From b6440091fbfbf44deffafa82ec284dad274a8e9e Mon Sep 17 00:00:00 2001
From: mrq <mrq@ecker.tech>
Date: Wed, 26 Apr 2023 04:48:09 +0000
Subject: [PATCH] Very, very, VERY, barebones integration with Bark
(documentation soon)
---
src/utils.py | 456 ++++++++++++++++++++++++++++++++++++++++++++++-----
src/webui.py | 54 +++---
2 files changed, 450 insertions(+), 60 deletions(-)
diff --git a/src/utils.py b/src/utils.py
index 9b07d93..3cf50fe 100755
--- a/src/utils.py
+++ b/src/utils.py
@@ -31,6 +31,7 @@ import music_tag
import gradio as gr
import gradio.utils
import pandas as pd
+import numpy as np
from glob import glob
from datetime import datetime
@@ -65,6 +66,7 @@ MIN_TRAINING_DURATION = 0.6
MAX_TRAINING_DURATION = 11.6097505669
VALLE_ENABLED = False
+BARK_ENABLED = False
try:
from vall_e.emb.qnt import encode as valle_quantize
@@ -76,11 +78,98 @@ try:
VALLE_ENABLED = True
except Exception as e:
+ if False: # args.tts_backend == "vall-e":
+ raise e
pass
if VALLE_ENABLED:
TTSES.append('vall-e')
+try:
+ from bark.generation import SAMPLE_RATE as BARK_SAMPLE_RATE, ALLOWED_PROMPTS, preload_models, codec_decode, generate_coarse, generate_fine, generate_text_semantic, load_codec_model
+ from bark.api import generate_audio as bark_generate_audio
+ from encodec.utils import convert_audio
+
+ from scipy.io.wavfile import write as write_wav
+
+ BARK_ENABLED = True
+except Exception as e:
+ if False: # args.tts_backend == "bark":
+ raise e
+ pass
+
+if BARK_ENABLED:
+ TTSES.append('bark')
+ class Bark_TTS():
+ def __init__(self, small=False):
+ self.input_sample_rate = BARK_SAMPLE_RATE
+ self.output_sample_rate = args.output_sample_rate
+
+ preload_models(
+ text_use_gpu=True,
+ coarse_use_gpu=True,
+ fine_use_gpu=True,
+ codec_use_gpu=True,
+
+ text_use_small=small,
+ coarse_use_small=small,
+ fine_use_small=small,
+
+ force_reload=False
+ )
+
+ def create_voice( self, voice, device='cuda' ):
+ transcription_json = f'./training/{voice}/whisper.json'
+ if not os.path.exists(transcription_json):
+ raise f"Transcription for voice not found: {voice}"
+
+ transcriptions = json.load(open(transcription_json, 'r', encoding="utf-8"))
+ candidates = []
+ for file in transcriptions:
+ result = transcriptions[file]
+ for segment in result['segments']:
+ entry = (
+ file.replace(".wav", f"_{pad(segment['id'], 4)}.wav"),
+ segment['end'] - segment['start'],
+ segment['text']
+ )
+ candidates.append(entry)
+
+ candidates.sort(key=lambda x: x[1])
+ candidate = random.choice(candidates)
+ audio_filepath = f'./training/{voice}/audio/{candidate[0]}'
+ text = candidate[-1]
+
+ print("Using as reference:", audio_filepath, text)
+
+ # Load and pre-process the audio waveform
+ model = load_codec_model(use_gpu=True)
+ wav, sr = torchaudio.load(audio_filepath)
+ wav = convert_audio(wav, sr, model.sample_rate, model.channels)
+ wav = wav.unsqueeze(0).to(device)
+
+ # Extract discrete codes from EnCodec
+ with torch.no_grad():
+ encoded_frames = model.encode(wav)
+ codes = torch.cat([encoded[0] for encoded in encoded_frames], dim=-1).squeeze().cpu().numpy() # [n_q, T]
+
+ # get seconds of audio
+ seconds = wav.shape[-1] / model.sample_rate
+ # generate semantic tokens
+ semantic_tokens = generate_text_semantic(text, max_gen_duration_s=seconds, top_k=50, top_p=.95, temp=0.7)
+
