forked from mrq/ai-voice-cloning
clamp batch size to sample count when generating for the sickos that want that, added setting to remove non-final output after a generation, something else I forgot already
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parent
f119993fb5
commit
9e64dad785
79
src/utils.py
79
src/utils.py
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@ -151,6 +151,11 @@ def generate(
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'cvvp_amount': cvvp_weight,
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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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if num_autoregressive_samples < args.sample_batch_size:
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settings['sample_batch_size'] = num_autoregressive_samples
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if delimiter is None:
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delimiter = "\n"
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elif delimiter == "\\n":
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@ -301,30 +306,61 @@ def generate(
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'time': time.time()-full_start_time,
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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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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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#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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load_voicefixer()
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fixed_output_voices = []
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for path in progress.tqdm(output_voices, desc="Running voicefix..."):
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fixed = path.replace(".wav", "_fixed.wav")
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fixed_cache = {}
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for name in progress.tqdm(audio_cache, desc="Running voicefix..."):
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del audio_cache[name]['audio']
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if 'output' not in audio_cache[name] or not audio_cache[name]['output']:
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continue
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path = f'{outdir}/{voice}_{name}.wav'
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fixed = f'{outdir}/{voice}_{name}_fixed.wav'
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voicefixer.restore(
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input=path,
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output=fixed,
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cuda=get_device_name() == "cuda" and args.voice_fixer_use_cuda,
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#mode=mode,
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)
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fixed_output_voices.append(fixed)
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output_voices = fixed_output_voices
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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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'output': True
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}
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audio_cache[name]['output'] = False
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for name in fixed_cache:
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audio_cache[name] = fixed_cache[name]
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for name in audio_cache:
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if 'output' not in audio_cache[name] or not audio_cache[name]['output']:
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if args.prune_nonfinal_outputs:
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audio_cache[name]['pruned'] = True
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os.remove(f'{outdir}/{voice}_{name}.wav')
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continue
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output_voices.append(f'{outdir}/{voice}_{name}.wav')
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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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with open(f'{get_voice_dir()}/{voice}/cond_latents.pth', 'rb') as f:
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@ -332,6 +368,9 @@ def generate(
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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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@ -490,7 +529,7 @@ def stop_training():
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training_process.kill()
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return "Training cancelled"
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def get_halfp_model():
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def get_halfp_model_path():
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autoregressive_model_path = get_model_path('autoregressive.pth')
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return autoregressive_model_path.replace(".pth", "_half.pth")
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@ -501,7 +540,7 @@ def convert_to_halfp():
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for k in model:
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model[k] = model[k].half()
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outfile = get_halfp_model()
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outfile = get_halfp_model_path()
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torch.save(model, outfile)
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print(f'Converted model to half precision: {outfile}')
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@ -733,13 +772,21 @@ def import_voices(files, saveAs=None, progress=None):
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print(f"Imported voice to {path}")
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def get_voice_list(dir=get_voice_dir()):
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def get_voice_list(dir=get_voice_dir(), append_defaults=False):
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os.makedirs(dir, exist_ok=True)
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return sorted([d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 ]) + ["microphone", "random"]
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res = sorted([d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 ])
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if append_defaults:
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res = res + ["random", "microphone"]
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return res
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def get_autoregressive_models(dir="./models/finetunes/"):
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os.makedirs(dir, exist_ok=True)
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return [get_model_path('autoregressive.pth')] + sorted([f'{dir}/{d}' for d in os.listdir(dir) if d[-4:] == ".pth" ])
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base = [get_model_path('autoregressive.pth')]
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halfp = get_halfp_model_path()
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if os.path.exists(halfp):
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base.append(halfp)
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return base + sorted([f'{dir}/{d}' for d in os.listdir(dir) if d[-4:] == ".pth" ])
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def get_dataset_list(dir="./training/"):
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return sorted([d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d)) and len(os.listdir(os.path.join(dir, d))) > 0 and "train.txt" in os.listdir(os.path.join(dir, d)) ])
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@ -842,6 +889,7 @@ def setup_args():
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'force-cpu-for-conditioning-latents': False,
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'defer-tts-load': False,
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'device-override': None,
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'prune-nonfinal-outputs': True,
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'whisper-model': "base",
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'autoregressive-model': None,
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'concurrency-count': 2,
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@ -867,6 +915,7 @@ def setup_args():
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parser.add_argument("--voice-fixer-use-cuda", action='store_true', default=default_arguments['voice-fixer-use-cuda'], help="Hints to voicefixer to use CUDA, if available.")
