diff --git a/src/utils.py b/src/utils.py
index 8991314..309b72f 100755
--- a/src/utils.py
+++ b/src/utils.py
@@ -31,7 +31,7 @@ import pandas as pd
from datetime import datetime
from datetime import timedelta
-from tortoise.api import TextToSpeech, MODELS, get_model_path
+from tortoise.api import TextToSpeech, MODELS, get_model_path, pad_or_truncate
from tortoise.utils.audio import load_audio, load_voice, load_voices, get_voice_dir
from tortoise.utils.text import split_and_recombine_text
from tortoise.utils.device import get_device_name, set_device_name
@@ -89,6 +89,8 @@ def generate(
if tts_loading:
raise Exception("TTS is still initializing...")
load_tts()
+ if hasattr(tts, "loading") and tts.loading:
+ raise Exception("TTS is still initializing...")
do_gc()
@@ -121,17 +123,8 @@ def generate(
voice_samples, conditioning_latents = load_voice(voice)
if voice_samples and len(voice_samples) > 0:
+ 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()
-
- 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:
if conditioning_latents is not None:
@@ -551,6 +544,10 @@ def update_baseline_for_latents_chunks( voice ):
if not os.path.isdir(path):
return 1
+ dataset_file = f'./training/{voice}/train.txt'
+ if os.path.exists(dataset_file):
+ return 0 # 0 will leverage using the LJspeech dataset for computing latents
+
files = os.listdir(path)
total = 0
@@ -565,11 +562,13 @@ def update_baseline_for_latents_chunks( voice ):
total_duration += duration
total = total + 1
+
+ # brain too fried to figure out a better way
if args.autocalculate_voice_chunk_duration_size == 0:
return int(total_duration / total) if total > 0 else 1
return int(total_duration / args.autocalculate_voice_chunk_duration_size) if total_duration > 0 else 1
-def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
+def compute_latents(voice=None, voice_samples=None, voice_latents_chunks=0, progress=None):
global tts
global args
@@ -581,12 +580,42 @@ def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm
raise Exception("TTS is still initializing...")
load_tts()
- voice_samples, conditioning_latents = load_voice(voice, load_latents=False)
+ if hasattr(tts, "loading") and tts.loading:
+ raise Exception("TTS is still initializing...")
+
+ if voice:
+ load_from_dataset = voice_latents_chunks == 0
+
+ if load_from_dataset:
+ dataset_path = f'./training/{voice}/train.txt'
+ if not os.path.exists(dataset_path):
+ load_from_dataset = False
+ else:
+ with open(dataset_path, 'r', encoding="utf-8") as f:
+ lines = f.readlines()
+
+ print("Leveraging LJSpeech dataset for computing latents")
+
+ voice_samples = []
+ max_length = 0
+ for line in lines:
+ filename = f'./training/{voice}/{line.split("|")[0]}'
+
+ waveform = load_audio(filename, 22050)
+ max_length = max(max_length, waveform.shape[-1])
+ voice_samples.append(waveform)
+
+ for i in range(len(voice_samples)):
+ voice_samples[i] = pad_or_truncate(voice_samples[i], max_length)
+
+ voice_latents_chunks = len(voice_samples)
+ if not load_from_dataset:
+ voice_samples, _ = load_voice(voice, load_latents=False)
if voice_samples is None:
return
- 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)
+ conditioning_latents = tts.get_conditioning_latents(voice_samples, return_mels=not args.latents_lean_and_mean, slices=voice_latents_chunks, force_cpu=args.force_cpu_for_conditioning_latents, progress=progress)
if len(conditioning_latents) == 4:
conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
@@ -596,7 +625,7 @@ def compute_latents(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm
else:
torch.save(conditioning_latents, f'{get_voice_dir()}/{voice}/cond_latents.pth')
- return voice
+ return conditioning_latents
# superfluous, but it cleans up some things
class TrainingState():
@@ -1847,6 +1876,10 @@ def update_autoregressive_model(autoregressive_model_path):
if tts_loading:
raise Exception("TTS is still initializing...")
return
+
+ if hasattr(tts, "loading") and tts.loading:
+ raise Exception("TTS is still initializing...")
+
print(f"Loading model: {autoregressive_model_path}")
tts.load_autoregressive_model(autoregressive_model_path)
@@ -1867,6 +1900,9 @@ def update_vocoder_model(vocoder_model):
raise Exception("TTS is still initializing...")
return
+ if hasattr(tts, "loading") and tts.loading:
+ raise Exception("TTS is still initializing...")
+
print(f"Loading model: {vocoder_model}")
tts.load_vocoder_model(vocoder_model)
print(f"Loaded model: {tts.vocoder_model}")
diff --git a/src/webui.py b/src/webui.py
index 3090b5d..abc73b3 100755
--- a/src/webui.py
+++ b/src/webui.py
@@ -163,6 +163,11 @@ def history_view_results( voice ):
gr.Dropdown.update(choices=sorted(files))
)
+def compute_latents_proxy(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
+ compute_latents( voice=voice, voice_latents_chunks=voice_latents_chunks, progress=progress )
+ return voice
+
+
def import_voices_proxy(files, name, progress=gr.Progress(track_tqdm=True)):
import_voices(files, name, progress)
return gr.update()
@@ -387,7 +392,7 @@ def setup_gradio():
prompt = gr.Textbox(lines=1, label="Custom Emotion")
voice = gr.Dropdown(choices=voice_list_with_defaults, label="Voice", type="value", value=voice_list_with_defaults[0]) # it'd be very cash money if gradio was able to default to the first value in the list without this shit
mic_audio = gr.Audio( label="Microphone Source", source="microphone", type="filepath", visible=False )
- voice_latents_chunks = gr.Slider(label="Voice Chunks", minimum=1, maximum=128, value=1, step=1)
+ voice_latents_chunks = gr.Number(label="Voice Chunks", precision=0, value=0)
with gr.Row():
refresh_voices = gr.Button(value="Refresh Voice List")
recompute_voice_latents = gr.Button(value="(Re)Compute Voice Latents")
@@ -704,7 +709,7 @@ def setup_gradio():
],
)
- recompute_voice_latents.click(compute_latents,
+ recompute_voice_latents.click(compute_latents_proxy,
inputs=[
voice,
voice_latents_chunks,