+ output_path = './modules/bark/bark/assets/prompts/' + voice.replace("/", "_") + '.npz'
+ np.savez(output_path, fine_prompt=codes, coarse_prompt=codes[:2, :], semantic_prompt=semantic_tokens)
+
+ def inference( self, text, voice, text_temp=0.7, waveform_temp=0.7 ):
+ if not os.path.exists('./modules/bark/bark/assets/prompts/' + voice + '.npz'):
+ self.create_voice( voice )
+ voice = voice.replace("/", "_")
+ if voice not in ALLOWED_PROMPTS:
+ ALLOWED_PROMPTS.add( voice )
+
+ return (bark_generate_audio(text, history_prompt=voice, text_temp=text_temp, waveform_temp=waveform_temp), BARK_SAMPLE_RATE)
+
args = None
tts = None
tts_loading = False
@@ -96,6 +185,9 @@ training_state = None
current_voice = None
+def cleanup_voice_name( name ):
+ return name.split("/")[-1]
+
def resample( waveform, input_rate, output_rate=44100 ):
# mono-ize
waveform = torch.mean(waveform, dim=0, keepdim=True)
@@ -121,6 +213,291 @@ def generate(**kwargs):
return generate_tortoise(**kwargs)
if args.tts_backend == "vall-e":
return generate_valle(**kwargs)
+ if args.tts_backend == "bark":
+ return generate_bark(**kwargs)
+
+def generate_bark(**kwargs):
+ parameters = {}
+ parameters.update(kwargs)
+
+ voice = parameters['voice']
+ progress = parameters['progress'] if 'progress' in parameters else None
+ if parameters['seed'] == 0:
+ parameters['seed'] = None
+
+ usedSeed = parameters['seed']
+
+ global args
+ global tts
+
+ unload_whisper()
+ unload_voicefixer()
+
+ if not tts:
+ # should check if it's loading or unloaded, and load it if it's unloaded
+ if tts_loading:
+ raise Exception("TTS is still initializing...")
+ if progress is not None:
+ progress(0, "Initializing TTS...")
+ load_tts()
+ if hasattr(tts, "loading") and tts.loading:
+ raise Exception("TTS is still initializing...")
+
+ do_gc()
+
+ voice_samples = None
+ conditioning_latents = None
+ sample_voice = None
+
+ voice_cache = {}
+
+ def get_settings( override=None ):
+ settings = {
+ 'voice': parameters['voice'],
+ 'text_temp': float(parameters['temperature']),
+ 'waveform_temp': float(parameters['temperature']),
+ }
+
+ # 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:
+ selected_voice = override['voice']
+
+ for k in override:
+ if k not in settings:
+ continue
+ settings[k] = override[k]
+
+ return settings
+
+ if not parameters['delimiter']:
+ parameters['delimiter'] = "\n"
+ elif parameters['delimiter'] == "\\n":
+ parameters['delimiter'] = "\n"
+
+ if parameters['delimiter'] and parameters['delimiter'] != "" and parameters['delimiter'] in parameters['text']:
+ texts = parameters['text'].split(parameters['delimiter'])
+ else:
+ texts = split_and_recombine_text(parameters['text'])
+
+ full_start_time = time.time()
+
+ outdir = f"{args.results_folder}/{voice}/"
+ os.makedirs(outdir, exist_ok=True)
+
+ audio_cache = {}
+
+ volume_adjust = torchaudio.transforms.Vol(gain=args.output_volume, gain_type="amplitude") if args.output_volume != 1 else None
+
+ idx = 0
+ idx_cache = {}
+ for i, file in enumerate(os.listdir(outdir)):
+ filename = os.path.basename(file)
+ extension = os.path.splitext(filename)[1]
+ if extension != ".json" and extension != ".wav":
+ continue
+ match = re.findall(rf"^{cleanup_voice_name(voice)}_(\d+)(?:.+?)?{extension}$", filename)
+ if match and len(match) > 0:
+ key = int(match[0])
+ idx_cache[key] = True
+
+ if len(idx_cache) > 0:
