# ASR def asr_test(model): messages = [ {"role": "system", "content": "请记录下你所听到的语音内容。"}, {"role": "human", "content": [{"type": "audio", "audio": "assets/give_me_a_brief_introduction_to_the_great_wall.wav"}]}, {"role": "assistant", "content": None} ] tokens, text, _ = model(messages, max_new_tokens=256) print(text) # S2TT（support: en,zh,ja） def s2tt_test(model): messages = [ {"role": "system", "content":"请仔细聆听这段语音，然后将其内容翻译成中文。"}, # {"role": "system", "content":"Please listen carefully to this audio and then translate its content into Chinese."}, {"role": "human", "content": [{"type": "audio", "audio": "assets/give_me_a_brief_introduction_to_the_great_wall.wav"}]}, {"role": "assistant", "content": None} ] tokens, text, _ = model(messages, max_new_tokens=256, temperature=0.1, do_sample=True) print(text) # audio caption def audio_caption_test(model): messages = [ {"role": "system", "content":"Please briefly explain the important events involved in this audio clip."}, {"role": "human", "content": [{"type": "audio", "audio": "assets/music_playing_followed_by_a_woman_speaking.wav"}]}, {"role": "assistant", "content": None} ] tokens, text, _ = model(messages, max_new_tokens=256, temperature=0.1, do_sample=True) print(text) # S2ST（support: en,zh） def s2st_test(model, token2wav): messages = [ {"role": "system", "content":"请仔细聆听这段语音，然后将其内容翻译成中文并用语音播报。"}, # {"role": "system", "content":"Please listen carefully to this audio and then translate its content into Chinese speech."}, {"role": "human", "content": [{"type": "audio", "audio": "assets/give_me_a_brief_introduction_to_the_great_wall.wav"}]}, {"role": "assistant", "content": "", "eot": False}, # Insert for speech response ] tokens, text, audio = model(messages, max_tokens=2048, temperature=0.7, do_sample=True) print(text) #print(tokens) audio = [x for x in audio if x < 6561] # remove audio padding audio = token2wav(audio, prompt_wav='assets/default_female.wav') with open('output-s2st.wav', 'wb') as f: f.write(audio) # multi turn aqta def multi_turn_aqta_test(model): history = [{"role": "system", "content": "You are a helpful assistant."}] for round_idx, inp_audio in enumerate([ "assets/multi-turn-round1-听说荡口古镇从下个月开始取消门票了，你知道这事吗。.wav", "assets/multi-turn-round2-新闻说九月十九号就免费开放了。好像整个古镇都升级改造了，现在变成开放式街区了。.wav" ]): print("round: ", round_idx) history.append( {"role": "human", "content": [{"type": "audio", "audio": inp_audio}]} ) history.append( {"role": "assistant", "content": None} ) tokens, text, _ = model(history, max_new_tokens=256, temperature=0.5, do_sample=True) print(text) history.pop(-1) history.append( {"role": "assistant", "content": text} ) # multi turn aqaa def multi_turn_aqaa_test(model, token2wav): history = [{"role": "system", "content": "You are a helpful assistant."}] for round_idx, inp_audio in enumerate([ "assets/multi-turn-round1-听说荡口古镇从下个月开始取消门票了，你知道这事吗。.wav", "assets/multi-turn-round2-新闻说九月十九号就免费开放了。好像整个古镇都升级改造了，现在变成开放式街区了。.wav" ]): print("round: ", round_idx) history.append( {"role": "human", "content": [{"type": "audio", "audio": inp_audio}]} ) history.append( {"role": "assistant", "content": "", "eot": False}, # Insert for speech response ) tokens, text, audio = model(history, max_new_tokens=2048, temperature=0.7, do_sample=True) print(text) audio = [x for x in audio if x < 6561] # remove audio padding audio = token2wav(audio, prompt_wav='assets/default_female.wav') with open(f'output-round-{round_idx}.wav', 'wb') as f: f.write(audio) history.pop(-1) history.append( { "role": "assistant", "content":[ {"type": "text", "text":""}, {"type":"token", "token": tokens} ] } ) # Tool call & Web search def tool_call_test(model, token2wav): history = [ {"role": "system", "content": "你的名字叫做小跃，是由阶跃星辰公司训练出来的语音大模型。\n你具备调用工具解决问题的能力，你需要根据用户的需求和上下文情景，自主选择是否调用系统提供的工具来协助用户。