forked from mrq/ai-voice-cloning
cleaned up brain worms with wrapping around gradio progress by instead just using tqdm directly (slight regressions with some messages not getting pushed)
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parent
09d849a78f
commit
5003bc89d3
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@ -1 +1 @@
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Subproject commit 086aad5b49e7ef39d043c6b0e12ac33c20773ab8
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Subproject commit c90ee7c5296992ad96c8790b5b7cc3737062e1e6
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70
src/utils.py
70
src/utils.py
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@ -238,7 +238,7 @@ def generate_bark(**kwargs):
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if tts_loading:
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raise Exception("TTS is still initializing...")
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if progress is not None:
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progress(0, "Initializing TTS...")
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notify_progress("Initializing TTS...", progress=progress)
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load_tts()
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if hasattr(tts, "loading") and tts.loading:
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raise Exception("TTS is still initializing...")
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@ -339,8 +339,8 @@ def generate_bark(**kwargs):
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INFERENCING = True
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for line, cut_text in enumerate(texts):
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progress.msg_prefix = f'[{str(line+1)}/{str(len(texts))}]'
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print(f"{progress.msg_prefix} Generating line: {cut_text}")
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tqdm_prefix = f'[{str(line+1)}/{str(len(texts))}]'
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print(f"{tqdm_prefix} Generating line: {cut_text}")
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start_time = time.time()
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# do setting editing
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@ -422,12 +422,12 @@ def generate_bark(**kwargs):
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if args.voice_fixer:
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if not voicefixer:
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progress(0, "Loading voicefix...")
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notify_progress("Loading voicefix...", progress=progress)
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load_voicefixer()
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try:
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fixed_cache = {}
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for name in progress.tqdm(audio_cache, desc="Running voicefix..."):
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for name in 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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@ -467,7 +467,7 @@ def generate_bark(**kwargs):
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f.write(json.dumps(audio_cache[name]['settings'], indent='\t') )
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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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for name in 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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@ -521,7 +521,7 @@ def generate_valle(**kwargs):
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if tts_loading:
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raise Exception("TTS is still initializing...")
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if progress is not None:
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progress(0, "Initializing TTS...")
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notify_progress("Initializing TTS...", progress=progress)
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load_tts()
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if hasattr(tts, "loading") and tts.loading:
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raise Exception("TTS is still initializing...")
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@ -630,8 +630,8 @@ def generate_valle(**kwargs):
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INFERENCING = True
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for line, cut_text in enumerate(texts):
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progress.msg_prefix = f'[{str(line+1)}/{str(len(texts))}]'
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print(f"{progress.msg_prefix} Generating line: {cut_text}")
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tqdm_prefix = f'[{str(line+1)}/{str(len(texts))}]'
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print(f"{tqdm_prefix} Generating line: {cut_text}")
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start_time = time.time()
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# do setting editing
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@ -715,12 +715,12 @@ def generate_valle(**kwargs):
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if args.voice_fixer:
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if not voicefixer:
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progress(0, "Loading voicefix...")
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notify_progress("Loading voicefix...", progress=progress)
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load_voicefixer()
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try:
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fixed_cache = {}
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for name in progress.tqdm(audio_cache, desc="Running voicefix..."):
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for name in 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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@ -760,7 +760,7 @@ def generate_valle(**kwargs):
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f.write(json.dumps(audio_cache[name]['settings'], indent='\t') )
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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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for name in 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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@ -839,7 +839,7 @@ def generate_tortoise(**kwargs):
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voice_samples, conditioning_latents = None, tts.get_random_conditioning_latents()
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else:
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if progress is not None:
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progress(0, desc=f"Loading voice: {voice}")
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notify_progress(f"Loading voice: {voice}", progress=progress)
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voice_samples, conditioning_latents = load_voice(voice, model_hash=tts.autoregressive_model_hash)
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@ -1032,8 +1032,8 @@ def generate_tortoise(**kwargs):
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elif parameters['emotion'] != "None" and parameters['emotion']:
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cut_text = f"[I am really {parameters['emotion'].lower()},] {cut_text}"
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progress.msg_prefix = f'[{str(line+1)}/{str(len(texts))}]'
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print(f"{progress.msg_prefix} Generating line: {cut_text}")
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tqdm_prefix = f'[{str(line+1)}/{str(len(texts))}]'
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print(f"{tqdm_prefix} Generating line: {cut_text}")
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start_time = time.time()
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# do setting editing
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@ -1115,12 +1115,12 @@ def generate_tortoise(**kwargs):
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if args.voice_fixer:
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if not voicefixer:
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progress(0, "Loading voicefix...")
