exist_ok=True)
audio_cache = {}
resample = None
if tts.output_sample_rate != args.output_sample_rate:
@ -241,9 +291,25 @@ def generate(
progress.msg_prefix = f'[{str(line+1)}/{str(len(texts))}]'
print(f"{progress.msg_prefix} Generating line: {cut_text}")
start_time = time.time()
gen, additionals = tts.tts(cut_text, **settings )
# do setting editing
match = re.findall(r'^(\{.+\}) (.+?)$', cut_text)
if match and len(match) > 0:
match = match[0]
try:
override = json.loads(match[0])
except Exception as e:
print(e)
raise Exception("Prompt settings editing requested, but received invalid JSON")
cut_text = match[1].strip()
new_settings = get_settings( override )
gen, additionals = tts.tts(cut_text, **new_settings )
else:
gen, additionals = tts.tts(cut_text, **settings )
seed = additionals[0]
run_time = time.time()-start_time
print(f"Generating line took {run_time} seconds")
@ -327,19 +393,6 @@ def generate(
'model_hash': tts.autoregressive_model_hash if hasattr(tts, 'autoregressive_model_hash') else None,
}
"""
# kludgy yucky codesmells
for name in audio_cache:
if 'output' not in audio_cache[name]:
continue
#output_voices.append(f'{outdir}/{voice}_{name}.wav')
output_voices.append(name)
if not args.embed_output_metadata:
with open(f'{outdir}/{voice}_{name}.json', 'w', encoding="utf-8") as f:
f.write(json.dumps(info, indent='\t') )
"""
if args.voice_fixer:
if not voicefixer:
progress(0, "Loading voicefix...")
@ -1057,8 +1110,8 @@ def prepare_dataset( files, outdir, language=None, skip_existings=False, progres
files = sorted(files)
previous_list = []
parsed_list = []
if skip_existings and os.path.exists(f'{outdir}/train.txt'):
parsed_list = []
with open(f'{outdir}/train.txt', 'r', encoding="utf-8") as f:
parsed_list = f.readlines()
@ -1103,20 +1156,13 @@ def prepare_dataset( files, outdir, language=None, skip_existings=False, progres
line = f"{sliced_name}|{segment['text'].strip()}"
transcription.append(line)
with open(f'{outdir}/train.txt', 'a', encoding="utf-8") as f:
f.write(f'{line}\n')
f.write(f'\n{line}')
do_gc()
with open(f'{outdir}/whisper.json', 'w', encoding="utf-8") as f:
f.write(json.dumps(results, indent='\t'))
if len(parsed_list) > 0:
transcription = parsed_list + transcription
joined = '\n'.join(transcription)
with open(f'{outdir}/train.txt', 'w', encoding="utf-8") as f:
f.write(joined)
unload_whisper()
return f"Processed dataset to: {outdir}\n{joined}"
@ -1688,6 +1734,22 @@ def read_generate_settings(file, read_latents=True):
latents,
)
def version_check_tts( min_version ):
global tts
if not tts:
raise Exception("TTS is not initialized")
if not hasattr(tts, 'version'):
return False
if min_version[0] > tts.version[0]:
return True
if min_version[1] > tts.version[1]:
return True
if min_version[2] >= tts.version[2]:
return True
return False
def load_tts( restart=False, model=None ):
global args
global tts