forked from mrq/ai-voice-cloning
added option to not slice audio when transcribing, added option to prepare validation dataset on audio duration, added a warning if youre using whisperx and you're slicing audio
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dcdcf8516c
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2424c455cb
74
src/utils.py
74
src/utils.py
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@ -667,7 +667,7 @@ class TrainingState():
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self.steps = int(self.info['steps'])
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if 'iteration_rate' in self.info:
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it_rate = self.info['iteration_rate']
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it_rate = self.info['iteration_rate'] / self.batch_size # why
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self.it_rate = f'{"{:.3f}".format(1/it_rate)}it/s' if 0 < it_rate and it_rate < 1 else f'{"{:.3f}".format(it_rate)}s/it'
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self.it_rates += it_rate
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@ -676,6 +676,7 @@ class TrainingState():
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eta = str(timedelta(seconds=int(self.eta)))
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self.eta_hhmmss = eta
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except Exception as e:
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self.eta_hhmmss = "?"
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pass
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self.metrics['step'] = [f"{self.epoch}/{self.epochs}"]
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@ -1064,13 +1065,16 @@ def whisper_transcribe( file, language=None ):
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return result
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def prepare_dataset( files, outdir, language=None, skip_existings=False, progress=None ):
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def prepare_dataset( files, outdir, language=None, skip_existings=False, slice_audio=False, progress=None ):
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unload_tts()
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global whisper_model
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if whisper_model is None:
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load_whisper_model(language=language)
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if args.whisper_backend == "m-bain/whisperx" and slice_audio:
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print("! CAUTION ! Slicing audio with whisperx is terrible. Please consider using a different whisper backend if you want to slice audio.")
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os.makedirs(f'{outdir}/audio/', exist_ok=True)
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results = {}
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@ -1092,6 +1096,14 @@ def prepare_dataset( files, outdir, language=None, skip_existings=False, progres
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if match[0] not in previous_list:
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previous_list.append(f'{match[0].split("/")[-1]}.wav')
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def validate_waveform( waveform, sample_rate, name ):
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if not torch.any(waveform < 0):
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return False
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if waveform.shape[-1] < (.6 * sampling_rate):
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return False
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return True
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for file in enumerate_progress(files, desc="Iterating through voice files", progress=progress):
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basename = os.path.basename(file)
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@ -1106,29 +1118,36 @@ def prepare_dataset( files, outdir, language=None, skip_existings=False, progres
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waveform, sampling_rate = torchaudio.load(file)
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num_channels, num_frames = waveform.shape
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idx = 0
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for segment in result['segments']: # enumerate_progress(result['segments'], desc="Segmenting voice file", progress=progress):
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start = int(segment['start'] * sampling_rate)
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end = int(segment['end'] * sampling_rate)
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sliced_waveform = waveform[:, start:end]
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sliced_name = basename.replace(".wav", f"_{pad(idx, 4)}.wav")
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if not torch.any(sliced_waveform < 0):
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print(f"Sound file is silent: {sliced_name}, skipping...")
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if not slice_audio:
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if not validate_waveform( waveform, sampling_rate, name ):
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print(f"Segment invalid: {name}, skipping...")
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continue
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if sliced_waveform.shape[-1] < (.6 * sampling_rate):
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print(f"Sound file is too short: {sliced_name}, skipping...")
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continue
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torchaudio.save(f"{outdir}/audio/{sliced_name}", sliced_waveform, sampling_rate)
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idx = idx + 1
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line = f"audio/{sliced_name}|{segment['text'].strip()}"
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torchaudio.save(f"{outdir}/audio/{basename}", waveform, sampling_rate)
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line = f"audio/{basename}|{result['text'].strip()}"
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transcription.append(line)
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with open(f'{outdir}/train.txt', 'a', encoding="utf-8") as f:
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f.write(f'\n{line}')
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else:
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idx = 0
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for segment in result['segments']: # enumerate_progress(result['segments'], desc="Segmenting voice file", progress=progress):
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start = int(segment['start'] * sampling_rate)
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end = int(segment['end'] * sampling_rate)
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sliced_waveform = waveform[:, start:end]
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sliced_name = basename.replace(".wav", f"_{pad(idx, 4)}.wav")
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if not validate_waveform( sliced_waveform, sampling_rate, sliced_name ):
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print(f"Trimmed segment invalid: {sliced_name}, skipping...")