+ keys = sorted(list(idx_cache.keys()))
+ idx = keys[-1] + 1
+
+ idx = pad(idx, 4)
+
+ def get_name(line=0, candidate=0, combined=False):
+ name = f"{idx}"
+ if combined:
+ name = f"{name}_combined"
+ elif len(texts) > 1:
+ name = f"{name}_{line}"
+ if parameters['candidates'] > 1:
+ name = f"{name}_{candidate}"
+ return name
+
+ def get_info( voice, settings = None, latents = True ):
+ info = {}
+ info.update(parameters)
+
+ info['time'] = time.time()-full_start_time
+ info['datetime'] = datetime.now().isoformat()
+
+ info['progress'] = None
+ del info['progress']
+
+ if info['delimiter'] == "\n":
+ info['delimiter'] = "\\n"
+
+ if settings is not None:
+ for k in settings:
+ if k in info:
+ info[k] = settings[k]
+ return info
+
+ INFERENCING = True
+ for line, cut_text in enumerate(texts):
+ progress.msg_prefix = f'[{str(line+1)}/{str(len(texts))}]'
+ print(f"{progress.msg_prefix} Generating line: {cut_text}")
+ start_time = time.time()
+
+ # do setting editing
+ match = re.findall(r'^(\{.+\}) (.+?)$', cut_text)
+ override = None
+ if match and len(match) > 0:
+ match = match[0]
+ try:
+ override = json.loads(match[0])
+ cut_text = match[1].strip()
+ except Exception as e:
+ raise Exception("Prompt settings editing requested, but received invalid JSON")
+
+ settings = get_settings( override=override )
+
+ gen = tts.inference(cut_text, **settings )
+
+ run_time = time.time()-start_time
+ print(f"Generating line took {run_time} seconds")
+
+ if not isinstance(gen, list):
+ gen = [gen]
+
+ for j, g in enumerate(gen):
+ wav, sr = g
+ name = get_name(line=line, candidate=j)
+
+ settings['text'] = cut_text
+ settings['time'] = run_time
+ settings['datetime'] = datetime.now().isoformat()
+
+ # save here in case some error happens mid-batch
+ #torchaudio.save(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav', wav.cpu(), sr)
+ write_wav(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav', sr, wav)
+ wav, sr = torchaudio.load(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav')
+
+ audio_cache[name] = {
+ 'audio': wav,
+ 'settings': get_info(voice=override['voice'] if override and 'voice' in override else voice, settings=settings)
+ }
+
+ del gen
+ do_gc()
+ INFERENCING = False
+
+ for k in audio_cache:
+ audio = audio_cache[k]['audio']
+
+ audio, _ = resample(audio, tts.output_sample_rate, args.output_sample_rate)
+ if volume_adjust is not None:
+ audio = volume_adjust(audio)
+
+ audio_cache[k]['audio'] = audio
+ torchaudio.save(f'{outdir}/{cleanup_voice_name(voice)}_{k}.wav', audio, args.output_sample_rate)
+
+ output_voices = []
+ for candidate in range(parameters['candidates']):
+ if len(texts) > 1:
+ audio_clips = []
+ for line in range(len(texts)):
+ name = get_name(line=line, candidate=candidate)
+ audio = audio_cache[name]['audio']
+ audio_clips.append(audio)
+
+ name = get_name(candidate=candidate, combined=True)
+ audio = torch.cat(audio_clips, dim=-1)
+ torchaudio.save(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav', audio, args.output_sample_rate)
+
+ audio = audio.squeeze(0).cpu()
+ audio_cache[name] = {
+ 'audio': audio,
+ 'settings': get_info(voice=voice),
+ 'output': True
+ }
+ else:
+ name = get_name(candidate=candidate)
+ audio_cache[name]['output'] = True
+
+
+ if args.voice_fixer:
+ if not voicefixer:
+ progress(0, "Loading voicefix...")
