Create app-backup.py

app-backup.py

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+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)