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notify_progress("Loading voicefix...", progress=progress)
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load_voicefixer()
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try:
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fixed_cache = {}
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for name in progress.tqdm(audio_cache, desc="Running voicefix..."):
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for name in 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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@ -1160,7 +1160,7 @@ def generate_tortoise(**kwargs):
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f.write(json.dumps(audio_cache[name]['settings'], indent='\t') )
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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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for name in 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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@ -1309,7 +1309,7 @@ def compute_latents(voice=None, voice_samples=None, voice_latents_chunks=0, prog
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if voice_samples is None:
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return
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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)
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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)
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if len(conditioning_latents) == 4:
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conditioning_latents = (conditioning_latents[0], conditioning_latents[1], conditioning_latents[2], None)
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@ -2117,7 +2117,7 @@ def transcribe_dataset( voice, language=None, skip_existings=False, progress=Non
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if os.path.exists(infile):
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results = json.load(open(infile, 'r', encoding="utf-8"))
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for file in enumerate_progress(files, desc="Iterating through voice files", progress=progress):
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for file in tqdm(files, desc="Iterating through voice files"):
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basename = os.path.basename(file)
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if basename in results and skip_existings:
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@ -2246,7 +2246,7 @@ def phonemize_txt_file( path ):
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reparsed = []
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with open(path.replace(".txt", ".phn.txt"), 'a', encoding='utf-8') as f:
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for line in enumerate_progress(lines, desc='Phonemizing...'):
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for line in tqdm(lines, desc='Phonemizing...'):
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split = line.split("|")
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audio = split[0]
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text = split[2]
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@ -2357,7 +2357,7 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
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text_length = 0
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audio_length = 0
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for filename in enumerate_progress(results, desc="Parsing results", progress=progress):
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for filename in tqdm(results, desc="Parsing results"):
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use_segment = use_segments
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result = results[filename]
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@ -2438,7 +2438,7 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
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'phonemize': [[], []],
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}
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for file in enumerate_progress(segments, desc="Parsing segments", progress=progress):
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for file in tqdm(segments, desc="Parsing segments"):
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result = segments[file]
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path = f'{indir}/audio/{file}'
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@ -2511,7 +2511,7 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
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print("Phonemized:", file, normalized, text)
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"""
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for i in enumerate_progress(range(len(jobs['quantize'][0])), desc="Quantizing", progress=progress):
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for i in tqdm(range(len(jobs['quantize'][0])), desc="Quantizing"):
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qnt_file = jobs['quantize'][0][i]
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waveform, sample_rate = jobs['quantize'][1][i]
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@ -2519,7 +2519,7 @@ def prepare_dataset( voice, use_segments=False, text_length=0, audio_length=0, p
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torch.save(quantized, qnt_file)
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print("Quantized:", qnt_file)
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for i in enumerate_progress(range(len(jobs['phonemize'][0])), desc="Phonemizing", progress=progress):
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for i in tqdm(range(len(jobs['phonemize'][0])), desc="Phonemizing"):
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phn_file = jobs['phonemize'][0][i]
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normalized = jobs['phonemize'][1][i]
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@ -2807,7 +2807,7 @@ def import_voices(files, saveAs=None, progress=None):
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if not isinstance(files, list):
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files = [files]
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for file in enumerate_progress(files, desc="Importing voice files", progress=progress):
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for file in tqdm(files, desc="Importing voice files"):
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j, latents = read_generate_settings(file, read_latents=True)
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if j is not None and saveAs is None:
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@ -3025,22 +3025,14 @@ def check_for_updates( dir = None ):
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return False
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def enumerate_progress(iterable, desc=None, progress=None, verbose=None):
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if verbose and desc is not None:
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print(desc)
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if progress is None:
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return tqdm(iterable, disable=False) #not verbose)
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return progress.tqdm(iterable, desc=f'{progress.msg_prefix} {desc}' if hasattr(progress, 'msg_prefix') else desc)
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def notify_progress(message, progress=None, verbose=True):
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if verbose:
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print(message)
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if progress is None:
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return
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progress(0, desc=message)
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tqdm.write( desc=message)
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else:
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progress(0, desc=message)
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def get_args():
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global args
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@ -3650,7 +3642,7 @@ def load_whisper_model(language=None, model_name=None, progress=None):
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model_name = f'{model_name}.{language}'
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print(f"Loading specialized model for language: {language}")
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notify_progress(f"Loading Whisper model: {model_name}", progress)
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notify_progress(f"Loading Whisper model: {model_name}", progress=progress)
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if args.whisper_backend == "openai/whisper":
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import whisper
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@ -3733,7 +3725,7 @@ def merge_models( primary_model_name, secondary_model_name, alpha, progress=gr.P
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theta_0 = read_model(primary_model_name)
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theta_1 = read_model(secondary_model_name)
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for key in enumerate_progress(theta_0.keys(), desc="Merging...", progress=progress):
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for key in tqdm(theta_0.keys(), desc="Merging..."):
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if key in key_blacklist:
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print("Skipping ignored key:", key)
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continue
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@ -200,7 +200,7 @@ 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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def diarize_dataset( voice, progress=gr.Progress(track_tqdm=True) ):
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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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@ -215,7 +215,7 @@ def diarize_dataset( voice, progress=gr.Progress(track_tqdm=False) ):
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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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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=True) ):
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kwargs = locals()
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messages = []
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@ -239,7 +239,7 @@ def prepare_all_datasets( language, validation_text_length, validation_audio_len
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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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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=True) ):
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messages = []
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message = transcribe_dataset( voice=voice, language=language, skip_existings=skip_existings, progress=progress )
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