+ load_voicefixer()
+
+ try:
+ fixed_cache = {}
+ for name in progress.tqdm(audio_cache, desc="Running voicefix..."):
+ del audio_cache[name]['audio']
+ if 'output' not in audio_cache[name] or not audio_cache[name]['output']:
+ continue
+
+ path = f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav'
+ fixed = f'{outdir}/{cleanup_voice_name(voice)}_{name}_fixed.wav'
+ voicefixer.restore(
+ input=path,
+ output=fixed,
+ cuda=get_device_name() == "cuda" and args.voice_fixer_use_cuda,
+ #mode=mode,
+ )
+
+ fixed_cache[f'{name}_fixed'] = {
+ 'settings': audio_cache[name]['settings'],
+ 'output': True
+ }
+ audio_cache[name]['output'] = False
+
+ for name in fixed_cache:
+ audio_cache[name] = fixed_cache[name]
+ except Exception as e:
+ print(e)
+ print("\nFailed to run Voicefixer")
+
+ for name in audio_cache:
+ if 'output' not in audio_cache[name] or not audio_cache[name]['output']:
+ if args.prune_nonfinal_outputs:
+ audio_cache[name]['pruned'] = True
+ os.remove(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav')
+ continue
+
+ output_voices.append(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav')
+
+ if not args.embed_output_metadata:
+ with open(f'{outdir}/{cleanup_voice_name(voice)}_{name}.json', 'w', encoding="utf-8") as f:
+ f.write(json.dumps(audio_cache[name]['settings'], indent='\t') )
+
+ if args.embed_output_metadata:
+ for name in progress.tqdm(audio_cache, desc="Embedding metadata..."):
+ if 'pruned' in audio_cache[name] and audio_cache[name]['pruned']:
+ continue
+
+ metadata = music_tag.load_file(f"{outdir}/{cleanup_voice_name(voice)}_{name}.wav")
+ metadata['lyrics'] = json.dumps(audio_cache[name]['settings'])
+ metadata.save()
+
+ if sample_voice is not None:
+ sample_voice = (tts.input_sample_rate, sample_voice.numpy())
+
+ info = get_info(voice=voice, latents=False)
+ print(f"Generation took {info['time']} seconds, saved to '{output_voices[0]}'\n")
+
+ info['seed'] = usedSeed
+ if 'latents' in info:
+ del info['latents']
+
+ os.makedirs('./config/', exist_ok=True)
+ with open(f'./config/generate.json', 'w', encoding="utf-8") as f:
+ f.write(json.dumps(info, indent='\t') )
+
+ stats = [
+ [ parameters['seed'], "{:.3f}".format(info['time']) ]
+ ]
+
+ return (
+ sample_voice,
+ output_voices,
+ stats,
+ )
def generate_valle(**kwargs):
parameters = {}
@@ -289,9 +666,9 @@ def generate_valle(**kwargs):
settings['datetime'] = datetime.now().isoformat()
# save here in case some error happens mid-batch
- #torchaudio.save(f'{outdir}/{voice}_{name}.wav', wav.cpu(), sr)
- soundfile.write(f'{outdir}/{voice}_{name}.wav', wav.cpu()[0,0], sr)
- wav, sr = torchaudio.load(f'{outdir}/{voice}_{name}.wav')
+ #torchaudio.save(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav', wav.cpu(), sr)
+ soundfile.write(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav', wav.cpu()[0,0], sr)
+ wav, sr = torchaudio.load(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav')
audio_cache[name] = {
'audio': wav,
@@ -310,7 +687,7 @@ def generate_valle(**kwargs):
audio = volume_adjust(audio)
audio_cache[k]['audio'] = audio
- torchaudio.save(f'{outdir}/{voice}_{k}.wav', audio, args.output_sample_rate)
+ torchaudio.save(f'{outdir}/{cleanup_voice_name(voice)}_{k}.wav', audio, args.output_sample_rate)
output_voices = []
for candidate in range(parameters['candidates']):
@@ -323,7 +700,7 @@ def generate_valle(**kwargs):
