added language selection in web UI, tweaked demo script
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@ -43,7 +43,7 @@ def main():
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parser.add_argument("--demo-dir", type=Path, default=None)
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parser.add_argument("--skip-existing", action="store_true")
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parser.add_argument("--sample-from-dataset", action="store_true")
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parser.add_argument("--load-from-dataloader", action="store_true")
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parser.add_argument("--skip-loading-dataloader", action="store_true")
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parser.add_argument("--dataset-samples", type=int, default=0)
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parser.add_argument("--audio-path-root", type=str, default=None)
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parser.add_argument("--preamble", type=str, default=None)
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@ -89,7 +89,7 @@ def main():
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if not args.preamble:
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args.preamble = "<br>".join([
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'Below are some samples from my VALL-E implementation: <a href="https://git.ecker.tech/mrq/vall-e/">https://git.ecker.tech/mrq/vall-e/</a>.',
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'I do not consider these to be state of the art, as the model does not follow close to the prompt as I would like for general speakers.',
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'Unlike the original VALL-E demo page, I\'m placing emphasis on the input prompt, as the model adheres to it stronger than others.',
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])
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# read html template
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@ -115,45 +115,46 @@ def main():
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"librispeech": args.demo_dir / "librispeech",
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}
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if (args.demo_dir / "dataset").exists():
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samples_dirs["dataset"] = args.demo_dir / "dataset"
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# pull from dataset samples
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if args.sample_from_dataset:
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cfg.dataset.cache = False
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samples_dirs["dataset"] = args.demo_dir / "dataset"
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if args.load_from_dataloader:
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_logger.info("Loading dataloader...")
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dataloader = create_train_dataloader()
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_logger.info("Loaded dataloader.")
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_logger.info("Loading dataloader...")
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dataloader = create_train_dataloader()
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_logger.info("Loaded dataloader.")
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num = args.dataset_samples if args.dataset_samples else cfg.evaluation.size
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num = args.dataset_samples if args.dataset_samples else cfg.evaluation.size
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length = len( dataloader.dataset )
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for i in trange( num, desc="Sampling dataset for samples" ):
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idx = random.randint( 0, length )
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batch = dataloader.dataset[idx]
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length = len( dataloader.dataset )
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for i in trange( num, desc="Sampling dataset for samples" ):
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idx = random.randint( 0, length )
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batch = dataloader.dataset[idx]
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dir = args.demo_dir / "dataset" / f'{i}'
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dir = args.demo_dir / "dataset" / f'{i}'
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(dir / "out").mkdir(parents=True, exist_ok=True)
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(dir / "out").mkdir(parents=True, exist_ok=True)
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metadata = batch["metadata"]
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metadata = batch["metadata"]
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text = metadata["text"]
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language = metadata["language"]
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prompt = dir / "prompt.wav"
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reference = dir / "reference.wav"
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out_path = dir / "out" / "ours.wav"
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text = metadata["text"]
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language = metadata["language"]
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prompt = dir / "prompt.wav"
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reference = dir / "reference.wav"
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out_path = dir / "out" / "ours.wav"
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if args.skip_existing and out_path.exists():
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continue
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if args.skip_existing and out_path.exists():
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continue
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open( dir / "prompt.txt", "w", encoding="utf-8" ).write( text )
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open( dir / "language.txt", "w", encoding="utf-8" ).write( language )
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open( dir / "prompt.txt", "w", encoding="utf-8" ).write( text )
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open( dir / "language.txt", "w", encoding="utf-8" ).write( language )
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decode_to_file( batch["proms"].to("cuda"), prompt, device="cuda" )
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decode_to_file( batch["resps"].to("cuda"), reference, device="cuda" )
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decode_to_file( batch["proms"].to("cuda"), prompt, device="cuda" )
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decode_to_file( batch["resps"].to("cuda"), reference, device="cuda" )
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for k, sample_dir in samples_dirs.items():
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if not sample_dir.exists():
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@ -182,23 +183,26 @@ def main():
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if args.skip_existing and out_path.exists():
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continue
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tts.inference(
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text=text,
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references=[prompt],
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language=language,
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out_path=out_path,
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input_prompt_length=args.input_prompt_length,
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max_ar_steps=args.max_ar_steps, max_nar_levels=args.max_nar_levels,
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ar_temp=args.ar_temp, nar_temp=args.nar_temp,
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min_ar_temp=args.min_ar_temp, min_nar_temp=args.min_nar_temp,
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top_p=args.top_p, top_k=args.top_k,
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repetition_penalty=args.repetition_penalty, repetition_penalty_decay=args.repetition_penalty_decay,
