forked from mrq/ai-voice-cloning
a bunch of shit i had uncommited over the past while pertaining to VALL-E
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@ -1 +1 @@
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Subproject commit 0bcdf81d0444218b4dedaefa5c546d42f36b8130
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Subproject commit f025470d60fd18993caaa651e6faa585bcc420f0
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50
src/utils.py
50
src/utils.py
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@ -75,6 +75,7 @@ try:
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VALLE_ENABLED = True
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except Exception as e:
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print(e)
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pass
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if VALLE_ENABLED:
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@ -156,10 +157,12 @@ def generate_valle(**kwargs):
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voice_cache = {}
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def fetch_voice( voice ):
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voice_dir = f'./voices/{voice}/'
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voice_dir = f'./training/{voice}/audio/'
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if not os.path.isdir(voice_dir):
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voice_dir = f'./voices/{voice}/'
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files = [ f'{voice_dir}/{d}' for d in os.listdir(voice_dir) if d[-4:] == ".wav" ]
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return files
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# return random.choice(files)
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# return files
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return random.choice(files)
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def get_settings( override=None ):
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settings = {
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@ -1089,13 +1092,13 @@ class TrainingState():
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'ar-quarter.lr', 'nar-quarter.lr',
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]
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keys['losses'] = [
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'ar.loss', 'nar.loss',
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'ar-half.loss', 'nar-half.loss',
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'ar-quarter.loss', 'nar-quarter.loss',
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'ar.loss', 'nar.loss', 'ar+nar.loss',
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'ar-half.loss', 'nar-half.loss', 'ar-half+nar-half.loss',
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'ar-quarter.loss', 'nar-quarter.loss', 'ar-quarter+nar-quarter.loss',
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'ar.loss.nll', 'nar.loss.nll',
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'ar-half.loss.nll', 'nar-half.loss.nll',
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'ar-quarter.loss.nll', 'nar-quarter.loss.nll',
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# 'ar.loss.nll', 'nar.loss.nll',
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# 'ar-half.loss.nll', 'nar-half.loss.nll',
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# 'ar-quarter.loss.nll', 'nar-quarter.loss.nll',
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]
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keys['accuracies'] = [
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@ -1123,7 +1126,7 @@ class TrainingState():
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prefix = ""
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if data["mode"] == "validation":
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if "mode" in self.info and self.info["mode"] == "validation":
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prefix = f'{self.info["name"] if "name" in self.info else "val"}_'
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self.statistics['loss'].append({'epoch': epoch, 'it': self.it, 'value': self.info[k], 'type': f'{prefix}{k}' })
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@ -1231,6 +1234,7 @@ class TrainingState():
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unq = {}
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averager = None
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prev_state = 0
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for log in logs:
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with open(log, 'r', encoding="utf-8") as f:
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@ -1250,6 +1254,7 @@ class TrainingState():
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name = "train"
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mode = "training"
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prev_state = 0
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elif line.find('Validation Metrics:') >= 0:
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data = json.loads(line.split("Validation Metrics:")[-1])
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if "it" not in data:
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@ -1257,8 +1262,15 @@ class TrainingState():
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if "epoch" not in data:
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data['epoch'] = epoch
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name = data['name'] if 'name' in data else "val"
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# name = data['name'] if 'name' in data else "val"
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mode = "validation"
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if prev_state == 0:
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name = "subtrain"
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else:
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name = "val"
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prev_state += 1
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else:
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continue
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@ -1272,6 +1284,7 @@ class TrainingState():
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if not averager or averager['key'] != f'{it}_{name}' or averager['mode'] != mode:
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averager = {
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'key': f'{it}_{name}',
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'name': name,
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'mode': mode,
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"metrics": {}
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}
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@ -1292,11 +1305,13 @@ class TrainingState():
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if update and it <= self.last_info_check_at:
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continue
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blacklist = [ "batch", "eval" ]
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for it in unq:
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if args.tts_backend == "vall-e":
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stats = unq[it]
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data = {k: sum(v) / len(v) for k, v in stats['metrics'].items()}
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data['mode'] = stats
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data = {k: sum(v) / len(v) for k, v in stats['metrics'].items() if k not in blacklist }
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data['name'] = stats['name']
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data['mode'] = stats['mode']
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data['steps'] = len(stats['metrics']['it'])
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else:
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data = unq[it]
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@ -1633,6 +1648,7 @@ def whisper_transcribe( file, language=None ):
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device = "cuda" if get_device_name() == "cuda" else "cpu"
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if whisper_vad:
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# omits a considerable amount of the end
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"""
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if args.whisper_batchsize > 1:
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result = whisperx.transcribe_with_vad_parallel(whisper_model, file, whisper_vad, batch_size=args.whisper_batchsize, language=language, task="transcribe")
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@ -1778,7 +1794,9 @@ def slice_dataset( voice, trim_silence=True, start_offset=0, end_offset=0, resul
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messages = []
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if not os.path.exists(infile):
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raise Exception(f"Missing dataset: {infile}")
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message = f"Missing dataset: {infile}"
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print(message)
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return message