name = get_name(candidate=candidate, combined=True)
audio = torch.cat(audio_clips, dim=-1)
- torchaudio.save(f'{outdir}/{voice}_{name}.wav', audio, args.output_sample_rate)
+ torchaudio.save(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav', audio, args.output_sample_rate)
audio = audio.squeeze(0).cpu()
audio_cache[name] = {
@@ -348,8 +725,8 @@ def generate_valle(**kwargs):
if 'output' not in audio_cache[name] or not audio_cache[name]['output']:
continue
- path = f'{outdir}/{voice}_{name}.wav'
- fixed = f'{outdir}/{voice}_{name}_fixed.wav'
+ path = f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav'
+ fixed = f'{outdir}/{cleanup_voice_name(voice)}_{name}_fixed.wav'
voicefixer.restore(
input=path,
output=fixed,
@@ -373,13 +750,13 @@ def generate_valle(**kwargs):
if 'output' not in audio_cache[name] or not audio_cache[name]['output']:
if args.prune_nonfinal_outputs:
audio_cache[name]['pruned'] = True
- os.remove(f'{outdir}/{voice}_{name}.wav')
+ os.remove(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav')
continue
- output_voices.append(f'{outdir}/{voice}_{name}.wav')
+ output_voices.append(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav')
if not args.embed_output_metadata:
- with open(f'{outdir}/{voice}_{name}.json', 'w', encoding="utf-8") as f:
+ with open(f'{outdir}/{cleanup_voice_name(voice)}_{name}.json', 'w', encoding="utf-8") as f:
f.write(json.dumps(audio_cache[name]['settings'], indent='\t') )
if args.embed_output_metadata:
@@ -387,7 +764,7 @@ def generate_valle(**kwargs):
if 'pruned' in audio_cache[name] and audio_cache[name]['pruned']:
continue
- metadata = music_tag.load_file(f"{outdir}/{voice}_{name}.wav")
+ metadata = music_tag.load_file(f"{outdir}/{cleanup_voice_name(voice)}_{name}.wav")
metadata['lyrics'] = json.dumps(audio_cache[name]['settings'])
metadata.save()
@@ -415,8 +792,6 @@ def generate_valle(**kwargs):
stats,
)
-
-
def generate_tortoise(**kwargs):
parameters = {}
parameters.update(kwargs)
@@ -698,7 +1073,7 @@ def generate_tortoise(**kwargs):
'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)
+ torchaudio.save(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav', audio, tts.output_sample_rate)
del gen
do_gc()
@@ -712,7 +1087,7 @@ def generate_tortoise(**kwargs):
audio = volume_adjust(audio)
audio_cache[k]['audio'] = audio
- torchaudio.save(f'{outdir}/{voice}_{k}.wav', audio, args.output_sample_rate)
+ torchaudio.save(f'{outdir}/{cleanup_voice_name(voice)}_{k}.wav', audio, args.output_sample_rate)
output_voices = []
for candidate in range(parameters['candidates']):
@@ -725,7 +1100,7 @@ def generate_tortoise(**kwargs):
name = get_name(candidate=candidate, combined=True)
audio = torch.cat(audio_clips, dim=-1)
- torchaudio.save(f'{outdir}/{voice}_{name}.wav', audio, args.output_sample_rate)
+ torchaudio.save(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav', audio, args.output_sample_rate)
audio = audio.squeeze(0).cpu()
audio_cache[name] = {
@@ -750,8 +1125,8 @@ def generate_tortoise(**kwargs):
if 'output' not in audio_cache[name] or not audio_cache[name]['output']:
continue
- path = f'{outdir}/{voice}_{name}.wav'
- fixed = f'{outdir}/{voice}_{name}_fixed.wav'
+ path = f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav'
+ fixed = f'{outdir}/{cleanup_voice_name(voice)}_{name}_fixed.wav'
voicefixer.restore(
input=path,
output=fixed,
@@ -775,13 +1150,13 @@ def generate_tortoise(**kwargs):
