fried my brain trying to nail out bugs involving using solely ar model=auto
This commit is contained in:
parent
d7a5ad9fd9
commit
2726d98ee1
100
src/utils.py
100
src/utils.py
|
|
@ -96,20 +96,17 @@ def generate(
|
|||
|
||||
do_gc()
|
||||
|
||||
voices = {}
|
||||
|
||||
voice_samples = None
|
||||
conditioning_latents =None
|
||||
sample_voice = None
|
||||
|
||||
def fetch_voice( requested ):
|
||||
voice = requested
|
||||
|
||||
if voice in voices:
|
||||
return voices[voice]
|
||||
if seed == 0:
|
||||
seed = None
|
||||
|
||||
def fetch_voice( voice ):
|
||||
print(f"Loading voice: {voice}")
|
||||
|
||||
sample_voice = None
|
||||
if voice == "microphone":
|
||||
if mic_audio is None:
|
||||
raise Exception("Please provide audio from mic when choosing `microphone` as a voice input")
|
||||
|
|
@ -117,37 +114,19 @@ def generate(
|
|||
elif voice == "random":
|
||||
voice_samples, conditioning_latents = None, tts.get_random_conditioning_latents()
|
||||
else:
|
||||
if progress is not None:
|
||||
progress(0, desc=f"Loading voice: {voice}")
|
||||
# nasty check for users that, for whatever reason, updated the web UI but not mrq/tortoise-tts
|
||||
|
||||
voice_samples, conditioning_latents = load_voice(voice, model_hash=tts.autoregressive_model_hash)
|
||||
|
||||
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=voice_latents_chunks)
|
||||
|
||||
sample_voice = torch.cat(voice_samples, dim=-1).squeeze().cpu()
|
||||
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()
|
||||
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
|
||||
|
||||
if conditioning_latents is not None and len(conditioning_latents) == 2 and cvvp_weight > 0:
|
||||
print("Requesting weighing against CVVP weight, but voice latents are missing some extra data. Please regenerate your voice latents.")
|
||||
cvvp_weight = 0
|
||||
|
||||
autoregressive_model = args.autoregressive_model
|
||||
if autoregressive_model == "auto":
|
||||
autoregressive_model = deduce_autoregressive_model(voice)
|
||||
return (voice_samples, conditioning_latents, sample_voice)
|
||||
|
||||
def get_settings( override=None ):
|
||||
settings = {
|
||||
|
|
@ -163,8 +142,8 @@ def generate(
|
|||
'sample_batch_size': args.sample_batch_size,
|
||||
'diffusion_iterations': diffusion_iterations,
|
||||
|
||||
'voice_samples': voice_samples,
|
||||
'conditioning_latents': conditioning_latents,
|
||||
'voice_samples': None,
|
||||
'conditioning_latents': None,
|
||||
|
||||
'use_deterministic_seed': seed,
|
||||
'return_deterministic_state': True,
|
||||
|
|
@ -175,31 +154,26 @@ def generate(
|
|||
'half_p': "Half Precision" in experimental_checkboxes,
|
||||
'cond_free': "Conditioning-Free" in experimental_checkboxes,
|
||||
'cvvp_amount': cvvp_weight,
|
||||
'autoregressive_model': autoregressive_model,
|
||||
'autoregressive_model': args.autoregressive_model,
|
||||
}
|
||||
|
||||
# could be better to just do a ternary on everything above, but i am not a professional
|
||||
selected_voice = voice
|
||||
if override is not None:
|
||||
if 'voice' in override:
|
||||
voice = override['voice']
|
||||
|
||||
if "autoregressive_model" in override:
|
||||
if override["autoregressive_model"] == "auto":
|
||||
override["autoregressive_model"] = deduce_autoregressive_model(voice)
|
||||
|
||||
tts.load_autoregressive_model( override["autoregressive_model"] )
|
||||
|
||||
fetched = fetch_voice(voice)
|
||||
|
||||
settings['voice_samples'] = fetched[0]
|
||||
settings['conditioning_latents'] = fetched[1]
|
||||
selected_voice = override['voice']
|
||||
|
||||
for k in override:
|
||||
if k not in settings:
|
||||
continue
|
||||
settings[k] = override[k]
|
||||
|
||||
tts.load_autoregressive_model( settings["autoregressive_model"] )
|
||||
if settings['autoregressive_model'] is not None:
|
||||
if settings['autoregressive_model'] == "auto":
|
||||
settings['autoregressive_model'] = deduce_autoregressive_model(selected_voice)
|
||||
tts.load_autoregressive_model(settings['autoregressive_model'])
|
||||
|
||||
settings['voice_samples'], settings['conditioning_latents'], _ = fetch_voice(voice=selected_voice)
|
||||
|
||||
# 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
|
||||
|
|
@ -209,9 +183,11 @@ def generate(
|
|||
if num_autoregressive_samples < sample_batch_size:
|
||||
settings['sample_batch_size'] = num_autoregressive_samples
|
||||
|
||||
return settings
|
||||
if settings['conditioning_latents'] is not None and len(settings['conditioning_latents']) == 2 and settings['cvvp_amount'] > 0:
|
||||
print("Requesting weighing against CVVP weight, but voice latents are missing some extra data. Please regenerate your voice latents.")
