Update app.py

app.py

@@ -5,29 +5,21 @@ import gradio as gr
 from zonos.model import Zonos
 from zonos.conditioning import make_cond_dict
 
-# Load the hybrid model
 model = Zonos.from_pretrained("Zyphra/Zonos-v0.1-hybrid", device="cuda")
-model.bfloat16()  # Switch model weights to bfloat16 precision (optional, but recommended for GPU)
+model.bfloat16()
 
-# Main inference function for Gradio
 def tts(text, reference_audio):
-    """
-    text: str
-    reference_audio: (numpy.ndarray, int) -> (data, sample_rate)
-    """
     if reference_audio is None:
-        return "No reference audio provided."
+        return None
     
-    # reference_audio[0] is a NumPy float32 array of shape (num_samples, 1) or (num_samples,)
-    # reference_audio[1] is the sample rate
-    wav_np, sr = reference_audio
+    # Gradio returns (sample_rate, audio_data) for type="numpy"
+    sr, wav_np = reference_audio
     
-    # Convert NumPy audio to Torch tensor
-    wav_torch = torch.from_numpy(wav_np).float().unsqueeze(0)  # shape: (1, num_samples)
+    # Convert NumPy audio data to Torch tensor
+    wav_torch = torch.from_numpy(wav_np).float().unsqueeze(0)
     if wav_torch.dim() == 2 and wav_torch.shape[0] > wav_torch.shape[1]:
-        # If the shape is (samples, 1), reorder to (1, samples)
         wav_torch = wav_torch.T
-    
+
     # Create speaker embedding
     spk_embedding = model.embed_spk_audio(wav_torch, sr)
     
@@ -39,35 +31,26 @@ def tts(text, reference_audio):
     )
     conditioning = model.prepare_conditioning(cond_dict)
     
-    # Generate codes
+    # Generate codes & decode
     with torch.no_grad():
-        torch.manual_seed(421)  # Seeding for reproducible results
+        torch.manual_seed(421)
         codes = model.generate(conditioning)
     
-    # Decode the codes into waveform
     wavs = model.autoencoder.decode(codes).cpu()
-    out_audio = wavs[0].numpy()  # shape: (num_samples,)
-
-    # Return as (sample_rate, audio_ndarray) for Gradio's "audio" output
+    out_audio = wavs[0].numpy()
+    
+    # Return a tuple of (sample_rate, audio_data) for playback
     return (model.autoencoder.sampling_rate, out_audio)
 
-
-# Define the Gradio interface
-# - text input for the prompt
-# - audio input for the speaker reference
-# - audio output with the generated speech
 demo = gr.Interface(
     fn=tts,
     inputs=[
         gr.Textbox(label="Text to Synthesize"),
-        gr.Audio(label="Reference Audio (for speaker embedding)"),
+        gr.Audio(type="numpy", label="Reference Audio (Speaker)"),
     ],
     outputs=gr.Audio(label="Generated Audio"),
     title="Zonos TTS Demo (Hybrid)",
-    description=(
-        "Provide a reference audio snippet for speaker embedding, "
-        "enter text, and generate speech with Zonos TTS."
-    ),
+    description="Upload a reference audio for speaker embedding, enter text, and generate speech!"
 )
 
 if __name__ == "__main__":