if 'output' not in audio_cache[name] or not audio_cache[name]['output']:
if args.prune_nonfinal_outputs:
audio_cache[name]['pruned'] = True
- os.remove(f'{outdir}/{voice}_{name}.wav')
+ os.remove(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav')
continue
- output_voices.append(f'{outdir}/{voice}_{name}.wav')
+ output_voices.append(f'{outdir}/{cleanup_voice_name(voice)}_{name}.wav')
if not args.embed_output_metadata:
- with open(f'{outdir}/{voice}_{name}.json', 'w', encoding="utf-8") as f:
+ with open(f'{outdir}/{cleanup_voice_name(voice)}_{name}.json', 'w', encoding="utf-8") as f:
f.write(json.dumps(audio_cache[name]['settings'], indent='\t') )
if args.embed_output_metadata:
@@ -789,7 +1164,7 @@ def generate_tortoise(**kwargs):
if 'pruned' in audio_cache[name] and audio_cache[name]['pruned']:
continue
- metadata = music_tag.load_file(f"{outdir}/{voice}_{name}.wav")
+ metadata = music_tag.load_file(f"{outdir}/{cleanup_voice_name(voice)}_{name}.wav")
metadata['lyrics'] = json.dumps(audio_cache[name]['settings'])
metadata.save()
@@ -1096,9 +1471,9 @@ class TrainingState():
'ar-half.loss', 'nar-half.loss', 'ar-half+nar-half.loss',
'ar-quarter.loss', 'nar-quarter.loss', 'ar-quarter+nar-quarter.loss',
- # 'ar.loss.nll', 'nar.loss.nll',
- # 'ar-half.loss.nll', 'nar-half.loss.nll',
- # 'ar-quarter.loss.nll', 'nar-quarter.loss.nll',
+ 'ar.loss.nll', 'nar.loss.nll',
+ 'ar-half.loss.nll', 'nar-half.loss.nll',
+ 'ar-quarter.loss.nll', 'nar-quarter.loss.nll',
]
keys['accuracies'] = [
@@ -1464,14 +1839,14 @@ def run_training(config_path, verbose=False, keep_x_past_checkpoints=0, progress
return_code = training_state.process.wait()
training_state = None
-def update_training_dataplot(x_lim=None, y_lim=None, config_path=None):
+def update_training_dataplot(x_min=None, x_max=None, y_min=None, y_max=None, config_path=None):
global training_state
losses = None
lrs = None
grad_norms = None
- x_lim = [ 0, x_lim ]
- y_lim = [ 0, y_lim ]
+ x_lim = [ x_min, x_max ]
+ y_lim = [ y_min, y_max ]
if not training_state:
if config_path:
@@ -1490,23 +1865,23 @@ def update_training_dataplot(x_lim=None, y_lim=None, config_path=None):
losses = gr.LinePlot.update(
value = pd.DataFrame(training_state.statistics['loss']),
x_lim=x_lim, y_lim=y_lim,
- x="epoch", y="value",
+ x="it", y="value", # x="epoch",
title="Loss Metrics", color="type", tooltip=['epoch', 'it', 'value', 'type'],
width=500, height=350
)
if len(training_state.statistics['lr']) > 0:
lrs = gr.LinePlot.update(
value = pd.DataFrame(training_state.statistics['lr']),
- x_lim=x_lim, y_lim=y_lim,
- x="epoch", y="value",
+ x_lim=x_lim,
+ x="it", y="value", # x="epoch",
title="Learning Rate", color="type", tooltip=['epoch', 'it', 'value', 'type'],
width=500, height=350
)
if len(training_state.statistics['grad_norm']) > 0:
grad_norms = gr.LinePlot.update(
value = pd.DataFrame(training_state.statistics['grad_norm']),
- x_lim=x_lim, y_lim=y_lim,
- x="epoch", y="value",
+ x_lim=x_lim,
+ x="it", y="value", # x="epoch",
title="Gradient Normals", color="type", tooltip=['epoch', 'it', 'value', 'type'],
width=500, height=350
)
@@ -1649,13 +2024,13 @@ def whisper_transcribe( file, language=None ):
device = "cuda" if get_device_name() == "cuda" else "cpu"
if whisper_vad:
# omits a considerable amount of the end
- """
if args.whisper_batchsize > 1:
result = whisperx.transcribe_with_vad_parallel(whisper_model, file, whisper_vad, batch_size=args.whisper_batchsize, language=language, task="transcribe")
