Delete app-backup.py

app-backup.py

@@ -1,370 +0,0 @@
-import os
-import shlex
-import subprocess
-
-subprocess.run(shlex.split("pip install flash-attn  --no-build-isolation"), env=os.environ | {"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"}, check=True)
-subprocess.run(shlex.split("pip install https://github.com/state-spaces/mamba/releases/download/v2.2.4/mamba_ssm-2.2.4+cu12torch2.4cxx11abiFALSE-cp310-cp310-linux_x86_64.whl"), check=True)
-subprocess.run(shlex.split("pip install https://github.com/Dao-AILab/causal-conv1d/releases/download/v1.5.0.post8/causal_conv1d-1.5.0.post8+cu12torch2.4cxx11abiFALSE-cp310-cp310-linux_x86_64.whl"), check=True)
-
-import spaces
-import torch
-import torchaudio
-import gradio as gr
-from os import getenv
-
-from zonos.model import Zonos
-from zonos.conditioning import make_cond_dict, supported_language_codes
-
-device = "cuda"
-MODEL_NAMES = ["Zyphra/Zonos-v0.1-transformer", "Zyphra/Zonos-v0.1-hybrid"]
-MODELS = {name: Zonos.from_pretrained(name, device=device) for name in MODEL_NAMES}
-for model in MODELS.values():
-    model.requires_grad_(False).eval()
-
-
-def update_ui(model_choice):
-    """
-    Dynamically show/hide UI elements based on the model's conditioners.
-    We do NOT display 'language_id' or 'ctc_loss' even if they exist in the model.
-    """
-    model = MODELS[model_choice]
-    cond_names = [c.name for c in model.prefix_conditioner.conditioners]
-    print("Conditioners in this model:", cond_names)
-
-    text_update = gr.update(visible=("espeak" in cond_names))
-    language_update = gr.update(visible=("espeak" in cond_names))
-    speaker_audio_update = gr.update(visible=("speaker" in cond_names))
-    prefix_audio_update = gr.update(visible=True)
-    emotion1_update = gr.update(visible=("emotion" in cond_names))
-    emotion2_update = gr.update(visible=("emotion" in cond_names))
-    emotion3_update = gr.update(visible=("emotion" in cond_names))
-    emotion4_update = gr.update(visible=("emotion" in cond_names))
-    emotion5_update = gr.update(visible=("emotion" in cond_names))
-    emotion6_update = gr.update(visible=("emotion" in cond_names))
-    emotion7_update = gr.update(visible=("emotion" in cond_names))
-    emotion8_update = gr.update(visible=("emotion" in cond_names))
-    vq_single_slider_update = gr.update(visible=("vqscore_8" in cond_names))
-    fmax_slider_update = gr.update(visible=("fmax" in cond_names))
-    pitch_std_slider_update = gr.update(visible=("pitch_std" in cond_names))
-    speaking_rate_slider_update = gr.update(visible=("speaking_rate" in cond_names))
-    dnsmos_slider_update = gr.update(visible=("dnsmos_ovrl" in cond_names))
-    speaker_noised_checkbox_update = gr.update(visible=("speaker_noised" in cond_names))
-    unconditional_keys_update = gr.update(
-        choices=[name for name in cond_names if name not in ("espeak", "language_id")]
-    )
-
-    return (
-        text_update,
-        language_update,
-        speaker_audio_update,
-        prefix_audio_update,
-        emotion1_update,
-        emotion2_update,
-        emotion3_update,
-        emotion4_update,
-        emotion5_update,
-        emotion6_update,
-        emotion7_update,
-        emotion8_update,
-        vq_single_slider_update,
-        fmax_slider_update,
-        pitch_std_slider_update,
-        speaking_rate_slider_update,
-        dnsmos_slider_update,
-        speaker_noised_checkbox_update,
-        unconditional_keys_update,
-    )
-
-
-@spaces.GPU(duration=120)
-def generate_audio(
-    model_choice,
-    text,
-    language,
-    speaker_audio,
-    prefix_audio,
-    e1,
-    e2,
-    e3,
-    e4,
-    e5,
-    e6,
-    e7,
-    e8,
-    vq_single,
-    fmax,
-    pitch_std,
-    speaking_rate,
-    dnsmos_ovrl,
-    speaker_noised,
-    cfg_scale,
-    min_p,
-    seed,
-    randomize_seed,
-    unconditional_keys,
-    progress=gr.Progress(),
-):
-    """
-    Generates audio based on the provided UI parameters.
-    We do NOT use language_id or ctc_loss even if the model has them.
