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import gradio as gr |
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from huggingface_hub import snapshot_download |
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from threading import Thread |
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import os |
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import time |
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import gradio as gr |
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import base64 |
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import numpy as np |
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import requests |
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from server import serve |
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repo_id = "gpt-omni/mini-omni" |
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snapshot_download(repo_id, local_dir="./checkpoint", revision="main") |
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IP='0.0.0.0' |
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PORT=60808 |
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thread = Thread(target=serve, daemon=True) |
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thread.start() |
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API_URL = "http://0.0.0.0:60808/chat" |
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OUT_CHUNK = 4096 |
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OUT_RATE = 24000 |
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OUT_CHANNELS = 1 |
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def process_audio(audio): |
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filepath = audio |
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print(f"filepath: {filepath}") |
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if filepath is None: |
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return |
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cnt = 0 |
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with open(filepath, "rb") as f: |
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data = f.read() |
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base64_encoded = str(base64.b64encode(data), encoding="utf-8") |
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files = {"audio": base64_encoded} |
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tik = time.time() |
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with requests.post(API_URL, json=files, stream=True) as response: |
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try: |
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for chunk in response.iter_content(chunk_size=OUT_CHUNK): |
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if chunk: |
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if cnt == 0: |
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print(f"first chunk time cost: {time.time() - tik:.3f}") |
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cnt += 1 |
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audio_data = np.frombuffer(chunk, dtype=np.int16) |
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audio_data = audio_data.reshape(-1, OUT_CHANNELS) |
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yield OUT_RATE, audio_data.astype(np.int16) |
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except Exception as e: |
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print(f"error: {e}") |
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def greet(name): |
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return "Hello " + name + "!!" |
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demo = gr.Interface(fn=greet, inputs="text", outputs="text") |
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demo.launch() |
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