Update app.py

app.py

@@ -16,7 +16,7 @@ def record_opt(msg):
     return f"{date_now()} {msg}\n"
 
 
-def speech_recognize(audio, model_name, hf_token, opt):
+def speech_recognize(audio, model_name, access_token, opt):
     opt += record_opt("Transcription starts ...")
     yield "Transcribing, please wait..", opt
     start = time.monotonic()
@@ -26,7 +26,7 @@ def speech_recognize(audio, model_name, hf_token, opt):
     try:
         url = API_URL + model_name
         print(f">>> url is {url}")
-        headers = {"Authorization": f"Bearer {hf_token}"}
+        headers = {"Authorization": f"Bearer {access_token}"}
         response = requests.request("POST", url, headers=headers, data=data)
         text = json.loads(response.content.decode("utf-8"))
         print(f">>> text is {text}")
@@ -48,7 +48,7 @@ with gr.Blocks() as demo:
 
             👉 The purpose is to practice using the Gradio Audio component and explore using the Huggingface Inference API
 
-            > 💡Tip: You need to fill in the Huggingface token to call the Huggingface Inference API
+            > 💡Tip: You need to fill in the Huggingface access token to call the Huggingface Inference API
             """
         )
     with gr.Row():
@@ -67,7 +67,7 @@ with gr.Blocks() as demo:
                 ],
                 value="openai/whisper-large-v3",
             )
-            hf_token = gr.Textbox(label="Huggingface token")
+            access_token = gr.Textbox(label="Huggingface access token")
         with gr.Column():
             output = gr.Textbox(label="Transcription results")
             operation = gr.Textbox(label="Component operation history")
@@ -91,7 +91,7 @@ with gr.Blocks() as demo:
         lambda x: x + record_opt("Finished playing"),
         inputs=operation, outputs=operation
     )
-    audio.stop_recording(speech_recognize, inputs=[audio, model_name, hf_token, operation], outputs=[output, operation])
+    audio.stop_recording(speech_recognize, inputs=[audio, model_name, access_token, operation], outputs=[output, operation])
 
 demo.queue(max_size=4, concurrency_count=4)
 demo.launch()