-    """
-    selected_model = MODELS[model_choice]
-
-    speaker_noised_bool = bool(speaker_noised)
-    fmax = float(fmax)
-    pitch_std = float(pitch_std)
-    speaking_rate = float(speaking_rate)
-    dnsmos_ovrl = float(dnsmos_ovrl)
-    cfg_scale = float(cfg_scale)
-    min_p = float(min_p)
-    seed = int(seed)
-    max_new_tokens = 86 * 30
-
-    if randomize_seed:
-        seed = torch.randint(0, 2**32 - 1, (1,)).item()
-    torch.manual_seed(seed)
-
-    speaker_embedding = None
-    if speaker_audio is not None and "speaker" not in unconditional_keys:
-        wav, sr = torchaudio.load(speaker_audio)
-        speaker_embedding = selected_model.make_speaker_embedding(wav, sr)
-        speaker_embedding = speaker_embedding.to(device, dtype=torch.bfloat16)
-
-    audio_prefix_codes = None
-    if prefix_audio is not None:
-        wav_prefix, sr_prefix = torchaudio.load(prefix_audio)
-        wav_prefix = wav_prefix.mean(0, keepdim=True)
-        wav_prefix = torchaudio.functional.resample(wav_prefix, sr_prefix, selected_model.autoencoder.sampling_rate)
-        wav_prefix = wav_prefix.to(device, dtype=torch.float32)
-        with torch.autocast(device, dtype=torch.float32):
-            audio_prefix_codes = selected_model.autoencoder.encode(wav_prefix.unsqueeze(0))
-
-    emotion_tensor = torch.tensor(list(map(float, [e1, e2, e3, e4, e5, e6, e7, e8])), device=device)
-
-    vq_val = float(vq_single)
-    vq_tensor = torch.tensor([vq_val] * 8, device=device).unsqueeze(0)
-
-    cond_dict = make_cond_dict(
-        text=text,
-        language=language,
-        speaker=speaker_embedding,
-        emotion=emotion_tensor,
-        vqscore_8=vq_tensor,
-        fmax=fmax,
-        pitch_std=pitch_std,
-        speaking_rate=speaking_rate,
-        dnsmos_ovrl=dnsmos_ovrl,
-        speaker_noised=speaker_noised_bool,
-        device=device,
-        unconditional_keys=unconditional_keys,
-    )
-    conditioning = selected_model.prepare_conditioning(cond_dict)
-
-    estimated_generation_duration = 30 * len(text) / 400
-    estimated_total_steps = int(estimated_generation_duration * 86)
-
-    def update_progress(_frame: torch.Tensor, step: int, _total_steps: int) -> bool:
-        progress((step, estimated_total_steps))
-        return True
-
-    codes = selected_model.generate(
-        prefix_conditioning=conditioning,
-        audio_prefix_codes=audio_prefix_codes,
-        max_new_tokens=max_new_tokens,
-        cfg_scale=cfg_scale,
-        batch_size=1,
-        sampling_params=dict(min_p=min_p),
-        callback=update_progress,
-    )
-
-    wav_out = selected_model.autoencoder.decode(codes).cpu().detach()
-    sr_out = selected_model.autoencoder.sampling_rate
-    if wav_out.dim() == 2 and wav_out.size(0) > 1:
-        wav_out = wav_out[0:1, :]
-    return (sr_out, wav_out.squeeze().numpy()), seed
-
-
-def build_interface():
-    with gr.Blocks(theme='ParityError/Interstellar') as demo:
-        with gr.Row():
-            with gr.Column():
-                model_choice = gr.Dropdown(
-                    choices=MODEL_NAMES,
-                    value="Zyphra/Zonos-v0.1-transformer",
-                    label="Zonos Model Type",
-                    info="Select the model variant to use.",
-                )
-                text = gr.Textbox(
-                    label="Text to Synthesize",
-                    value="Zonos uses eSpeak for text to phoneme conversion!",
-                    lines=4,
-                    max_length=500,  # approximately
-                )
-                language = gr.Dropdown(
-                    choices=supported_language_codes,
-                    value="en-us",
-                    label="Language Code",
-                    info="Select a language code.",
-                )
-            prefix_audio = gr.Audio(
-                value="assets/silence_100ms.wav",
-                label="Optional Prefix Audio (continue from this audio)",
-                type="filepath",
-            )
-            with gr.Column():
-                speaker_audio = gr.Audio(
-                    label="Optional Speaker Audio (for cloning)",
-                    type="filepath",
-                )
-                speaker_noised_checkbox = gr.Checkbox(label="Denoise Speaker?", value=False)
-
-        with gr.Row():
-            with gr.Column():
-                gr.Markdown("## Conditioning Parameters")
-                dnsmos_slider = gr.Slider(1.0, 5.0, value=4.0, step=0.1, label="DNSMOS Overall")
-                fmax_slider = gr.Slider(0, 24000, value=24000, step=1, label="Fmax (Hz)")
-                vq_single_slider = gr.Slider(0.5, 0.8, 0.78, 0.01, label="VQ Score")
-                pitch_std_slider = gr.Slider(0.0, 300.0, value=45.0, step=1, label="Pitch Std")
-                speaking_rate_slider = gr.Slider(5.0, 30.0, value=15.0, step=0.5, label="Speaking Rate")
-
-            with gr.Column():
-                gr.Markdown("## Generation Parameters")
-                cfg_scale_slider = gr.Slider(1.0, 5.0, 2.0, 0.1, label="CFG Scale")
-                min_p_slider = gr.Slider(0.0, 1.0, 0.15, 0.01, label="Min P")
-                seed_number = gr.Number(label="Seed", value=420, precision=0)
-                randomize_seed_toggle = gr.Checkbox(label="Randomize Seed (before generation)", value=True)
-
-        with gr.Accordion("Advanced Parameters", open=False):
-            gr.Markdown(
-                "### Unconditional Toggles\n"
-                "Checking a box will make the model ignore the corresponding conditioning value and make it unconditional.\n"
-                'Practically this means the given conditioning feature will be unconstrained and "filled in automatically".'
