ken11o2/tortoise-tts
1
0
Fork 0
forked from mrq/tortoise-tts
Code Pull Requests Activity

revamped result formatting, added "kludgy" stop button

Browse Source
This commit is contained in:
mrq 2023-02-10 22:12:37 +00:00
parent 9e0fbff545
commit 4f903159ee
2 changed files with 49 additions and 20 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
tortoise/api.py
View File

@ -36,6 +36,8 @@ from tortoise.utils.device import get_device, get_device_name, get_device_batch_
pbar = None pbar = None
STOP_SIGNAL = False
MODELS_DIR = os.environ.get('TORTOISE_MODELS_DIR') MODELS_DIR = os.environ.get('TORTOISE_MODELS_DIR')
MODELS = { MODELS = {
'autoregressive.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/autoregressive.pth', 'autoregressive.pth': 'https://huggingface.co/jbetker/tortoise-tts-v2/resolve/main/.models/autoregressive.pth',
@ -49,6 +51,11 @@ MODELS = {
} }
def tqdm_override(arr, verbose=False, progress=None, desc=None): def tqdm_override(arr, verbose=False, progress=None, desc=None):
global STOP_SIGNAL
if STOP_SIGNAL:
STOP_SIGNAL = False
raise Exception("Kill signal detected")
if verbose and desc is not None: if verbose and desc is not None:
print(desc) print(desc)
@ -60,6 +67,7 @@ def download_models(specific_models=None):
""" """
Call to download all the models that Tortoise uses. Call to download all the models that Tortoise uses.
""" """
os.makedirs(MODELS_DIR, exist_ok=True) os.makedirs(MODELS_DIR, exist_ok=True)
def show_progress(block_num, block_size, total_size): def show_progress(block_num, block_size, total_size):

