working webui
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@ -64,34 +64,43 @@ def init_tts(restart=False):
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@gradio_wrapper(inputs=layout["inference"]["inputs"].keys())
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def do_inference( progress=gr.Progress(track_tqdm=True), *args, **kwargs ):
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"""
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if kwargs.pop("dynamic-sampling", False):
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kwargs['min-ar-temp'] = 0.85 if kwargs['ar-temp'] > 0.85 else 0.0
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kwargs['min-nar-temp'] = 0.85 if kwargs['nar-temp'] > 0.85 else 0.0 # should probably disable it for the NAR
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else:
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kwargs['min-ar-temp'] = -1
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kwargs['min-nar-temp'] = -1
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"""
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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("--text", type=str, default=kwargs["text"])
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parser.add_argument("--references", type=str, default=kwargs["reference"])
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parser.add_argument("--max-ar-steps", type=int, default=int(kwargs["max-ar-steps"]))
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parser.add_argument("--max-diffusion-steps", type=int, default=int(kwargs["max-diffusion-steps"]))
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"""
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parser.add_argument("--language", type=str, default="en")
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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-ar-context", type=int, default=int(kwargs["max-seconds-context"]*cfg.dataset.frames_per_second))
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parser.add_argument("--max-nar-levels", type=int, default=kwargs["max-nar-levels"])
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"""
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parser.add_argument("--ar-temp", type=float, default=kwargs["ar-temp"])
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parser.add_argument("--nar-temp", type=float, default=kwargs["nar-temp"])
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parser.add_argument("--diffusion-temp", type=float, default=kwargs["diffusion-temp"])
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"""
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parser.add_argument("--min-ar-temp", type=float, default=kwargs["min-ar-temp"])
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parser.add_argument("--min-nar-temp", type=float, default=kwargs["min-nar-temp"])
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parser.add_argument("--min-diffusion-temp", type=float, default=kwargs["min-diffusion-temp"])
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"""
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parser.add_argument("--top-p", type=float, default=kwargs["top-p"])
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parser.add_argument("--top-k", type=int, default=kwargs["top-k"])
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parser.add_argument("--repetition-penalty", type=float, default=kwargs["repetition-penalty"])
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parser.add_argument("--repetition-penalty-decay", type=float, default=kwargs["repetition-penalty-decay"])
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parser.add_argument("--length-penalty", type=float, default=kwargs["length-penalty"])
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parser.add_argument("--beam-width", type=int, default=kwargs["beam-width"])
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"""
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parser.add_argument("--repetition-penalty-decay", type=float, default=kwargs["repetition-penalty-decay"])
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parser.add_argument("--mirostat-tau", type=float, default=kwargs["mirostat-tau"])
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parser.add_argument("--mirostat-eta", type=float, default=kwargs["mirostat-eta"])
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"""
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args, unknown = parser.parse_known_args()
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tmp = tempfile.NamedTemporaryFile(suffix='.wav')
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@ -103,23 +112,19 @@ def do_inference( progress=gr.Progress(track_tqdm=True), *args, **kwargs ):
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with timer() as t:
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wav, sr = tts.inference(
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text=args.text,
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language=args.language,
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references=[args.references.split(";")],
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#language=args.language,
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references=[args.references],
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out_path=tmp.name,
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max_ar_steps=args.max_ar_steps,
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max_nar_levels=args.max_nar_levels,
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input_prompt_length=args.input_prompt_length,
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max_diffusion_steps=args.max_diffusion_steps,
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ar_temp=args.ar_temp,
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nar_temp=args.nar_temp,
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min_ar_temp=args.min_ar_temp,
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min_nar_temp=args.min_nar_temp,
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diffusion_temp=args.diffusion_temp,
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top_p=args.top_p,
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top_k=args.top_k,
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repetition_penalty=args.repetition_penalty,
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repetition_penalty_decay=args.repetition_penalty_decay,
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#repetition_penalty_decay=args.repetition_penalty_decay,
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length_penalty=args.length_penalty,
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mirostat_tau=args.mirostat_tau,
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mirostat_eta=args.mirostat_eta,
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beam_width=args.beam_width,
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)
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wav = wav.squeeze(0).cpu().numpy()
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@ -207,16 +212,23 @@ with ui:
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layout["inference"]["outputs"]["output"] = gr.Audio(label="Output")
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layout["inference"]["buttons"]["inference"] = gr.Button(value="Inference")
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with gr.Column(scale=7):
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"""
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with gr.Row():
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layout["inference"]["inputs"]["max-seconds"] = gr.Slider(value=12, minimum=1, maximum=32, step=0.1, label="Maximum Seconds", info="Limits how many steps to perform in the AR pass.")