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continue
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torchaudio.save(f"{outdir}/audio/{sliced_name}", sliced_waveform, sampling_rate)
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idx = idx + 1
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line = f"audio/{sliced_name}|{segment['text'].strip()}"
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transcription.append(line)
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with open(f'{outdir}/train.txt', 'a', encoding="utf-8") as f:
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f.write(f'\n{line}')
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do_gc()
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@ -1144,7 +1163,7 @@ def prepare_dataset( files, outdir, language=None, skip_existings=False, progres
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return f"Processed dataset to: {outdir}\n{joined}"
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def prepare_validation_dataset( voice, text_length ):
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def prepare_validation_dataset( voice, text_length, audio_length ):
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indir = f'./training/{voice}/'
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infile = f'{indir}/dataset.txt'
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if not os.path.exists(infile):
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@ -1166,8 +1185,14 @@ def prepare_validation_dataset( voice, text_length ):
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split = line.split("|")
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filename = split[0]
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text = split[1]
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culled = len(text) < text_length
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if len(text) < text_length:
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if not culled and audio_length > 0:
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metadata = torchaudio.info(f'{indir}/{filename}')
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duration = metadata.num_channels * metadata.num_frames / metadata.sample_rate
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culled = duration < audio_length
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if culled:
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validation.append(line.strip())
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else:
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training.append(line.strip())
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@ -1178,7 +1203,7 @@ def prepare_validation_dataset( voice, text_length ):
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with open(f'{indir}/validation.txt', 'w', encoding="utf-8") as f:
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f.write("\n".join(validation))
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msg = f"Culled {len(validation)} lines"
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msg = f"Culled {len(validation)}/{len(lines)} lines."
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print(msg)
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return msg
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@ -1896,6 +1921,9 @@ def load_tts( restart=False, autoregressive_model=None ):
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print(f"Loading TorToiSe... (AR: {autoregressive_model}, vocoder: {args.vocoder_model})")
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if get_device_name() == "cpu":
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print("!!!! WARNING !!!! No GPU available in PyTorch. You may need to reinstall PyTorch.")
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tts_loading = True
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try:
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tts = TextToSpeech(minor_optimizations=not args.low_vram, autoregressive_model_path=autoregressive_model, vocoder_model=args.vocoder_model)
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32
src/webui.py
Normal file → Executable file
32
src/webui.py
Normal file → Executable file
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@ -152,9 +152,7 @@ def import_generate_settings_proxy( file=None ):
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res = []
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for k in GENERATE_SETTINGS_ARGS:
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res.append(settings[k] if k in settings else None)
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print(GENERATE_SETTINGS_ARGS)
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print(settings)
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print(res)
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return tuple(res)
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def compute_latents_proxy(voice, voice_latents_chunks, progress=gr.Progress(track_tqdm=True)):
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@ -184,12 +182,12 @@ def read_generate_settings_proxy(file, saveAs='.temp'):
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gr.update(visible=j is not None),
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)
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def prepare_dataset_proxy( voice, language, validation_size, skip_existings, progress=gr.Progress(track_tqdm=True) ):
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def prepare_dataset_proxy( voice, language, validation_text_length, validation_audio_length, skip_existings, slice_audio, progress=gr.Progress(track_tqdm=True) ):
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messages = []
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message = prepare_dataset( get_voices(load_latents=False)[voice], outdir=f"./training/{voice}/", language=language, skip_existings=skip_existings, progress=progress )