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if results is None:
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results = json.load(open(infile, 'r', encoding="utf-8"))
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@ -1903,7 +1921,9 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
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indir = f'./training/{voice}/'
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infile = f'{indir}/whisper.json'
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if not os.path.exists(infile):
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raise Exception(f"Missing dataset: {infile}")
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message = f"Missing dataset: {infile}"
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print(message)
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return message
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results = json.load(open(infile, 'r', encoding="utf-8"))
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63
src/webui.py
63
src/webui.py
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@ -196,6 +196,50 @@ def read_generate_settings_proxy(file, saveAs='.temp'):
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def slice_dataset_proxy( voice, trim_silence, start_offset, end_offset, progress=gr.Progress(track_tqdm=True) ):
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return slice_dataset( voice, trim_silence=trim_silence, start_offset=start_offset, end_offset=end_offset, results=None, progress=progress )
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def diarize_dataset( voice, progress=gr.Progress(track_tqdm=False) ):
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from pyannote.audio import Pipeline
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pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization", use_auth_token=args.hf_token)
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messages = []
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files = sorted( get_voices(load_latents=False)[voice] )
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for file in enumerate_progress(files, desc="Iterating through voice files", progress=progress):
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diarization = pipeline(file)
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for turn, _, speaker in diarization.itertracks(yield_label=True):
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message = f"start={turn.start:.1f}s stop={turn.end:.1f}s speaker_{speaker}"
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print(message)
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messages.append(message)
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return "\n".join(messages)
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def prepare_all_datasets( language, validation_text_length, validation_audio_length, skip_existings, slice_audio, trim_silence, slice_start_offset, slice_end_offset, progress=gr.Progress(track_tqdm=False) ):
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kwargs = locals()
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messages = []
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voices = get_voice_list()
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"""
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for voice in voices:
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message = prepare_dataset_proxy(voice, **kwargs)
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messages.append(message)
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"""
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for voice in voices:
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print("Processing:", voice)
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message = transcribe_dataset( voice=voice, language=language, skip_existings=skip_existings, progress=progress )
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messages.append(message)
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if slice_audio:
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for voice in voices:
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print("Processing:", voice)
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message = slice_dataset( voice, trim_silence=trim_silence, start_offset=slice_start_offset, end_offset=slice_end_offset, results=None, progress=progress )
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messages.append(message)
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for voice in voices:
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print("Processing:", voice)
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message = prepare_dataset( voice, use_segments=slice_audio, text_length=validation_text_length, audio_length=validation_audio_length, progress=progress )
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messages.append(message)
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return "\n".join(messages)
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def prepare_dataset_proxy( voice, language, validation_text_length, validation_audio_length, skip_existings, slice_audio, trim_silence, slice_start_offset, slice_end_offset, progress=gr.Progress(track_tqdm=False) ):
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messages = []
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@ -468,6 +512,8 @@ def setup_gradio():
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DATASET_SETTINGS['slice_end_offset'] = gr.Number(label="Slice End Offset", value=0)
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transcribe_button = gr.Button(value="Transcribe and Process")
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transcribe_all_button = gr.Button(value="Transcribe All")
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diarize_button = gr.Button(value="Diarize")
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with gr.Row():
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slice_dataset_button = gr.Button(value="(Re)Slice Audio")
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@ -579,7 +625,7 @@ def setup_gradio():
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tooltip=['epoch', 'it', 'value', 'type'],
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width=500,
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height=350,
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visible=args.tts_backend=="vall-e"
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visible=False, # args.tts_backend=="vall-e"
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)
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view_losses = gr.Button(value="View Losses")
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@ -611,10 +657,7 @@ def setup_gradio():
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# EXEC_SETTINGS['tts_backend'] = gr.Dropdown(TTSES, label="TTS Backend", value=args.tts_backend if args.tts_backend else TTSES[0])
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with gr.Column(visible=args.tts_backend=="vall-e"):
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default_valle_model_choice = ""
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if len(valle_models):
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default_valle_model_choice = valle_models[0]
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EXEC_SETTINGS['valle_model'] = gr.Dropdown(choices=valle_models, label="VALL-E Model Config", value=args.valle_model if args.valle_model else default_valle_model_choice)
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EXEC_SETTINGS['valle_model'] = gr.Dropdown(choices=valle_models, label="VALL-E Model Config", value=args.valle_model if args.valle_model else valle_models[0])
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with gr.Column(visible=args.tts_backend=="tortoise"):
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EXEC_SETTINGS['autoregressive_model'] = gr.Dropdown(choices=["auto"] + autoregressive_models, label="Autoregressive Model", value=args.autoregressive_model if args.autoregressive_model else "auto")
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@ -859,6 +902,16 @@ def setup_gradio():
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inputs=dataset_settings,
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outputs=prepare_dataset_output #console_output
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)
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transcribe_all_button.click(
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prepare_all_datasets,
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inputs=dataset_settings[1:],
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outputs=prepare_dataset_output #console_output
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)
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diarize_button.click(
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diarize_dataset,
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inputs=dataset_settings[0],
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outputs=prepare_dataset_output #console_output
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)
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prepare_dataset_button.click(
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prepare_dataset,
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inputs=[
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