else:
result = whisperx.transcribe_with_vad(whisper_model, file, whisper_vad)
"""
result = whisperx.transcribe_with_vad(whisper_model, file, whisper_vad)
+ """
else:
result = whisper_model.transcribe(file)
@@ -1717,7 +2092,7 @@ def transcribe_dataset( voice, language=None, skip_existings=False, progress=Non
os.makedirs(f'{indir}/audio/', exist_ok=True)
TARGET_SAMPLE_RATE = 22050
- if args.tts_backend == "vall-e":
+ if args.tts_backend != "tortoise":
TARGET_SAMPLE_RATE = 24000
if tts:
TARGET_SAMPLE_RATE = tts.input_sample_rate
@@ -1735,7 +2110,7 @@ def transcribe_dataset( voice, language=None, skip_existings=False, progress=Non
try:
result = whisper_transcribe(file, language=language)
except Exception as e:
- print("Failed to transcribe:", file)
+ print("Failed to transcribe:", file, e)
continue
results[basename] = result
@@ -1802,7 +2177,7 @@ def slice_dataset( voice, trim_silence=True, start_offset=0, end_offset=0, resul
results = json.load(open(infile, 'r', encoding="utf-8"))
TARGET_SAMPLE_RATE = 22050
- if args.tts_backend == "vall-e":
+ if args.tts_backend != "tortoise":
TARGET_SAMPLE_RATE = 24000
if tts:
TARGET_SAMPLE_RATE = tts.input_sample_rate
@@ -1934,8 +2309,7 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
lines = { 'training': [], 'validation': [] }
segments = {}
- # I'm not sure how the VALL-E implementation decides what's validation and what's not
- if args.tts_backend == "vall-e":
+ if args.tts_backend != "tortoise":
text_length = 0
audio_length = 0
@@ -3008,6 +3382,10 @@ def load_tts( restart=False,
print(f"Loading VALL-E... (Config: {valle_model})")
tts = VALLE_TTS(config=args.valle_model)
+ elif args.tts_backend == "bark":
+
+ print(f"Loading Bark...")
+ tts = Bark_TTS(small=args.low_vram)
print("Loaded TTS, ready for generation.")
tts_loading = False
diff --git a/src/webui.py b/src/webui.py
index 66540c1..6bc4bbd 100755
--- a/src/webui.py
+++ b/src/webui.py
@@ -167,6 +167,10 @@ def reset_generate_settings_proxy():
return tuple(res)
def compute_latents_proxy(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
+ if args.tts_backend == "bark":
+ global tts
+ tts.create_voice( voice )
+ return voice
compute_latents( voice=voice, voice_latents_chunks=voice_latents_chunks, progress=progress )
return voice
@@ -222,13 +226,13 @@ def prepare_all_datasets( language, validation_text_length, validation_audio_len
print("Processing:", voice)
message = transcribe_dataset( voice=voice, language=language, skip_existings=skip_existings, progress=progress )
messages.append(message)
- """
if slice_audio:
for voice in voices:
print("Processing:", voice)
message = slice_dataset( voice, trim_silence=trim_silence, start_offset=slice_start_offset, end_offset=slice_end_offset, results=None, progress=progress )
messages.append(message)
+ """
for voice in voices:
print("Processing:", voice)
@@ -400,12 +404,13 @@ def setup_gradio():
outputs=GENERATE_SETTINGS["mic_audio"],
)
with gr.Column():
+ preset = None
GENERATE_SETTINGS["candidates"] = gr.Slider(value=1, minimum=1, maximum=6, step=1, label="Candidates", visible=args.tts_backend=="tortoise")
- GENERATE_SETTINGS["seed"] = gr.Number(value=0, precision=0, label="Seed")
+ GENERATE_SETTINGS["seed"] = gr.Number(value=0, precision=0, label="Seed", visible=args.tts_backend!="tortoise")
- preset = gr.Radio( ["Ultra Fast", "Fast", "Standard", "High Quality"], label="Preset", type="value", value="Ultra Fast" )