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message = prepare_dataset( get_voices(load_latents=False)[voice], outdir=f"./training/{voice}/", language=language, skip_existings=skip_existings, slice_audio=slice_audio, progress=progress )
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messages.append(message)
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if validation_size > 0:
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message = prepare_validation_dataset( voice, text_length=validation_size )
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if validation_text_length > 0 or validation_audio_length > 0:
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message = prepare_validation_dataset( voice, text_length=validation_text_length, audio_length=validation_audio_length )
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messages.append(message)
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return "\n".join(messages)
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@ -246,8 +244,7 @@ def import_training_settings_proxy( voice ):
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output[k] = settings[k]
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output = list(output.values())
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print(list(TRAINING_SETTINGS.keys()))
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print(output)
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messages.append(f"Imported training settings: {injson}")
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return output[:-1] + ["\n".join(messages)]
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@ -413,13 +410,20 @@ def setup_gradio():
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DATASET_SETTINGS['voice'] = gr.Dropdown( choices=voice_list, label="Dataset Source", type="value", value=voice_list[0] if len(voice_list) > 0 else "" )
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with gr.Row():
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DATASET_SETTINGS['language'] = gr.Textbox(label="Language", value="en")
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DATASET_SETTINGS['validation_size'] = gr.Number(label="Validation Text Length Cull Size", value=12, precision=0)
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DATASET_SETTINGS['skip'] = gr.Checkbox(label="Skip Already Transcribed", value=False)
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DATASET_SETTINGS['validation_text_length'] = gr.Number(label="Validation Text Length Threshold", value=12, precision=0)
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DATASET_SETTINGS['validation_audio_length'] = gr.Number(label="Validation Audio Length Threshold", value=1 )
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with gr.Row():
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DATASET_SETTINGS['skip'] = gr.Checkbox(label="Skip Already Transcribed", value=False)
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DATASET_SETTINGS['slice'] = gr.Checkbox(label="Slice Segments", value=False)
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with gr.Row():
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transcribe_button = gr.Button(value="Transcribe")
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prepare_validation_button = gr.Button(value="Prepare Validation")
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with gr.Row():
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EXEC_SETTINGS['whisper_backend'] = gr.Dropdown(WHISPER_BACKENDS, label="Whisper Backends", value=args.whisper_backend)
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EXEC_SETTINGS['whisper_model'] = gr.Dropdown(WHISPER_MODELS, label="Whisper Model", value=args.whisper_model)
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dataset_settings = list(DATASET_SETTINGS.values())
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with gr.Column():
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prepare_dataset_output = gr.TextArea(label="Console Output", interactive=False, max_lines=8)
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@ -533,8 +537,7 @@ def setup_gradio():
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EXEC_SETTINGS['autoregressive_model'] = gr.Dropdown(choices=autoregressive_models, label="Autoregressive Model", value=args.autoregressive_model if args.autoregressive_model else autoregressive_models[0])
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EXEC_SETTINGS['vocoder_model'] = gr.Dropdown(VOCODERS, label="Vocoder", value=args.vocoder_model if args.vocoder_model else VOCODERS[-1])
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EXEC_SETTINGS['whisper_backend'] = gr.Dropdown(WHISPER_BACKENDS, label="Whisper Backends", value=args.whisper_backend)
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EXEC_SETTINGS['whisper_model'] = gr.Dropdown(WHISPER_MODELS, label="Whisper Model", value=args.whisper_model)
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EXEC_SETTINGS['training_default_halfp'] = TRAINING_SETTINGS['half_p']
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EXEC_SETTINGS['training_default_bnb'] = TRAINING_SETTINGS['bitsandbytes']
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@ -739,7 +742,8 @@ def setup_gradio():
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prepare_validation_dataset,
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inputs=[
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dataset_settings[0],
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DATASET_SETTINGS['validation_size'],
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DATASET_SETTINGS['validation_text_length'],
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DATASET_SETTINGS['validation_audio_length'],
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],
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outputs=prepare_dataset_output #console_output
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)
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