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Running
on
Zero
| 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" | |
| CURRENT_MODEL_TYPE = None | |
| CURRENT_MODEL = None | |
| def load_model_if_needed(model_choice: str): | |
| global CURRENT_MODEL_TYPE, CURRENT_MODEL | |
| if CURRENT_MODEL_TYPE != model_choice: | |
| if CURRENT_MODEL is not None: | |
| del CURRENT_MODEL | |
| torch.cuda.empty_cache() | |
| print(f"Loading {model_choice} model...") | |
| CURRENT_MODEL = Zonos.from_pretrained(model_choice, device=device) | |
| CURRENT_MODEL.requires_grad_(False).eval() | |
| CURRENT_MODEL_TYPE = model_choice | |
| print(f"{model_choice} model loaded successfully!") | |
| return CURRENT_MODEL | |
| 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 = load_model_if_needed(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, | |
| ) | |
| 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 = load_model_if_needed(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() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| model_choice = gr.Dropdown( | |
| choices=["Zyphra/Zonos-v0.1-transformer", "Zyphra/Zonos-v0.1-hybrid"], | |
| 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) |