\n你情感细腻，观察能力强，擅长分析用户的内容，并作出善解人意的回复，说话的过程中时刻注意用户的感受，富有同理心，提供多样的情绪价值。\n今天是2025年8月28日，星期四\n请用默认女声与用户交流"}, {"role": "tool_json_schemas", "content": '[{"type": "function", "function": {"name": "search", "description": "搜索工具", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "搜索关键词"}}, "required": ["query"], "additionalProperties": false}}}]'}, {"role": "human", "content": [{"type": "audio", "audio": "assets/帮我查一下今天上证指数的开盘价是多少.wav"}]}, {"role": "assistant", "content": "", "eot": False}, # Insert for speech response ] tokens, text, audio = model(history, max_new_tokens=4096, repetition_penalty=1.05, top_p=0.9, temperature=0.7, do_sample=True) print(text) audio = [x for x in audio if x < 6561] # remove audio padding audio = token2wav(audio, prompt_wav='assets/default_female.wav') with open('output-tool-call-1.wav', 'wb') as f: f.write(audio) history.pop(-1) with open('assets/search_result.txt') as f: search_result = f.read().strip() history += [ {"role": "assistant", "content": [{"type": "text", "text": ""}, {"type": "token", "token": tokens}]}, {"role": "input", "content": [{"type": "text", "text": search_result}, {"type": "text", "text": '\n\n\n请用口语化形式总结检索结果，简短地回答用户的问题。'}]}, {"role": "assistant", "content": "", "eot": False}, # Insert for speech response ] tokens, text, audio = model(history, max_new_tokens=4096, repetition_penalty=1.05, top_p=0.9, temperature=0.7, do_sample=True) print(text) audio = [x for x in audio if x < 6561] # remove audio padding audio = token2wav(audio, prompt_wav='assets/default_female.wav') with open('output-tool-call-2.wav', 'wb') as f: f.write(audio) # Paralingustic information understanding def paralinguistic_test(model, token2wav): messages = [ {"role": "system", "content":"请用语音与我交流。"}, {"role": "human", "content": [{"type": "audio", "audio": "assets/paralinguistic_information_understanding.wav"}]}, {"role": "assistant", "content": "", "eot": False}, # Insert for speech response ] tokens, text, audio = model(messages, max_tokens=2048, temperature=0.7, do_sample=True) print(text) #print(tokens) audio = [x for x in audio if x < 6561] # remove audio padding audio = token2wav(audio, prompt_wav='assets/default_female.wav') with open('output-paralinguistic.wav', 'wb') as f: f.write(audio) # Audio understanding def mmau_test(model): messages = [ {"role": "system", "content": "You are an expert in audio analysis, please analyze the audio content and answer the questions accurately."}, {"role": "human", "content": [{"type": "audio", "audio": "assets/mmau_test.wav"}, {"type": "text", "text": f"Which of the following best describes the male vocal in the audio? Please choose the answer from the following options: [Soft and melodic, Aggressive and talking, High-pitched and singing, Whispering] Output the final answer in ."}]}, {"role": "assistant", "content": None} ] tokens, text, _ = model(messages, max_new_tokens=256, num_beams=2) print(text) # Universal audio caption def uac_test(model): messages = [ {"role": "system", "content": "你是一位经验丰富的音频分析专家，擅长对各种语音音频进行深入细致的分析。你的任务不仅仅是将音频内容准确转写为文字，还要对说话人的声音特征（如性别、年龄、情绪状态）、背景声音、环境信息以及可能涉及的事件进行全面描述。请以专业、客观的视角，详细、准确地完成每一次分析和转写。"}, {"role": "human", "content": [{"type": "audio", "audio": "assets/music_playing_followed_by_a_woman_speaking.wav"}]}, {"role": "assistant", "content": None} ] _, text, _ = model(messages, max_new_tokens=1024, temperature=0.5, top_p=0.9, do_sample=True) print(text) if __name__ == '__main__': from stepaudio2 import StepAudio2 from token2wav import Token2wav model = StepAudio2('Step-Audio-2-mini') token2wav = Token2wav('Step-Audio-2-mini/token2wav') asr_test(model) s2tt_test(model) audio_caption_test(model) s2st_test(model, token2wav) multi_turn_aqta_test(model) multi_turn_aqaa_test(model, token2wav) tool_call_test(model, token2wav) paralinguistic_test(model, token2wav) mmau_test(model) uac_test(model)