61
webui.py
View File

@ -16,6 +16,8 @@ from datetime import datetime
from fastapi import FastAPI from fastapi import FastAPI
import tortoise.api
from tortoise.api import TextToSpeech from tortoise.api import TextToSpeech
from tortoise.utils.audio import load_audio, load_voice, load_voices, get_voice_dir 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.text import split_and_recombine_text
@ -124,7 +126,7 @@ def generate(
start_time = time.time() start_time = time.time()
outdir = f"./results/{voice}/{int(start_time)}/" outdir = f"./results/{voice}/"
os.makedirs(outdir, exist_ok=True) os.makedirs(outdir, exist_ok=True)
audio_cache = {} audio_cache = {}
@ -140,6 +142,22 @@ def generate(
volume_adjust = torchaudio.transforms.Vol(gain=args.output_volume, gain_type="amplitude") if args.output_volume != 1 else None volume_adjust = torchaudio.transforms.Vol(gain=args.output_volume, gain_type="amplitude") if args.output_volume != 1 else None
idx = 0
for i, file in enumerate(os.listdir(outdir)):
if file[-4:] == ".wav":
idx = idx + 1
def get_name(line=0, candidate=0, combined=False):
name = f"{idx}"
if len(texts) > 1:
name = f"{name}_{line}"
if candidates > 1:
name = f"{name}_{candidate}"
if combined:
return f"{idx}_combined"
return name
for line, cut_text in enumerate(texts): for line, cut_text in enumerate(texts):
if emotion == "Custom": if emotion == "Custom":
if prompt.strip() != "": if prompt.strip() != "":
@ -154,13 +172,14 @@ def generate(
if isinstance(gen, list): if isinstance(gen, list):
for j, g in enumerate(gen): for j, g in enumerate(gen):
os.makedirs(f'{outdir}/candidate_{j}', exist_ok=True) name = get_name(line=line, candidate=j)
audio_cache[f"candidate_{j}/result_{line}.wav"] = { audio_cache[name] = {
'audio': g, 'audio': g,
'text': cut_text, 'text': cut_text,
} }
else: else:
audio_cache[f"result_{line}.wav"] = { name = get_name(line=line)
audio_cache[name] = {
'audio': gen, 'audio': gen,
'text': cut_text, 'text': cut_text,
} }
@ -173,7 +192,7 @@ def generate(
audio = volume_adjust(audio) audio = volume_adjust(audio)
audio_cache[k]['audio'] = audio audio_cache[k]['audio'] = audio
torchaudio.save(f'{outdir}/{k}', audio, args.output_sample_rate) torchaudio.save(f'{outdir}/{voice}_{k}.wav', audio, args.output_sample_rate)
output_voice = None output_voice = None
@ -182,30 +201,29 @@ def generate(
audio_clips = [] audio_clips = []
for line in range(len(texts)): for line in range(len(texts)):
if isinstance(gen, list): if isinstance(gen, list):
audio = audio_cache[f'candidate_{candidate}/result_{line}.wav']['audio'] name = get_name(line=line, candidate=candidate)
audio = audio_cache[name]['audio']
else: else:
audio = audio_cache[f'result_{line}.wav']['audio'] name = get_name(line=line)
audio = audio_cache[name]['audio']
audio_clips.append(audio) audio_clips.append(audio)
name = get_name(combined=True)
audio = torch.cat(audio_clips, dim=-1) audio = torch.cat(audio_clips, dim=-1)
torchaudio.save(f'{outdir}/combined_{candidate}.wav', audio, args.output_sample_rate) torchaudio.save(f'{outdir}/{voice}_{name}.wav', audio, args.output_sample_rate)
audio = audio.squeeze(0).cpu() audio = audio.squeeze(0).cpu()
audio_cache[f'combined_{candidate}.wav'] = { audio_cache[name] = {
'audio': audio, 'audio': audio,
'text': cut_text, 'text': cut_text,
} }
if output_voice is None: if output_voice is None:
output_voice = f'{outdir}/combined_{candidate}.wav' output_voice = f'{outdir}/{voice}_{name}.wav'
# output_voice = audio # output_voice = audio
else: else:
if isinstance(gen, list): name = get_name()
output_voice = f'{outdir}/candidate_0/result_0.wav' output_voice = f'{outdir}/{voice}_{name}.wav'
#output_voice = gen[0]
else:
output_voice = f'{outdir}/result_0.wav'
#output_voice = gen
info = { info = {
'text': text, 'text': text,
@ -231,7 +249,7 @@ def generate(
'time': time.time()-start_time, 'time': time.time()-start_time,
} }
with open(f'{outdir}/input.json', 'w', encoding="utf-8") as f: with open(f'{outdir}/input_{idx}.json', 'w', encoding="utf-8") as f:
f.write(json.dumps(info, indent='\t') ) f.write(json.dumps(info, indent='\t') )
if voice is not None and conditioning_latents is not None: if voice is not None and conditioning_latents is not None:
@ -242,7 +260,7 @@ def generate(
for path in audio_cache: for path in audio_cache:
info['text'] = audio_cache[path]['text'] info['text'] = audio_cache[path]['text']
metadata = music_tag.load_file(f"{outdir}/{path}") metadata = music_tag.load_file(f"{outdir}/{voice}_{path}.wav")
metadata['lyrics'] = json.dumps(info) metadata['lyrics'] = json.dumps(info)
metadata.save() metadata.save()
@ -389,6 +407,9 @@ def check_for_updates():
def reload_tts(): def reload_tts():
tts = setup_tortoise() tts = setup_tortoise()
def cancel_generate():
tortoise.api.STOP_SIGNAL = True
def update_voices(): def update_voices():
return gr.Dropdown.update(choices=sorted(os.listdir(get_voice_dir())) + ["microphone"]) return gr.Dropdown.update(choices=sorted(os.listdir(get_voice_dir())) + ["microphone"])
@ -574,7 +595,7 @@ def setup_gradio():
usedSeed = gr.Textbox(label="Seed", placeholder="0", interactive=False) usedSeed = gr.Textbox(label="Seed", placeholder="0", interactive=False)
submit = gr.Button(value="Generate") submit = gr.Button(value="Generate")
#stop = gr.Button(value="Stop") stop = gr.Button(value="Stop")
with gr.Tab("Utilities"): with gr.Tab("Utilities"):
with gr.Row(): with gr.Row():
with gr.Column(): with gr.Column():
@ -676,7 +697,7 @@ def setup_gradio():
if args.check_for_updates: if args.check_for_updates:
webui.load(check_for_updates) webui.load(check_for_updates)
#stop.click(fn=None, inputs=None, outputs=None, cancels=[submit_event]) stop.click(fn=cancel_generate, inputs=None, outputs=None, cancels=[submit_event])
webui.queue(concurrency_count=args.concurrency_count) webui.queue(concurrency_count=args.concurrency_count)