init

app.py

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+import gradio as gr
+import numpy as np
+import scipy.io.wavfile
+import torch
+import torch.nn.functional as F
+from whisperspeech.pipeline import Pipeline
+
+def process_audio(audio_elem):
+    
+    scipy.io.wavfile.write('test.mp3', 48000, audio_elem[1])
+
+    # print out details about ut
+    pipe = Pipeline(s2a_ref='collabora/whisperspeech:s2a-q4-base-en+pl.model')
+    # save audio_elem as a file
+    speaker = pipe.extract_spk_emb("test.mp3")
+    speaker = speaker.cpu().numpy()  # Move tensor from GPU to CPU and convert to numpy array
+    print(speaker)
+    #save it locally
+    np.savez_compressed("speaker", features=speaker)
+    return "speaker.npz"
+
+
+# Define Gradio interface
+with gr.Interface(fn=process_audio, inputs="audio", outputs="file") as iface:
+    iface.launch()

requirements.txt

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+python ==3.10
+WhisperSpeech==0.8
+torch==2.0.1
+gradio