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Running
on
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Running
on
Zero
Update app.py
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app.py
CHANGED
@@ -4,7 +4,7 @@ import gradio as gr
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import spaces
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from zonos.model import Zonos
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from zonos.conditioning import make_cond_dict
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# We'll keep a global dictionary of loaded models to avoid reloading
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MODELS_CACHE = {}
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@@ -13,15 +13,6 @@ device = "cuda"
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banner_url = "https://huggingface.co/datasets/Steveeeeeeen/random_images/resolve/main/ZonosHeader.png"
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BANNER = f'<div style="display: flex; justify-content: space-around;"><img src="{banner_url}" alt="Banner" style="width: 40vw; min-width: 150px; max-width: 300px;"> </div>'
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# Define a list of tuples: (Display Label, Language Code)
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LANGUAGES = [
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("English", "en-us"),
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("Japanese", "ja"),
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("Chinese", "cmn"),
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("French", "fr-fr"),
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("German", "de"),
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]
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def load_model(model_name: str):
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"""
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Loads or retrieves a cached Zonos model, sets it to eval and bfloat16.
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@@ -37,20 +28,15 @@ def load_model(model_name: str):
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return MODELS_CACHE[model_name]
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@spaces.GPU(duration=90)
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def tts(text, speaker_audio,
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"""
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text: str (Text prompt to synthesize)
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speaker_audio: (sample_rate, numpy_array) from Gradio if type="numpy"
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model_choice: str (which Zonos model to use, e.g., "Zyphra/Zonos-v0.1-hybrid")
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Returns (sr_out, wav_out_numpy).
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"""
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# Map from label -> actual language code
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label_to_code = dict(LANGUAGES)
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# Convert the human-readable label back to the code
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selected_language = label_to_code[selected_language_label]
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model = load_model(model_choice)
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if not text:
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@@ -66,11 +52,12 @@ def tts(text, speaker_audio, selected_language_label, model_choice):
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# Convert to Torch tensor
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wav_tensor = torch.from_numpy(wav_np).float()
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# If stereo or multi-channel, downmix to mono
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if wav_tensor.ndim == 2 and wav_tensor.shape[0] > 1:
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wav_tensor = wav_tensor.mean(dim=0) # => (samples,)
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#
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wav_tensor = wav_tensor.unsqueeze(0)
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# Get speaker embedding
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@@ -79,12 +66,12 @@ def tts(text, speaker_audio, selected_language_label, model_choice):
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spk_embedding = spk_embedding.to(device, dtype=torch.bfloat16)
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# Prepare conditioning dictionary
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cond_dict =
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conditioning = model.prepare_conditioning(cond_dict)
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# Generate codes
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@@ -119,6 +106,8 @@ def build_demo():
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ref_audio_input = gr.Audio(
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label="Reference Audio (Speaker Cloning)",
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type="numpy"
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)
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model_dropdown = gr.Dropdown(
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@@ -127,12 +116,10 @@ def build_demo():
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value="Zyphra/Zonos-v0.1-hybrid",
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interactive=True,
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)
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# For the language dropdown, we display only the friendly label
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language_dropdown = gr.Dropdown(
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label="Language",
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choices=[
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value="
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interactive=True,
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)
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@@ -150,3 +137,5 @@ def build_demo():
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if __name__ == "__main__":
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demo_app = build_demo()
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demo_app.launch(server_name="0.0.0.0", server_port=7860, share=True)
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import spaces
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from zonos.model import Zonos
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from zonos.conditioning import make_cond_dict, supported_language_codes
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# We'll keep a global dictionary of loaded models to avoid reloading
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MODELS_CACHE = {}
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banner_url = "https://huggingface.co/datasets/Steveeeeeeen/random_images/resolve/main/ZonosHeader.png"
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BANNER = f'<div style="display: flex; justify-content: space-around;"><img src="{banner_url}" alt="Banner" style="width: 40vw; min-width: 150px; max-width: 300px;"> </div>'
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def load_model(model_name: str):
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"""
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Loads or retrieves a cached Zonos model, sets it to eval and bfloat16.
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return MODELS_CACHE[model_name]
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@spaces.GPU(duration=90)
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def tts(text, speaker_audio, selected_language, model_choice):
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"""
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text: str (Text prompt to synthesize)
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speaker_audio: (sample_rate, numpy_array) from Gradio if type="numpy"
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selected_language: str (language code)
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model_choice: str (which Zonos model to use, e.g., "Zyphra/Zonos-v0.1-hybrid")
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Returns (sr_out, wav_out_numpy).
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"""
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model = load_model(model_choice)
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if not text:
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# Convert to Torch tensor
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wav_tensor = torch.from_numpy(wav_np).float()
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# If stereo (shape [channels, samples]) or multi-channel, downmix to mono
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# e.g. shape (2, samples) -> shape (samples,) by averaging
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if wav_tensor.ndim == 2 and wav_tensor.shape[0] > 1:
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wav_tensor = wav_tensor.mean(dim=0) # shape => (samples,)
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# Now add a batch dimension => shape (1, samples)
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wav_tensor = wav_tensor.unsqueeze(0)
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# Get speaker embedding
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spk_embedding = spk_embedding.to(device, dtype=torch.bfloat16)
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# Prepare conditioning dictionary
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cond_dict = make_cond_dict(
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text=text,
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speaker=spk_embedding,
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language=selected_language,
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device=device,
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)
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conditioning = model.prepare_conditioning(cond_dict)
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# Generate codes
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ref_audio_input = gr.Audio(
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label="Reference Audio (Speaker Cloning)",
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type="numpy"
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# Optionally add mono=True if you want Gradio to always downmix automatically:
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# mono=True
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)
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model_dropdown = gr.Dropdown(
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value="Zyphra/Zonos-v0.1-hybrid",
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interactive=True,
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)
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language_dropdown = gr.Dropdown(
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label="Language Code",
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choices=["en-us", "ja", "cmn", "fr-fr", "de"]
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value="en-us",
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interactive=True,
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)
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if __name__ == "__main__":
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demo_app = build_demo()
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demo_app.launch(server_name="0.0.0.0", server_port=7860, share=True)
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This is my code. replace supported_language_codes with a list of the languages i asked you and in the gr.Dropdown it display the name of the language instead of just "cmn'
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