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layout["inference"]["inputs"]["max-nar-levels"] = gr.Slider(value=7, minimum=0, maximum=7, step=1, label="Max NAR Levels", info="Limits how many steps to perform in the NAR pass.")
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layout["inference"]["inputs"]["input-prompt-length"] = gr.Slider(value=3.0, minimum=0.0, maximum=12.0, step=0.05, label="Input Prompt Trim Length", info="Trims the input prompt down to X seconds. Set 0 to disable.")
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layout["inference"]["inputs"]["max-seconds-context"] = gr.Slider(value=0.0, minimum=0.0, maximum=12.0, step=0.05, label="Context Length", info="Amount of generated audio to keep in the context during inference, in seconds. Set 0 to disable.")
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"""
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with gr.Row():
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layout["inference"]["inputs"]["max-ar-steps"] = gr.Slider(value=500, minimum=16, maximum=1200, step=1, label="Maximum AR Steps", info="Limits how many steps to perform in the AR pass.")
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layout["inference"]["inputs"]["max-diffusion-steps"] = gr.Slider(value=80, minimum=16, maximum=500, step=1, label="Maximum Diffusion Steps", info="Limits how many steps to perform in the Diffusion pass.")
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with gr.Row():
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layout["inference"]["inputs"]["ar-temp"] = gr.Slider(value=0.95, 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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layout["inference"]["inputs"]["nar-temp"] = gr.Slider(value=0.01, 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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layout["inference"]["inputs"]["diffusion-temp"] = gr.Slider(value=0.01, 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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"""
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with gr.Row():
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layout["inference"]["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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"""
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with gr.Row():
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layout["inference"]["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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@ -226,10 +238,11 @@ with ui:
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layout["inference"]["inputs"]["repetition-penalty"] = gr.Slider(value=1.0, minimum=-2.0, maximum=2.0, step=0.05, label="Repetition Penalty", info="Incurs a penalty to tokens based on how often they appear in a sequence.")
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layout["inference"]["inputs"]["repetition-penalty-decay"] = gr.Slider(value=0.0, minimum=-2.0, maximum=2.0, step=0.05, label="Repetition Penalty Length Decay", info="Modifies the reptition penalty based on how far back in time the token appeared in the sequence.")
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layout["inference"]["inputs"]["length-penalty"] = gr.Slider(value=0.0, minimum=-2.0, maximum=2.0, step=0.05, label="Length Penalty", info="(AR only) Modifies the probability of a stop token based on the current length of the sequence.")
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"""
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with gr.Row():
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layout["inference"]["inputs"]["mirostat-tau"] = gr.Slider(value=0.0, minimum=0.0, maximum=8.0, step=0.05, label="Mirostat τ (Tau)", info="The \"surprise\" value when performing mirostat sampling. 0 to disable.")
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layout["inference"]["inputs"]["mirostat-eta"] = gr.Slider(value=0.0, minimum=0.0, maximum=2.0, step=0.05, label="Mirostat η (Eta)", info="The \"learning rate\" during mirostat sampling applied to the maximum surprise.")
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"""
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layout["inference"]["buttons"]["inference"].click(
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fn=do_inference,
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inputs=[ x for x in layout["inference"]["inputs"].values() if x is not None],
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