|
||||
settings['cvvp_amount'] = 0
|
||||
|
||||
settings = get_settings()
|
||||
return settings
|
||||
|
||||
if not delimiter:
|
||||
delimiter = "\n"
|
||||
|
|
@ -355,16 +331,12 @@ def generate(
|
|||
match = match[0]
|
||||
try:
|
||||
override = json.loads(match[0])
|
||||
cut_text = match[1].strip()
|
||||
except Exception as e:
|
||||
print(e)
|
||||
raise Exception("Prompt settings editing requested, but received invalid JSON")
|
||||
|
||||
cut_text = match[1].strip()
|
||||
used_settings = get_settings( override )
|
||||
else:
|
||||
used_settings = settings.copy()
|
||||
|
||||
gen, additionals = tts.tts(cut_text, **used_settings )
|
||||
settings = get_settings( override=override )
|
||||
gen, additionals = tts.tts(cut_text, **settings )
|
||||
|
||||
seed = additionals[0]
|
||||
run_time = time.time()-start_time
|
||||
|
|
@ -377,15 +349,15 @@ def generate(
|
|||
audio = g.squeeze(0).cpu()
|
||||
name = get_name(line=line, candidate=j)
|
||||
|
||||
used_settings['text'] = cut_text
|
||||
used_settings['time'] = run_time
|
||||
used_settings['datetime'] = datetime.now().isoformat(),
|
||||
used_settings['model'] = tts.autoregressive_model_path
|
||||
used_settings['model_hash'] = tts.autoregressive_model_hash
|
||||
settings['text'] = cut_text
|
||||
settings['time'] = run_time
|
||||
settings['datetime'] = datetime.now().isoformat(),
|
||||
settings['model'] = tts.autoregressive_model_path
|
||||
settings['model_hash'] = tts.autoregressive_model_hash
|
||||
|
||||
audio_cache[name] = {
|
||||
'audio': audio,
|
||||
'settings': get_info(voice=override['voice'] if override and 'voice' in override else voice, settings=used_settings)
|
||||
'settings': get_info(voice=override['voice'] if override and 'voice' in override else voice, settings=settings)
|
||||
}
|
||||
# save here in case some error happens mid-batch
|
||||
torchaudio.save(f'{outdir}/{voice}_{name}.wav', audio, tts.output_sample_rate)
|
||||
|
|
@ -485,7 +457,7 @@ def generate(
|
|||
info = get_info(voice=voice, latents=False)
|
||||
print(f"Generation took {info['time']} seconds, saved to '{output_voices[0]}'\n")
|
||||
|
||||
info['seed'] = settings['use_deterministic_seed']
|
||||
info['seed'] = seed
|
||||
if 'latents' in info:
|
||||
del info['latents']
|
||||
|
||||
|
|
@ -620,7 +592,9 @@ def compute_latents(voice=None, voice_samples=None, voice_latents_chunks=0, prog
|
|||
if len(conditioning_latents) == 4:
|
||||
conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
|
||||
|
||||
torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth')
|
||||
outfile = f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth'
|
||||
torch.save(conditioning_latents, outfile)
|
||||
print(f'Saved voice latents: {outfile}')
|
||||
|
||||
return conditioning_latents
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user