t :# 请为您的变更输入提交说明。以 '#' 开始的行将被忽略，而一个空的提交

demo/stream_whisper.py

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+import tempfile
+
+import gradio as gr
+import numpy as np
+from gradio_webrtc import AdditionalOutputs, ReplyOnPause, WebRTC
+from openai import OpenAI
+from pydub import AudioSegment
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+client = OpenAI()
+
+
+def transcribe(audio: tuple[int, np.ndarray], transcript: list[dict]):
+    print("audio", audio)
+    segment = AudioSegment(
+        audio[1].tobytes(),
+        frame_rate=audio[0],
+        sample_width=audio[1].dtype.itemsize,
+        channels=1,
+    )
+
+    transcript.append({"role": "user", "content": gr.Audio((audio[0], audio[1].squeeze()))})
+
+    with tempfile.NamedTemporaryFile(suffix=".mp3") as temp_audio:
+        segment.export(temp_audio.name, format="mp3")
+        next_chunk = client.audio.transcriptions.create(
+            model="whisper-1", file=open(temp_audio.name, "rb")
+        ).text
+        transcript.append({"role": "assistant", "content": next_chunk})
+        yield AdditionalOutputs(transcript)
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            audio = WebRTC(
+                label="Stream",
+                mode="send",
+                modality="audio",
+            )
+        with gr.Column():
+            transcript = gr.Chatbot(label="transcript", type="messages")
+
+    audio.stream(ReplyOnPause(transcribe), inputs=[audio, transcript], outputs=[audio],
+                 time_limit=30)
+    audio.on_additional_outputs(lambda s: s, outputs=transcript)
+
+if __name__ == "__main__":
+    demo.launch()