forked from mrq/ai-voice-cloning
fried my brain trying to nail out bugs involving using solely ar model=auto
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d7a5ad9fd9
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2726d98ee1
108
src/utils.py
108
src/utils.py
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@ -96,20 +96,17 @@ def generate(
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do_gc()
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voices = {}
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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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if seed == 0:
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seed = None
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def fetch_voice( voice ):
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print(f"Loading voice: {voice}")
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sample_voice = None
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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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@ -117,37 +114,19 @@ def generate(
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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=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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voice_samples, conditioning_latents = load_voice(voice, model_hash=tts.autoregressive_model_hash)
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if progress is not None:
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progress(0, desc=f"Loading voice: {voice}")
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voice_samples, conditioning_latents = load_voice(voice, model_hash=tts.autoregressive_model_hash)
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if voice_samples and len(voice_samples) > 0:
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conditioning_latents = compute_latents(voice=voice, voice_samples=voice_samples, voice_latents_chunks=voice_latents_chunks)
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if conditioning_latents is None:
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conditioning_latents = compute_latents(voice=voice, voice_samples=voice_samples, voice_latents_chunks=voice_latents_chunks)
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sample_voice = torch.cat(voice_samples, dim=-1).squeeze().cpu()
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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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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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if conditioning_latents is not None and len(conditioning_latents) == 2 and cvvp_weight > 0:
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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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autoregressive_model = args.autoregressive_model
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if autoregressive_model == "auto":
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autoregressive_model = deduce_autoregressive_model(voice)
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return (voice_samples, conditioning_latents, sample_voice)
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def get_settings( override=None ):
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settings = {
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@ -163,8 +142,8 @@ def generate(
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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': None,
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'conditioning_latents': None,
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'use_deterministic_seed': seed,
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'return_deterministic_state': True,
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@ -175,31 +154,26 @@ def generate(
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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': autoregressive_model,
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'autoregressive_model': args.autoregressive_model,
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}
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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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selected_voice = voice
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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 "autoregressive_model" in override:
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if override["autoregressive_model"] == "auto":
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override["autoregressive_model"] = deduce_autoregressive_model(voice)
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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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selected_voice = override['voice']
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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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tts.load_autoregressive_model( settings["autoregressive_model"] )
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if settings['autoregressive_model'] is not None:
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if settings['autoregressive_model'] == "auto":
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settings['autoregressive_model'] = deduce_autoregressive_model(selected_voice)
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tts.load_autoregressive_model(settings['autoregressive_model'])
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settings['voice_samples'], settings['conditioning_latents'], _ = fetch_voice(voice=selected_voice)
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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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@ -208,11 +182,13 @@ def generate(
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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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if settings['conditioning_latents'] is not None and len(settings['conditioning_latents']) == 2 and settings['cvvp_amount'] > 0:
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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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settings['cvvp_amount'] = 0
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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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@ -355,16 +331,12 @@ def generate(
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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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cut_text = match[1].strip()
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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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used_settings = get_settings( override )
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else:
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used_settings = settings.copy()
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gen, additionals = tts.tts(cut_text, **used_settings )
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settings = get_settings( override=override )
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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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@ -377,15 +349,15 @@ def generate(
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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
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settings['text'] = cut_text
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settings['time'] = run_time
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settings['datetime'] = datetime.now().isoformat(),
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settings['model'] = tts.autoregressive_model_path
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settings['model_hash'] = tts.autoregressive_model_hash
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audio_cache[name] = {
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'audio': audio,
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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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'settings': get_info(voice=override['voice'] if override and 'voice' in override else voice, settings=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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@ -485,7 +457,7 @@ def generate(
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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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info['seed'] = seed
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if 'latents' in info:
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del info['latents']
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@ -619,8 +591,10 @@ def compute_latents(voice=None, voice_samples=None, voice_latents_chunks=0, prog
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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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torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth')
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outfile = f'{get_voice_dir()}/{voice}/cond_latents_{tts.autoregressive_model_hash[:8]}.pth'
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torch.save(conditioning_latents, outfile)
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print(f'Saved voice latents: {outfile}')
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return conditioning_latents
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