-            )
-            with gr.Row():
-                unconditional_keys = gr.CheckboxGroup(
-                    [
-                        "speaker",
-                        "emotion",
-                        "vqscore_8",
-                        "fmax",
-                        "pitch_std",
-                        "speaking_rate",
-                        "dnsmos_ovrl",
-                        "speaker_noised",
-                    ],
-                    value=["emotion"],
-                    label="Unconditional Keys",
-                )
-
-            gr.Markdown(
-                "### Emotion Sliders\n"
-                "Warning: The way these sliders work is not intuitive and may require some trial and error to get the desired effect.\n"
-                "Certain configurations can cause the model to become unstable. Setting emotion to unconditional may help."
-            )
-            with gr.Row():
-                emotion1 = gr.Slider(0.0, 1.0, 1.0, 0.05, label="Happiness")
-                emotion2 = gr.Slider(0.0, 1.0, 0.05, 0.05, label="Sadness")
-                emotion3 = gr.Slider(0.0, 1.0, 0.05, 0.05, label="Disgust")
-                emotion4 = gr.Slider(0.0, 1.0, 0.05, 0.05, label="Fear")
-            with gr.Row():
-                emotion5 = gr.Slider(0.0, 1.0, 0.05, 0.05, label="Surprise")
-                emotion6 = gr.Slider(0.0, 1.0, 0.05, 0.05, label="Anger")
-                emotion7 = gr.Slider(0.0, 1.0, 0.1, 0.05, label="Other")
-                emotion8 = gr.Slider(0.0, 1.0, 0.2, 0.05, label="Neutral")
-
-        with gr.Column():
-            generate_button = gr.Button("Generate Audio")
-            output_audio = gr.Audio(label="Generated Audio", type="numpy", autoplay=True)
-
-        model_choice.change(
-            fn=update_ui,
-            inputs=[model_choice],
-            outputs=[
-                text,
-                language,
-                speaker_audio,
-                prefix_audio,
-                emotion1,
-                emotion2,
-                emotion3,
-                emotion4,
-                emotion5,
-                emotion6,
-                emotion7,
-                emotion8,
-                vq_single_slider,
-                fmax_slider,
-                pitch_std_slider,
-                speaking_rate_slider,
-                dnsmos_slider,
-                speaker_noised_checkbox,
-                unconditional_keys,
-            ],
-        )
-
-        # On page load, trigger the same UI refresh
-        demo.load(
-            fn=update_ui,
-            inputs=[model_choice],
-            outputs=[
-                text,
-                language,
-                speaker_audio,
-                prefix_audio,
-                emotion1,
-                emotion2,
-                emotion3,
-                emotion4,
-                emotion5,
-                emotion6,
-                emotion7,
-                emotion8,
-                vq_single_slider,
-                fmax_slider,
-                pitch_std_slider,
-                speaking_rate_slider,
-                dnsmos_slider,
-                speaker_noised_checkbox,
-                unconditional_keys,
-            ],
-        )
-
-        # Generate audio on button click
-        generate_button.click(
-            fn=generate_audio,
-            inputs=[
-                model_choice,
-                text,
-                language,
-                speaker_audio,
-                prefix_audio,
-                emotion1,
-                emotion2,
-                emotion3,
-                emotion4,
-                emotion5,
-                emotion6,
-                emotion7,
-                emotion8,
-                vq_single_slider,
-                fmax_slider,
-                pitch_std_slider,
-                speaking_rate_slider,
-                dnsmos_slider,
-                speaker_noised_checkbox,
-                cfg_scale_slider,
-                min_p_slider,
-                seed_number,
-                randomize_seed_toggle,
-                unconditional_keys,
-            ],
-            outputs=[output_audio, seed_number],
-        )
-
-    return demo
-
-
-if __name__ == "__main__":
-    demo = build_interface()
-    share = getenv("GRADIO_SHARE", "False").lower() in ("true", "1", "t")
-    demo.launch(server_name="0.0.0.0", server_port=7860, share=share)