+ preset = gr.Radio( ["Ultra Fast", "Fast", "Standard", "High Quality"], label="Preset", type="value", value="Ultra Fast", visible=args.tts_backend=="tortoise" )
- GENERATE_SETTINGS["num_autoregressive_samples"] = gr.Slider(value=16, minimum=2, maximum=512, step=1, label="Samples")
+ GENERATE_SETTINGS["num_autoregressive_samples"] = gr.Slider(value=16, minimum=2, maximum=512, step=1, label="Samples", visible=args.tts_backend!="bark")
GENERATE_SETTINGS["diffusion_iterations"] = gr.Slider(value=30, minimum=0, maximum=512, step=1, label="Iterations", visible=args.tts_backend=="tortoise")
GENERATE_SETTINGS["temperature"] = gr.Slider(value=0.2, minimum=0, maximum=1, step=0.1, label="Temperature")
@@ -490,7 +495,7 @@ def setup_gradio():
merger_button = gr.Button(value="Run Merger")
with gr.Column():
merger_output = gr.TextArea(label="Console Output", max_lines=8)
- with gr.Tab("Training"):
+ with gr.Tab("Training", visible=args.tts_backend != "bark"):
with gr.Tab("Prepare Dataset"):
with gr.Row():
with gr.Column():
@@ -586,8 +591,10 @@ def setup_gradio():
keep_x_past_checkpoints = gr.Slider(label="Keep X Previous States", minimum=0, maximum=8, value=0, step=1)
with gr.Row():
- training_graph_x_lim = gr.Number(label="X Limit", precision=0, value=0)
- training_graph_y_lim = gr.Number(label="Y Limit", precision=0, value=0)
+ training_graph_x_min = gr.Number(label="X Min", precision=0, value=0)
+ training_graph_x_max = gr.Number(label="X Max", precision=0, value=0)
+ training_graph_y_min = gr.Number(label="Y Min", precision=0, value=0)
+ training_graph_y_max = gr.Number(label="Y Max", precision=0, value=0)
with gr.Row():
start_training_button = gr.Button(value="Train")
@@ -597,7 +604,7 @@ def setup_gradio():
with gr.Column():
training_loss_graph = gr.LinePlot(label="Training Metrics",
- x="epoch",
+ x="it", # x="epoch",
y="value",
title="Loss Metrics",
color="type",
@@ -606,7 +613,7 @@ def setup_gradio():
height=350,
)
training_lr_graph = gr.LinePlot(label="Training Metrics",
- x="epoch",
+ x="it", # x="epoch",
y="value",
title="Learning Rate",
color="type",
@@ -615,7 +622,7 @@ def setup_gradio():
height=350,
)
training_grad_norm_graph = gr.LinePlot(label="Training Metrics",
- x="epoch",
+ x="it", # x="epoch",
y="value",
title="Gradient Normals",
color="type",
@@ -765,13 +772,14 @@ def setup_gradio():
inputs=show_experimental_settings,
outputs=experimental_column
)
- preset.change(fn=update_presets,
- inputs=preset,
- outputs=[
- GENERATE_SETTINGS['num_autoregressive_samples'],
- GENERATE_SETTINGS['diffusion_iterations'],
- ],
- )
+ if preset:
+ preset.change(fn=update_presets,
+ inputs=preset,
+ outputs=[
+ GENERATE_SETTINGS['num_autoregressive_samples'],
+ GENERATE_SETTINGS['diffusion_iterations'],
+ ],
+ )
recompute_voice_latents.click(compute_latents_proxy,
inputs=[
@@ -860,8 +868,10 @@ def setup_gradio():
training_output.change(
fn=update_training_dataplot,
inputs=[
- training_graph_x_lim,
- training_graph_y_lim,
+ training_graph_x_min,
+ training_graph_x_max,
+ training_graph_y_min,
+ training_graph_y_max,
],
outputs=[
training_loss_graph,
@@ -874,8 +884,10 @@ def setup_gradio():
view_losses.click(
fn=update_training_dataplot,
inputs=[
- training_graph_x_lim,
- training_graph_y_lim,
+ training_graph_x_min,
+ training_graph_x_max,
+ training_graph_y_min,
+ training_graph_y_max,
training_configs,
],
outputs=[