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length_penalty=args.length_penalty,
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beam_width=args.beam_width,
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mirostat_tau=args.mirostat_tau, mirostat_eta=args.mirostat_eta,
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seed=args.seed,
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tqdm=False,
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)
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try:
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tts.inference(
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text=text,
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references=[prompt],
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language=language,
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out_path=out_path,
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input_prompt_length=args.input_prompt_length,
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max_ar_steps=args.max_ar_steps, max_nar_levels=args.max_nar_levels,
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ar_temp=args.ar_temp, nar_temp=args.nar_temp,
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min_ar_temp=args.min_ar_temp, min_nar_temp=args.min_nar_temp,
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top_p=args.top_p, top_k=args.top_k,
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repetition_penalty=args.repetition_penalty, repetition_penalty_decay=args.repetition_penalty_decay,
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length_penalty=args.length_penalty,
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beam_width=args.beam_width,
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mirostat_tau=args.mirostat_tau, mirostat_eta=args.mirostat_eta,
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seed=args.seed,
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tqdm=False,
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)
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except Exception as e:
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print(f'Error while processing {out_path}: {e}')
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# collate entries into HTML
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samples = [
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@ -22,6 +22,7 @@ from .train import train
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from .utils import get_devices, setup_logging
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from .utils.io import json_read, json_stringify
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from .emb.qnt import decode_to_wave
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from .data import get_lang_symmap
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tts = None
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@ -100,6 +101,9 @@ def load_model( yaml, device, dtype, attention ):
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def get_speakers():
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return cfg.dataset.training
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def get_languages():
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return get_lang_symmap().keys()
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#@gradio_wrapper(inputs=layout["dataset"]["inputs"].keys())
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def load_sample( speaker ):
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metadata_path = cfg.metadata_dir / f'{speaker}.json'
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@ -158,7 +162,7 @@ def do_inference_tts( progress=gr.Progress(track_tqdm=True), *args, **kwargs ):
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parser.add_argument("--text", type=str, default=kwargs["text"])
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parser.add_argument("--task", type=str, default="tts")
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parser.add_argument("--references", type=str, default=kwargs["reference"])
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parser.add_argument("--language", type=str, default="en")
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parser.add_argument("--language", type=str, default=kwargs["language"])
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parser.add_argument("--input-prompt-length", type=float, default=kwargs["input-prompt-length"])
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parser.add_argument("--max-ar-steps", type=int, default=int(kwargs["max-seconds"]*cfg.dataset.frames_per_second))
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parser.add_argument("--max-nar-levels", type=int, default=0), # kwargs["max-nar-levels"])
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@ -231,7 +235,7 @@ def do_inference_stt( progress=gr.Progress(track_tqdm=True), *args, **kwargs ):
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parser = argparse.ArgumentParser(allow_abbrev=False)
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# I'm very sure I can procedurally generate this list
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parser.add_argument("--references", type=str, default=kwargs["reference"])
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parser.add_argument("--language", type=str, default="en")
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parser.add_argument("--language", type=str, default=kwargs["language"])
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parser.add_argument("--max-ar-steps", type=int, default=0)
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parser.add_argument("--ar-temp", type=float, default=kwargs["ar-temp"])
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parser.add_argument("--min-ar-temp", type=float, default=kwargs["min-ar-temp"])
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@ -381,6 +385,7 @@ with ui:
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layout["inference_tts"]["inputs"]["nar-temp"] = gr.Slider(value=0.0, minimum=0.0, maximum=1.5, step=0.05, label="Temperature (NAR)", info="Modifies the randomness from the samples in the NAR. (0 to greedy sample)")
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with gr.Row():
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layout["inference_tts"]["inputs"]["dynamic-sampling"] = gr.Checkbox(label="Dynamic Temperature", info="Dynamically adjusts the temperature based on the highest confident predicted token per sampling step.")
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layout["inference_tts"]["inputs"]["language"] = gr.Dropdown(choices=get_languages(), label="Language", value="en")
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with gr.Tab("Sampler Settings"):
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with gr.Row():
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layout["inference_tts"]["inputs"]["top-p"] = gr.Slider(value=1.0, minimum=0.0, maximum=1.0, step=0.05, label="Top P", info=r"Limits the samples that are outside the top P% of probabilities.")
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@ -419,7 +424,7 @@ with ui:
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layout["inference_stt"]["inputs"]["ar-temp"] = gr.Slider(value=0.0, minimum=0.0, maximum=1.5, step=0.05, label="Temperature (AR)", info="Modifies the randomness from the samples in the AR. (0 to greedy sample)")
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with gr.Row():
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layout["inference_stt"]["inputs"]["dynamic-sampling"] = gr.Checkbox(label="Dynamic Temperature", info="Dynamically adjusts the temperature based on the highest confident predicted token per sampling step.")
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layout["inference_stt"]["inputs"]["language"] = gr.Dropdown(choices=get_languages(), label="Language", value="en")
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with gr.Tab("Sampler Settings"):
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with gr.Row():
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layout["inference_stt"]["inputs"]["top-p"] = gr.Slider(value=1.0, minimum=0.0, maximum=1.0, step=0.05, label="Top P", info=r"Limits the samples that are outside the top P% of probabilities.")
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