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parser.add_argument("--force-cpu-for-conditioning-latents", default=default_arguments['force-cpu-for-conditioning-latents'], action='store_true', help="Forces computing conditional latents to be done on the CPU (if you constantyl OOM on low chunk counts)")
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parser.add_argument("--defer-tts-load", default=default_arguments['defer-tts-load'], action='store_true', help="Defers loading TTS model")
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parser.add_argument("--prune-nonfinal-outputs", default=default_arguments['prune-nonfinal-outputs'], action='store_true', help="Deletes non-final output files on completing a generation")
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parser.add_argument("--device-override", default=default_arguments['device-override'], help="A device string to override pass through Torch")
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parser.add_argument("--whisper-model", default=default_arguments['whisper-model'], help="Specifies which whisper model to use for transcription.")
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parser.add_argument("--autoregressive-model", default=default_arguments['autoregressive-model'], help="Specifies which autoregressive model to use for sampling.")
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@ -901,7 +950,7 @@ def setup_args():
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return args
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def update_args( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, defer_tts_load, device_override, sample_batch_size, concurrency_count, output_sample_rate, output_volume ):
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def update_args( listen, share, check_for_updates, models_from_local_only, low_vram, embed_output_metadata, latents_lean_and_mean, voice_fixer, voice_fixer_use_cuda, force_cpu_for_conditioning_latents, defer_tts_load, prune_nonfinal_outputs, device_override, sample_batch_size, concurrency_count, output_sample_rate, output_volume ):
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global args
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args.listen = listen
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@ -911,6 +960,7 @@ def update_args( listen, share, check_for_updates, models_from_local_only, low_v
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args.low_vram = low_vram
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args.force_cpu_for_conditioning_latents = force_cpu_for_conditioning_latents
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args.defer_tts_load = defer_tts_load
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args.prune_nonfinal_outputs = prune_nonfinal_outputs
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args.device_override = device_override
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args.sample_batch_size = sample_batch_size
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args.embed_output_metadata = embed_output_metadata
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@ -932,6 +982,7 @@ def save_args_settings():
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'models-from-local-only':args.models_from_local_only,
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'force-cpu-for-conditioning-latents': args.force_cpu_for_conditioning_latents,
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'defer-tts-load': args.defer_tts_load,
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'prune-nonfinal-outputs': args.prune_nonfinal_outputs,
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'device-override': args.device_override,
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'whisper-model': args.whisper_model,
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'autoregressive-model': args.autoregressive_model,
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10
src/webui.py
10
src/webui.py
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@ -238,7 +238,7 @@ def save_training_settings_proxy( epochs, batch_size, learning_rate, learning_ra
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def update_voices():
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return (
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gr.Dropdown.update(choices=get_voice_list()),
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gr.Dropdown.update(choices=get_voice_list(append_defaults=True)),
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gr.Dropdown.update(choices=get_voice_list()),
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gr.Dropdown.update(choices=get_voice_list("./results/")),
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)
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@ -277,7 +277,8 @@ def setup_gradio():
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emotion = gr.Radio( ["Happy", "Sad", "Angry", "Disgusted", "Arrogant", "Custom"], value="Custom", label="Emotion", type="value", interactive=True )
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prompt = gr.Textbox(lines=1, label="Custom Emotion + Prompt (if selected)")
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voice = gr.Dropdown(get_voice_list(), label="Voice", type="value")
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voice_list = get_voice_list(append_defaults=True)
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voice = gr.Dropdown(choices=voice_list, label="Voice", type="value", value=voice_list[0]) # it'd be very cash money if gradio was able to default to the first value in the list without this shit
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mic_audio = gr.Audio( label="Microphone Source", source="microphone", type="filepath" )
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refresh_voices = gr.Button(value="Refresh Voice List")
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voice_latents_chunks = gr.Slider(label="Voice Chunks", minimum=1, maximum=64, value=1, step=1)
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@ -410,14 +411,15 @@ def setup_gradio():
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gr.Checkbox(label="Use CUDA for Voice Fixer", value=args.voice_fixer_use_cuda),
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gr.Checkbox(label="Force CPU for Conditioning Latents", value=args.force_cpu_for_conditioning_latents),
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gr.Checkbox(label="Do Not Load TTS On Startup", value=args.defer_tts_load),
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gr.Checkbox(label="Delete Non-Final Output", value=args.prune_nonfinal_outputs),
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gr.Textbox(label="Device Override", value=args.device_override),
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]
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with gr.Column():
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exec_inputs = exec_inputs + [
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gr.Number(label="Sample Batch Size", precision=0, value=args.sample_batch_size),
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gr.Number(label="Concurrency Count", precision=0, value=args.concurrency_count),
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gr.Number(label="Ouptut Sample Rate", precision=0, value=args.output_sample_rate),
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gr.Slider(label="Ouptut Volume", minimum=0, maximum=2, value=args.output_volume),
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gr.Number(label="Output Sample Rate", precision=0, value=args.output_sample_rate),
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gr.Slider(label="Output Volume", minimum=0, maximum=2, value=args.output_volume),
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]
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autoregressive_models = get_autoregressive_models()
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